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Soil salinity imposes osmotic, ionic, and oxidative stresses on plants, resulting in growth inhibition, developmental changes, metabolic adaptations, and ion sequestration or exclusion. Identifying salinity-tolerant resources and understanding physiological and molecular mechanisms of salinity tolerance could lay a foundation for the improvement of salinity tolerance in rice. In this study, a series of salinity-tolerance-related morphological and physiological traits were investigated in 46 rice genotypes, including Sea Rice 86, to reveal the main strategies of rice in responding to salinity stress at the seedling stage. No genotypes showed the same tolerance level as the two landraces Pokkali and Nona Bokra, which remain the donors for improving the salinity tolerance of rice. However, due to undesirable agronomic traits of these donors, alternative cultivars such as JC118S and R1 are recommended as novel source of salinity tolerance. Correlation and principal component analyses revealed that the salinity tolerance of rice seedlings is not only controlled by growth vigor but also regulated by ion transport pathways such as long-distance Na+ transport, root Na+ sequestration, and root K+ retention. Therefore, such key traits should be targeted in future breeding programs as the strategy of obtaining better Na+ exclusion is still the bottleneck for improving salinity tolerance in rice. [ABSTRACT FROM AUTHOR]

To identify stable quantitative trait loci (QTLs) responsible for sheath blight resistance, a recombinant inbred line mapping population consisting of 219 lines was developed by crossing Lemont and Yangdao 4. Average disease rating, average lesion length, maximum disease rating and maximum lesion length were assayed in six different environments. A total of 128 minor effect QTLs were detected by multiple interval mapping. These QTLs explained less than 11.2% of the phenotypic variations individually, and 106 QTLs were clustered in 20 QTL-rich regions/putative loci. Significant QTL × environment interactions were detected at three putative loci (qSBR11.1, qSBR11.2 and qSBR11.3), indicating that these three loci were not stable. The other 17 stable loci (qSBR1.1, qSBR1.2, qSBR2.1, qSBR2.3, qSBR3.1, qSBR3.2, qSBR3.5, qSBR3.6, qSBR5.1, qSBR7.1, qSBR8.1, qSBR9.1, qSBR9.2, qSBR9.3, qSBR12.1, qSBR12.2 and qSBR12.4) provided a foundation for marker-assisted selection in breeding. Analysis of allelic effect on the 20 putative loci identified 7 highly stable loci, including qSBR3.2, qSBR7.1, qSBR8.1, qSBR9.2, qSBR9.3, qSBR12.1 and qSBR12.2. Keywords: Rice, Sheath blight resistance, Quantitative trait locus, Recombinant inbred line

Xanthomonas oryzae pv. oryzae (Xoo) is a causative agent of rice bacterial blight (BB). In 2020–2022, BB re-emerged, and there was a break out in the Yangtze River area, China. The pandemic Xoo strain, LA20, was isolated and identified from cultivar Quanyou1606 and demonstrated to be the Chinese R9 Xoo strain, which is able to override the widely adopted xa5-, Xa7- and xa13-mediated resistance in rice varieties in Yangtze River. Here, we report the complete genome of LA20 by PacBio and Illumina sequencing. The assembled genome consists of one circular chromosome of 4,960,087 bp, sharing 99.65% sequence identity with the traditional representative strain, YC11 (R5), in the Yangtze River. Comparative genome analysis of LA20 and YC11 revealed the obvious variability in Tal genes (the uppermost virulence determinants) in numbers and sequences. Particularly, six Tal genes were only found in LA20, but not in YC11, among which Tal1b (pthXo1)/Tal4 (pthXo6), along with the lost one, pthXo3 (avrXa7), might be the major factors for LA20 to overcome xa5-, Xa7- and xa13-mediated resistance, thus, leading to the resurgence of BB. This complete genome of the new pandemic Xoo strain will provide novel insights into pathogen evolution, the traits of pathogenicity on genomic level and the epidemic disease status in China. [ABSTRACT FROM AUTHOR]

Grain weight, which is controlled by quantitative trait loci (QTLs), is one of the most important determinants of rice yield. Although many QTLs for grain weight have been identified, little is known about how different alleles in different QTLs coordinate to determine grain weight. In the present study, six grain-weight-QTLs were detected in seven mapping populations (two F2, one F3, and four recombinant inbred lines) developed by crossing ‘Lemont’, a United States japonica variety, with ‘Yangdao 4’, a Chinese indica variety. In each of the six loci, one allele from one parent increased grain weight and one allele from another parent decreased it. Thus, the 12 alleles at the six QTLs were subjected to regression analysis to examine whether they acted additively across loci leading to a linear relationship between the predicted breeding value of QTL and phenotype. Results suggested that a combination of the 12 alleles determined grain weight. In addition, plants carrying more grain-weight-increasing alleles had heavier grains than those carrying more grain-weight-decreasing alleles. This trend was consistent in the seven mapping populations. Thus, these six QTLs might be used in marker-assisted selection of grain weight, by stacking different grain-weight-increasing or -decreasing alleles. [ABSTRACT FROM AUTHOR]

Grain length is an important quantitative trait in rice (Oryza sativa L.) that influences both grain yield and exterior quality. Although many quantitative trait loci (QTLs) for grain length have been identified, it is still unclear how different alleles from different QTLs regulate grain length coordinately. To explore the mechanisms of QTL combination in the determination of grain length, five mapping populations, including two F2 populations, an F3 population, an F7 recombinant inbred line (RIL) population, and an F8 RIL population, were developed from the cross between the U.S. tropical japonica variety ‘Lemont’ and the Chinese indica variety ‘Yangdao 4’ and grown under different environmental conditions. Four QTLs (qGL-3-1, qGL-3-2, qGL-4, and qGL-7) for grain length were detected using both composite interval mapping and multiple interval mapping methods in the mapping populations. In each locus, there was an allele from one parent that increased grain length and another allele from another parent that decreased it. The eight alleles in the four QTLs were analyzed to determine whether these alleles act additively across loci, and lead to a linear relationship between the predicted breeding value of QTLs and phenotype. Linear regression analysis suggested that the combination of eight alleles determined grain length. Plants carrying more grain length-increasing alleles had longer grain length than those carrying more grain length-decreasing alleles. This trend was consistent in all five mapping populations and demonstrated the regulation of grain length by the four QTLs. Thus, these QTLs are ideal resources for modifying grain length in rice. [ABSTRACT FROM AUTHOR]

Rice hull, the outer cover of the rice grain, determines grain shape and size. Changes in the rice hull proteome in different growth stages may reflect the underlying mechanisms involved in grain development. To better understand these changes, isobaric tags for relative and absolute quantitative (iTRAQ) MS/MS was used to detect statistically significant changes in the rice hull proteome in the booting, flowering, and milk-ripe growth stages. Differentially expressed proteins were analyzed to predict their potential functions during development. Gene ontology (GO) terms and pathways were used to evaluate the biological mechanisms involved in rice hull at the three growth stages. In total, 5,268 proteins were detected and characterized, of which 563 were differentially expressed across the development stages. The results showed that the flowering and milk-ripe stage proteomes were more similar to each other (r=0.61) than either was to the booting stage proteome. A GO enrichment analysis of the differentially expressed proteins was used to predict their roles during rice hull development. The potential functions of 25 significantly differentially expressed proteins were used to evaluate their possible roles at various growth stages. Among these proteins, an unannotated protein (Q7X8A1) was found to be overexpressed especially in the flowering stage, while a putative uncharacterized protein (B8BF94) and an aldehyde dehydrogenase (Q9FPK6) were overexpressed only in the milk-ripe stage. Pathways regulated by differentially expressed proteins were also analyzed. Magnesium-protoporphyrin IX monomethyl ester [oxidative] cyclase (Q9SDJ2), and two magnesium-chelatase subunits, ChlD (Q6ATS0), and ChlI (Q53RM0), were associated with chlorophyll biosynthesis at different developmental stages. The expression of Q9SDJ2 in the flowering and milk-ripe stages was validated by qRT-PCR. The 25 candidate proteins may be pivotal markers for controlling rice hull development at various growth stages and chlorophyll biosynthesis pathway related proteins, especially magnesium-protoporphyrin IX monomethyl ester [oxidative] cyclase (Q9SDJ2), may provide new insights into the molecular mechanisms of rice hull development and chlorophyll associated regulation. [ABSTRACT FROM AUTHOR]

Uniformity of stem height in rice directly affects crop yield potential and appearance, and has become a vital index for rice improvement. In the present study, a doubled haploid (DH) population, derived from a cross between japonica rice Chunjiang 06 and indica rice TN1 was used to analyze the quantitative trait locus (QTL) for three related traits of panicle-layer-uniformity; that is, the tallest panicle height, the lowest panicle height and panicle layer disuniformity in two locations: Hangzhou (HZ) and Hainan (HN). A total of 16 QTLs for three traits distributed on eight chromosomes were detected in two different environments. Two QTLs, qTPH-4 and qTPH-8 were co-located with the QTLs for qLPH-4 and qLPH-8, which were only significant in the HZ environment, whereas the qTPH-6 and qLPH-6 located at the same interval were only significant in the HN environment. Two QTLs, qPLD-10-1 and qPLD-10-2, were closely linked to qTPH-10, and they might have been at the same locus. One QTL, qPLD-3, was detected in both environments, explaining more than 23% of the phenotypic variations. The CJ06 allele of qPLD-3 could increase the panicle layer disuniformity by 9.23 and 4.74 cm in the HZ and HN environments. Except for qPLD-3, almost all other QTLs for the same trait were detected only in one environment, indicating that these three traits were dramatically affected by environmental factors. The results may be useful for elucidation of the molecular mechanism of panicle-layer-uniformity and marker assisted breeding for super-rice. [ABSTRACT FROM AUTHOR]

Thousand-grain weight (TGWT) is an important factor affecting grain yield as well as grain quality in rice. A quantitative trait locus (QTL) qTGWT1-1 for TGWT was detected previously near DNA marker RG532 on the short arm of chromosome 1 in a recombinant inbred line (RIL) population derived from the indica-indica rice cross Zhengshan9lB (ZS97B)IMilyang46 (MY46). In this study, two residual heterozygous lines (RHLs), Chi and Ch2, derived from the ZS97B/MY46 RIL F7 population, were used to develop two F6 populations, RIL-1 and RIL-2. The genome of Ch1 and Ch2 contains a heterozygous region flanked by RM1 -RM3746 and RM151 -RM243 on the short arm of chromosome 1, respectively, but is homozygous in other regions. Two tightly linked OTLs, Gwl-1 and Gwl-2, with the same additive direction and similar effect on TGWT, were detected in the region of QTL qTGWT1-1 in population RIL-2. No QTL was detected in the population RIL-1. Four individual RHL5 from the population RIL-2 carrying heterozygous segments flanked by RM151 - RM10404, RM10381 - RM243, RM10435 - RM259 and RM10398-RM5359, respectively, were chosen to develop four F2 populations. Ten maternal homozygotes and 10 paternal homozygotes were selected from each of the four F2 populations derived from the four RHLs. The four sets of near isogenic lines (NILs) were grown for phenotyping of TGWT and delimitation of Gwl-1 and Gwl-2. Results showed that Gwl-1 and Gwl-2 were located in the intervals RM10376-RM10398 and RM10404-RM1344 which cover 392.9 and 308.5 kb regions, respectively. The enhancing alleles were from ZS97B at both loci, and no significant interactions were detected. Genetic dissection of Gwl-1 and Gwl-2 has laid a foundation for their cloning and molecular breeding of grain yield and quality in rice. [ABSTRACT FROM AUTHOR]

Rice sheath blight (SB) disease is a global issue that causes great yield losses each year. To explore whether SB field resistance can be predicted, 273 rice genotypes were inoculated and evaluated for SB field resistance across nine environments from 2012 to 2019 to identify loci associated with SB resistance by association mapping. A total of 80 significant marker-trait associations were detected in nine environments, among which six loci (D130B, D230A, D304B, D309, D427A, and RM409) were repeatedly detected in at least two environments. A linear regression model for predicting SB lesion length was developed using genotypic data of these six loci and SB field resistance data of the 273 rice genotypes: y = 34.44 - 0.56 x , where y is the predicted value of lesion length, and x is the total genotypic value of the six loci. A recombinant inbred line (RIL) population consisting of 219 lines that was grown in six environments (from 2013 to 2018) for evaluation of SB field resistance was used to check the prediction accuracy of the prediction model. The average absolute error between the predicted lesion length and real lesion length for the RIL population was 6.67 cm. The absolute errors between predicted and real lesion lengths were <6 cm for 51.22% of the lines and <9 cm for 71.22% of the lines. An SB visual rating prediction model was also developed, and the average absolute error between the predicted visual rating and real visual rating for the RIL population was 0.94. These results indicated that the rice SB lesion length can be predicted by the development of a linear regression model using both genotypic and phenotypic data.

In autumn 2020, leaf blight was observed on rice (Oryza sativa L., variety Zhongzao39, Yongyou9, Yongyou12, Yongyou15, Yongyou18, Yongyou1540, Zhongzheyou8, Jiafengyou2, Xiangliangyou900 and Jiyou351) in the fields of 17 towns in Zhejiang and Jiangxi Provinces, China. The disease incidence was 45%-60%. Initially, water-soaked, linear, light brown lesions emerged in the upper blades of the leaves, and then spread down to leaf margins, which ultimately caused leaf curling and blight during the booting-harvest stage (Fig. S1). The disease symptoms were assumed to be caused by Xanthomonas oryzae pv. oryzae (Xoo), the pathogen of rice bacterial blight. 63 isolates were obtained from the collected diseased leaves as previously described (Hou et al. 2020). All isolates showed circular, smooth-margined, yellow colonies when cultured on peptone sugar agar (PSA) medium for 24h at 28℃. The cells were all gram-negative and rod-shaped with three to six peritrichous flagella; positive for catalase, indole, glucose fermentation and citrate utilization, while negative for oxidase, alkaline, phenylalanine deaminase, urease, and nitrate reductase reactions. 16S rRNA gene sequence analysis from the 6 isolates (FY43, JH31, JH99, TZ20, TZ39 and TZ68) revealed that the amplified fragments shared 98% similarity with Pantoea ananatis type strain LMG 2665T (GenBank JFZU01) (Table S3). To further verify P. ananatis identity of these isolates, fragments of three housekeeping genes including gyrB, leuS and rpoB from the 6 isolates were amplified and sequenced, which showed highest homology to LMG 2665T with a sequence similarity of 95%-100% (Table S3). Primers (Brady et al. 2008) and GenBank accession numbers of gene sequences from the 6 isolates are listed in Table S1 and Table S2. Phylogenetic analysis of gyrB, leuS and rpoB concatenated sequences indicated that the 6 isolates were clustered in a stable branch with P. ananatis (Fig. S2). Based on the above morphological, physiological, biochemical and molecular data, the isolates are identified as P. ananatis. For pathogenicity tests, bacterial suspension at 108 CFU/mL was inoculated into flag leaves of rice (cv. Zhongzao39) at the late booting stage using clipping method. Water was used as a negative control. The clipped leaves displayed water-soaked lesions at 3 to 5 days after inoculation (DAI); then the lesion spread downward and turned light brown. At about 14 DAI, blight was shown with similar symptoms to those samples collected from the rice field of Zhejiang and Jiangxi provinces (Fig. S1). In contrast, the control plants remained healthy and symptomless. The same P. ananatis was re-isolated in the inoculated rice plants, fulfilling Koch's postulates. In the past decade, P. ananatis has been reported to cause grain discoloration in Hangzhou, China (Yan et al. 2010) and induce leaf blight as a companion of Enterobacter asburiae in Sichuan province, China (Xue et al. 2020). Nevertheless, to the best of our knowledge, this is the first report of P. ananatis as the causative agent of rice leaf blight in southeast China. This study raises the alarm that the emerging rice bacterial leaf blight in southeast China might be caused by a new pathogen P. ananatis, instead of Xoo as traditionally assumed. Further, the differences of occurrence, spread and control between two rice bacterial leaf blight diseases caused by P. ananatis and Xoo, respectively need to be determined in the future.

Rice sheath blight (SB) is the most serious rice disease in China. Resistance of rice to SB is a quantitative trait that is easily influenced by the environment; however, the extent of environmental influence on SB field resistance is still poorly understood. To identify rice genotype by environment interactions for SB resistance, 211 rice genotypes originating from 15 countries were planted and evaluated for SB field resistance in six different environments between 2012 and 2016 after inoculation with the SB pathogen isolate ZJ03. In addition, 65 rice genotypes were evaluated for SB field resistance in another four environments between 2013 and 2016 using ZJ03. Variations in SB field resistance were observed in different genotypes in different environments using objective and subjective rating methods. Two-way analysis of variance indicated that the interaction between the genotype and environment had a highly significant effect on SB field resistance. This analysis indicated that the environment had more of an influence than the genotype itself on SB field resistance, and the genotype by environment interaction was the greatest obstacle in obtaining a precise determination of SB field resistance in rice. The most resistant genotype, GD66, is a good candidate for genetic studies and breeding.

Background: Polyploidy has pivotal influences on rice (Oryza sativa L.) morphology and physiology, and is very important for understanding rice domestication and improving agricultural traits. Diploid (DP) and triploid (TP) rice shows differences in morphological parameters, such as plant height, leaf length, leaf width and the physiological index of chlorophyll content. However, the underlying mechanisms determining these morphological differences are remain to be defined. To better understand the proteomic changes between DP and TP, tandem mass tags (TMT) mass spectrometry (MS)/MS was used to detect the significant changes to protein expression between DP and TP., Results: Results indicated that both photosynthesis and metabolic pathways were highly significantly associated with proteomic alteration between DP and TP based on biological process and pathway enrichment analysis, and 13 higher abundance chloroplast proteins involving in these two pathways were identified in TP. Quantitative real-time PCR analysis demonstrated that 5 of the 13 chloroplast proteins ATPF, PSAA, PSAB, PSBB and RBL in TP were higher abundance compared with those in DP., Conclusions: This study integrates morphology, physiology and proteomic profiling alteration of DP and TP to address their underlying different molecular mechanisms. Our finding revealed that ATPF, PSAA, PSAB, PSBB and RBL can induce considerable expression changes in TP and may affect the development and growth of rice through photosynthesis and metabolic pathways.

Simultaneous heading of plants within the same rice variety, also refer to heading synchrony, is an important factor that affects simultaneous ripening of the variety. Understanding of the genetic basis of heading synchrony may contribute to molecular breeding of rice with simultaneous heading and ripening. In the present study, a doubled haploid (DH) population, derived from a cross between Chunjiang 06 and TN1 was used to analyze quantitative trait locus (QTL) for heading synchrony related traits, i.e., early heading date (EHD), late heading date (LHD), heading asynchrony (HAS), and tiller number (PN). A total of 19 QTLs for four traits distributed on nine chromosomes were detected in two environments. One QTL, qHAS-8 for HAS, explained 27.7% of the phenotypic variation, co-located with the QTLs for EHD and LHD, but it was only significant under long-day conditions in Hangzhou, China. The other three QTLs, qHAS-6, qHAS-9, and qHAS-10, were identified under short-day conditions in Hainan, China, each of which explained about 11% of the phenotypic variation. Two of them, qHAS-6 and qHAS-9, were co-located with the QTLs for EHD and LHD. Two QTLs, qPN-4 and qPN-5 for PN, were detected in Hangzhou, and qPN-5 was also detected in Hainan. However, none of them was co-located with QTLs for EHD, LHD, and HAS, suggesting that PN and HAS were controlled by different genetic factors. The results of this study can be useful in marker assisted breeding for improvement of heading synchrony.