Structural mechanism of heavy metal-associated integrated domain engineering of paired nucleotide-binding and leucine-rich repeat proteins in rice

Plant nucleotide-binding and leucine-rich repeat (NLR) proteins are immune sensors that detect pathogen effectors and initiate a strong immune response. In many cases, single NLR proteins are sufficient for both effector recognition and signaling activation. These proteins possess a conserved architecture, including a C-terminal leucine-rich repeat (LRR) domain, a central nucleotide-binding (NB) domain, and a variable N-terminal domain. Nevertheless, many paired NLRs linked in a head-to-head configuration have now been identified. The ones carrying integrated domains (IDs) can recognize pathogen effector proteins by various modes; these are known as sensor NLR (sNLR) proteins. Structural and biochemical studies have provided insights into the molecular basis of heavy metal-associated IDs (HMA IDs) from paired NLRs in rice and revealed the co-evolution between pathogens and hosts by combining naturally occurring favorable interactions across diverse interfaces. Focusing on structural and molecular models, here we highlight advances in structure-guided engineering to expand and enhance the response profile of paired NLR-HMA IDs in rice to variants of the rice blast pathogen MAX-effectors (Magnaporthe oryzae AVRs and ToxB-like). These results demonstrate that the HMA IDs-based design of rice materials with broad and enhanced resistance profiles possesses great application potential but also face considerable challenges

Genome-Wide Association Study for Adult-Plant Resistance to Stripe Rust in Chinese Wheat Landraces (Triticum aestivum L.) From the Yellow and Huai River Valleys

Stripe rust (also known as yellow rust), caused by the pathogen Puccinia striiformis f. sp. tritici (Pst), is a common and serious fungal disease of wheat (Triticum aestivum L.) worldwide. To identify effective stripe rust resistance loci, a genome-wide association study was performed using 152 wheat landraces from the Yellow and Huai River Valleys in China based on Diversity Arrays Technology and simple sequence repeat markers. Phenotypic evaluation of the degree of resistance to stripe rust at the adult-plant stage under field conditions was carried out in five environments. In total, 19 accessions displayed stable, high degrees of resistance to stripe rust development when exposed to mixed races of Pst at the adult-plant stage in multi-environment field assessments. A marker–trait association analysis indicated that 51 loci were significantly associated with adult-plant resistance to stripe rust. These loci included 40 quantitative trait loci (QTL) regions for adult-plant resistance. Twenty identified resistance QTL were linked closely to previously reported yellow rust resistance genes or QTL regions, which were distributed across chromosomes 1B, 1D, 2A, 2B, 3A, 3B, 4A, 4B, 5B, 6B, 7A, 7B, and 7D. Six multi-trait QTL were detected on chromosomes 1B, 1D, 2B, 3A, 3B, and 7D. Twenty QTL were mapped to chromosomes 1D, 2A, 2D, 4B, 5B, 6A, 6B, 6D, 7A, 7B, and 7D, distant from previously identified yellow rust resistance genes. Consequently, these QTL are potentially novel loci for stripe rust resistance. Among the 20 potentially novel QTL, five (QDS.sicau-2A, QIT.sicau-4B, QDS.sicau-4B.2, QDS.sicau-6A.3, and QYr.sicau-7D) were associated with field responses at the adult-plant stage in at least two environments, and may have large effects on stripe rust resistance. The novel effective QTL for adult-plant resistance to stripe rust will improve understanding of the genetic mechanisms that control the spread of stripe rust, and will aid in the molecular marker-assisted selection-based breeding of wheat for stripe rust resistance

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

This paper deals with the identification of the fractional order Hammerstein model by using proposed adaptive differential evolution with the Local search strategy (ADELS) algorithm with the steepest descent method and the overparameterization based auxiliary model recursive least squares (OAMRLS) algorithm. The parameters of the static nonlinear block and the dynamic linear block of the model are all unknown, including the fractional order. The initial value of the parameter is obtained by the proposed ADELS algorithm. The main innovation of ADELS is to adaptively generate the next generation based on the fitness function value within the population through scoring rules and introduce Chebyshev mapping into the newly generated population for local search. Based on the steepest descent method, the fractional order identification using initial values is derived. The remaining parameters are derived through the OAMRLS algorithm. With the initial value obtained by ADELS, the identification result of the algorithm is more accurate. The simulation results illustrate the significance of the proposed algorithm

Metabolite Profiling of Wheat Response to Cultivar Improvement and Nitrogen Fertilizer

Both genetic improvement and the application of N fertilizer increase the quality and yields of wheat. However, the molecular kinetics that underlies the differences between them are not well understood. In this study, we performed a non-targeted metabolomic analysis on wheat cultivars from different release years to comprehensively investigate the metabolic differences between cultivar and N treatments. The results revealed that the plant height and tiller number steadily decreased with increased ears numbers, whereas the grain number and weight increased with genetic improvement. Following the addition of N fertilizer, the panicle numbers and grain weights increased in an old cultivar, whereas the panicle number and grain number per panicle increased in a modern cultivar. For the 1950s to 2010s cultivar, the yield increases due to genetic improvements ranged from −1.9% to 96.7%, whereas that of N application ranged from 19.1% to 81.6%. Based on the untargeted metabolomics approach, the findings demonstrated that genetic improvements induced 1.4 to 7.4 times more metabolic alterations than N fertilizer supply. After the addition of N, 69.6%, 29.4%, and 33.3% of the differential metabolites were upregulated in the 1950s, 1980s, and 2010s cultivars, respectively. The results of metabolic pathway analysis of the identified differential metabolites via genetic improvement indicated enrichment in 1-2 KEGG pathways, whereas the application of N fertilizer enriched 2–4 pathways. Our results provide new insights into the molecular mechanisms of wheat quality and grain yield developments

Application of cosmic‐ray neutron probes for measuring soil moisture in rocky areas of the Taihang Mountains, North China

Abstract The cosmic‐ray neutron probe (CRNP) is a mesoscale and noninvasive method for measuring soil moisture and has been widely studied and applied. However, studies of its applicability in rocky mountainous areas are still challenging in complex topography and high gravel content. In this study, a field experiment was carried out to assess the applicability of the CRNP for measuring soil moisture in rocky areas of Taihang Mountains of North China. The results showed that the Pearson correlation coefficient and the root mean square error between the soil moisture from CRNP and the drying method are 0.911 and 0.025 m3 m−3, respectively, indicating that the CRNP can estimate the average soil moisture well in the study area. Compared with the capacitive sensor, the CRNP overestimated soil moisture when small rainfall events occurred, which was caused by the interception of canopy and litter. The nonlinear weighting method performed better than the linear weighting method in representing average soil moisture within the CRNP footprint. The high gravel content that contained high lattice water content reduced the penetration depth of CRNP. Biomass reduces the accuracy of the CRNP by affecting the neutron intensity. In summary, CRNP can measure soil moisture accurately in rocky areas of the Taihang Mountains, especially in dry environments with low biomass

Efficient Physiological and Nutrient Use Efficiency Responses of Maize Leaves to Drought Stress under Different Field Nitrogen Conditions

Inadequate water and nitrogen (N) supplies can limit the productivity of maize. Climate change will likely increase drought in many regions on a global scale. The determination of N fertilizer rates under field drought conditions will be critical toward the reduction of agricultural risk. For this study, drought-resistant/sensitive cultivars were selected as experimental samples. Our results revealed that drought stress reduced the relative water content (RWC) of leaves, which resulted in leaf curling, while decreasing photosynthesis levels and N accumulation. In contrast to those without N treatments, the application of N significantly increased grain yields by 26.8% during the wet year but increased only by 5.4% during the dry year. Under the same N levels, the reduction in yield caused by drought increased with the increased application of N. This was because the application of the N fertilizer translated to increase the leaf area and transpiration, exacerbated the soil water loss and induced a leaf curling state in maize, which had deleterious effects on photosynthesis and N absorption. During the dry year, the yields of drought-sensitive cultivars were even less than those without the application of N. Compared with those of drought-sensitive cultivars, the RWCs of drought-resistant cultivars decreased more rapidly, and they entered the state of leaf curling earlier. Thus, N fertilizer inputs should be reduced, and the extent of N fertilization for drought-sensitive cultivars should be reduced even further