Physiological and genetic characterization of rice nitrogen fixer PGPR isolated from rhizosphere soils of different crops

Aims

We aimed to identify plant growth-promoting rhizobacteria that could be used to develop a biofertilizer for rice.

Methods

To obtain plant growth-promoting rhizobacteria, rhizosphere soils from different crops (rice, wheat, oats, crabgrass, maize, ryegrass, and sweet potato) were inoculated to rice plants. In total, 166 different bacteria were isolated and their plant growth-promoting traits were evaluated in terms of colony morphology, indole-3-acetic acid production, acetylene reduction activity, and phosphate solubilization activity. Moreover, genetic analysis was carried out to evaluate their phylogenetic relationships based on 16S rRNA sequence data.

Results

Strains of Bacillus altitudinis, Pseudomonas monteilii, and Pseudomonas mandelii formed associations with rice plants and fixed nitrogen. A strain of Rhizobium daejeonense showed nitrogen fixation activity in an in vitro assay and in vivo. Strains of B. altitudinis and R. daejeonense derived from rice rhizosphere soil, strains of P. monteilii and Enterobacter cloacae derived from wheat rhizosphere soil, and a strain of Bacillus pumilus derived from maize rhizosphere soil significantly promoted rice plant growth.

Conclusions

These methods are effective to identify candidate species that could be developed as biofertilizers for target crops.     

Quantitative Trait Locus Mapping and Candidate Gene Analysis for Plant Architecture Traits Using Whole Genome Re-Sequencing in Rice

Plant breeders have focused on improving plant architecture as an effective means to increase crop yield. Here, we identify the main-effect quantitative trait loci (QTLs) for plant shape-related traits in rice (Oryza sativa) and find candidate genes by applying whole genome re-sequencing of two parental cultivars using next-generation sequencing. To identify QTLs influencing plant shape, we analyzed six traits: plant height, tiller number, panicle diameter, panicle length, flag leaf length, and flag leaf width. We performed QTL analysis with 178 F₇ recombinant in-bred lines (RILs) from a cross of japonica rice line ‘SNUSG1’ and indica rice line ‘Milyang23’. Using 131 molecular markers, including 28 insertion/deletion markers, we identified 11 main- and 16 minor-effect QTLs for the six traits with a threshold LOD value > 2.8. Our sequence analysis identified fifty-four candidate genes for the main-effect QTLs. By further comparison of coding sequences and meta-expression profiles between japonica and indica rice varieties, we finally chose 15 strong candidate genes for the 11 main-effect QTLs. Our study shows that the whole-genome sequence data substantially enhanced the efficiency of polymorphic marker development for QTL fine-mapping and the identification of possible candidate genes. This yields useful genetic resources for breeding high-yielding rice cultivars with improved plant architecture.     

Transmission of important chromosomal regions under selection revealed in rice pedigree breeding programs

Genetic analysis across a whole plant genome based on pedigree information offers considerable potential for enhancing genetic gain from plant breeding programs through quantitative trait loci (QTL) mapping and marker-assisted selection. Here, we report its application for graphically genotyping varieties used in Chinese japonica rice (Oryza sativa L.) pedigree breeding programs. We identified 34 important chromosomal regions from the founder parent that are under selection in the breeding programs, and by comparing donor genomic regions that are under selection with QTL locations of agronomic traits, we found that QTL clustered in important genomic regions, in accordance with association analyses of natural populations and other previous studies. The convergence of genomic regions under selection with QTL locations suggests that donor genomic regions harboring key genes/QTL for important agronomic traits have been selected by plant breeders since the 1950s from the founder rice plants. The results provide better understanding of the effects of selection in breeding programs on the traits of rice cultivars. They also provide potentially valuable information for enhancing rice breeding programs through screening candidate parents for targeted molecular markers, improving crop yield potential and identifying suitable genetic material for use in future breeding programs.     

Genomewide discovery of DNA polymorphisms in rice cultivars with contrasting drought and salinity stress response and their functional relevance

Next‐generation sequencing technologies provide opportunities to understand the genetic basis of phenotypic differences, such as abiotic stress response, even in the closely related cultivars via identification of large number of DNA polymorphisms. We performed whole‐genome resequencing of three rice cultivars with contrasting responses to drought and salinity stress (sensitive IR64, drought‐tolerant Nagina 22 and salinity‐tolerant Pokkali). More than 356 million 90‐bp paired‐end reads were generated, which provided about 85% coverage of the rice genome. Applying stringent parameters, we identified a total of 1 784 583 nonredundant single‐nucleotide polymorphisms (SNPs) and 154 275 InDels between reference (Nipponbare) and the three resequenced cultivars. We detected 401 683 and 662 509 SNPs between IR64 and Pokkali, and IR64 and N22 cultivars, respectively. The distribution of DNA polymorphisms was found to be uneven across and within the rice chromosomes. One‐fourth of the SNPs and InDels were detected in genic regions, and about 3.5% of the total SNPs resulted in nonsynonymous changes. Large‐effect SNPs and InDels, which affect the integrity of the encoded protein, were also identified. Further, we identified DNA polymorphisms present in the differentially expressed genes within the known quantitative trait loci. Among these, a total of 548 SNPs in 232 genes, located in the conserved functional domains, were identified. The data presented in this study provide functional markers and promising target genes for salinity and drought tolerance and present a valuable resource for high‐throughput genotyping and molecular breeding for abiotic stress traits in rice.     

Physiological and genomic evidence supports the role of Serratia quinivorans PKL:12 as a biopriming agent for the biohardening of micropropagated Picrorhiza kurroa plantlets in cold regions

The medicinal herb, Picrorhiza kurroa Royle ex Benth has become endangered because of indiscriminate over-harvesting. Although micropropagation has been attempted for mass propagation of the plant, survival of in vitro plantlets under green house/open field poses a major challenge. Biopriming of micropropagated plantlets with plant growth-promoting rhizobacteria (PGPR) are among the successful methods to combat this problem. Serratia quinivorans PKL:12 was the best-characterized PGPR from rhizospheric soil of P. kurroa as it increased the vegetative growth and survival of the micropropagated plantlets most effectively. Complete genome (5.29 Mb) predicted genes encoding proteins for cold adaptation and plant growth-promoting traits in PKL:12. Antibiotic and biosynthetic gene cluster prediction supported PKL:12 as a potential biocontrol agent. Comparative genomics revealed 226 unique genes with few genes associated with plant growth-promoting potential. Physiological and genomic evidence supports S. quinivorans PKL:12 as a potential agent for bio-hardening of micropropagated P. kurroa plantlets in cold regions.     

A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa)

Rice (Oryza sativa) is one of the most important staple foods for more than half of the global population. Many rice traits are quantitative, complex and controlled by multiple interacting genes. Thus, a full understanding of genetic relationships will be critical to systematically identify genes controlling agronomic traits. We developed a genome‐wide rice protein–protein interaction network (RicePPINet, http://netbio.sjtu.edu.cn/riceppinet ) using machine learning with structural relationship and functional information. RicePPINet contained 708 819 predicted interactions for 16 895 non‐transposable element related proteins. The power of the network for discovering novel protein interactions was demonstrated through comparison with other publicly available protein–protein interaction (PPI) prediction methods, and by experimentally determined PPI data sets. Furthermore, global analysis of domain‐mediated interactions revealed RicePPINet accurately reflects PPIs at the domain level. Our studies showed the efficiency of the RicePPINet‐based method in prioritizing candidate genes involved in complex agronomic traits, such as disease resistance and drought tolerance, was approximately 2–11 times better than random prediction. RicePPINet provides an expanded landscape of computational interactome for the genetic dissection of agronomically important traits in rice.     

Genomic evidence of genetic variation with pleiotropic effects on caterpillar fitness and plant traits in a model legume

Plant–insect interactions are ubiquitous, and have been studied intensely because of their relevance to damage and pollination in agricultural plants, and to the ecology and evolution of biodiversity. Variation within species can affect the outcome of these interactions. Specific genes and chemicals that mediate these interactions have been identified, but genome‐ or metabolome‐scale studies might be necessary to better understand the ecological and evolutionary consequences of intraspecific variation for plant–insect interactions. Here, we present such a study. Specifically, we assess the consequences of genome‐wide genetic variation in the model plant Medicago truncatula for Lycaeides melissa caterpillar growth and survival (larval performance). Using a rearing experiment and a whole‐genome SNP data set (>5 million SNPs), we found that polygenic variation in M. truncatula explains 9%–41% of the observed variation in caterpillar growth and survival. Genetic correlations among caterpillar performance and other plant traits, including structural defences and some anonymous chemical features, suggest that multiple M. truncatula alleles have pleiotropic effects on plant traits and caterpillar performance (or that substantial linkage disequilibrium exists among distinct loci affecting subsets of these traits). A moderate proportion of the genetic effect of M. truncatula alleles on L. melissa performance can be explained by the effect of these alleles on the plant traits we measured, especially leaf toughness. Taken together, our results show that intraspecific genetic variation in M. truncatula has a substantial effect on the successful development of L. melissa caterpillars (i.e., on a plant–insect interaction), and further point toward traits potentially mediating this genetic effect.     

Can biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought

The possibility of introducing metabolic/biochemical phenotyping to complement genomics-based predictions in breeding pipelines has been considered for years. Here we examine to what extent and under what environmental conditions metabolic/biochemical traits can effectively contribute to understanding and predicting plant performance. In this study, multivariable statistical models based on flag leaf central metabolism and oxidative stress status were used to predict grain yield (GY) performance for 271 indica rice (Oryza sativa) accessions grown in the field under well-watered and reproductive stage drought conditions. The resulting models displayed significantly higher predictability than multivariable models based on genomic data for the prediction of GY under drought (Q² = 0.54–0.56 versus 0.35) and for stress-induced GY loss (Q² = 0.59–0.64 versus 0.03–0.06). Models based on the combined datasets showed predictabilities similar to metabolic/biochemical-based models alone. In contrast to genetic markers, models with enzyme activities and metabolite values also quantitatively integrated the effect of physiological differences such as plant height on GY. The models highlighted antioxidant enzymes of the ascorbate–glutathione cycle and a lipid oxidation stress marker as important predictors of rice GY stability under drought at the reproductive stage, and these stress-related variables were more predictive than leaf central metabolites. These findings provide evidence that metabolic/biochemical traits can integrate dynamic cellular and physiological responses to the environment and can help bridge the gap between the genome and the phenome of crops as predictors of GY performance under drought.

Biochemical traits outperform the explanatory power of genetic markers when used as variables in models for predicting yield performance in rice under drought stress.     

Gene identification using rice genome sequences

Identifying useful gene(s) is one of the most important objectives of plant geneticists. Various strategies can be used, which are based on the characteristics of plant reproduction and available technology. Rice is the first model crop whose whole genome sequence has been reported. In addition, information on the whole genome sequences of two important rice subspecies (japonica and indica rice) is also available. Rice is a self-pollinating crop and methods of artificial crossing are relatively easy to perform; such methods enable the production of numerous seeds for genetic analyses. Based on these features, a map-based cloning (i.e., positional cloning) strategy has been successfully applied over the last decade to identify rice genes. Recently, advanced next-generation sequencing (NGS) technology was used to ascertain the genome sequences of individual plants, opening up a new strategy for gene identification. This strategy has been used successfully to identify the genes responsible for certain qualitative traits in rice. However, to identify the gene(s) involved in a quantitative trait, a map-based cloning strategy is still required after quantitative trait loci analysis using NGS technology. In this review, we discuss both map-based cloning (which is still the primary strategy used to identify rice genes) and NGS-based strategies.     

Combined effect of salicylic acid and potassium mitigates drought stress through the modulation of physio-biochemical attributes and key antioxidants in wheat

Improving physio-biochemical traits in wheat under drought stress conditions has received more research attention in recent years for better adaptability and higher yield. In this study, we explored the potential bio-physiological mechanisms underlying improved plant growth and water use efficiency in wheat following soil application of potassium (0 and 100 kg ha^?1) and seed primed salicylic acid (SA) (150 mg per L) and SA foliar application (100 mg per L) under drought stresses (100%, 60% and 30% FC). Two years' average data revealed that inducing drought stress resulted in a decrease in plant pigments content, growth traits, and plant water status however, the influence was substantially reduced with the combined application of K and SA under drought stress conditions. The SA foliar spray in combination with K had increased chlorophyll a (174% and 83%), chl b (130% and 192%), chl a + b (156% and 120), carotenoid (22% and 11%), proline contents (24% and 29%) leaf relative water content (24% and 29%) while reduced leaf WSD (17% and 20%), WRC (6% and 7%), and WUC (23% and 28%) under mild and severe drought stresses, respectively. The increase in grain yield by 41% and 37% with enhanced water use efficiency was obtained with combined foliar SA and K under mild and severe drought stress, respectively indicating its vital role in overcoming the deleterious effects of drought via regulation of osmotic and metabolic processes and stabilizes cell components. RDA analysis revealed that the studied traits were completely discriminated under severe stress than mild or no drought stress. A positive and significant association was found between plant pigments with seed yield whereas a negative and significant correlation existed between water leaf traits and plant pigments. It was concluded that both foliar SA and seed primed SA with K fertilization combat the adverse effects of drought and improved plant water status as well as growth and bio-physiological traits of wheat under drought stress conditions.     

Genomewide survey and characterization of metacaspase gene family in rice (Oryza sativa)

Metacaspases (MCs), which are cysteine-dependent proteases found in plants, fungi, and protozoa, may be involved in programmed cell death processes, being distant relatives of metazoan caspases. In this study, we analysed the structures, phylogenetic relationship, genome localizations, expression patterns and domestic selections of eight MC genes identified in rice (OsMC). Alignment analysis of the corresponding protein sequences suggested OsMC proteins can be classified into two subtypes. The expression profiles of eight OsMC genes were analysed in 27 tissues covering the whole life cycle of rice. There are four OsMC genes uniquely expressed in mature tissues, indicating that these genes might play certain roles in senescence. Under abiotic and biotic stresses, four OsMC genes were expressed with treatments of one or more of Magnaporthe oryzae (M. oryzae) infected, pest damaged, cold stress and drought stress, indicating they might be involved in plant defense. In addition, gene trees and genetic diversity (π) were performed to measure whether candidate genes were selected during rice domestication. The results suggested that all the type I genes could not be domestication genes. However, two of five type II OsMC genes showed strong evidence for selective sweep, suggesting that these genes might be involved in cultivated rice domestication. These results provide a foundation for future functional genomic studies of this family in rice.