Genome-wide association study and gene network analyses reveal potential candidate genes for high night temperature tolerance in rice

Natural variation in LONELY GUY-Like 1 regulates rice grain weight under warmer night conditions

Global nighttime temperatures are rising at twice the rate of daytime temperatures and pose a challenge for rice (Oryza sativa) production. High nighttime temperature (HNT) stress affects rice yield by reducing grain weight, size, and fertility. Although the genes associated with these yield parameters have been identified and characterized under normal temperatures, the genetic basis of grain weight regulation under HNT stress remains less explored. We examined the natural variation for rice single grain weight (SGW) under HNT stress imposed during grain development. A genome-wide association analysis identified several loci associated with grain weight under HNT stress. A locus, SGW1, specific to HNT conditions resolved to LONELY GUY-Like 1 (LOGL1), which encodes a putative cytokinin-activation enzyme. We demonstrated that LOGL1 contributes to allelic variation at SGW1. Accessions with lower LOGL1 transcript abundance had higher grain weight under HNT. This was supported by the higher grain weight of logl1-mutants relative to the wild type under HNT. Compared to logl1-mutants, LOGL1 over-expressers showed increased sensitivity to HNT. We showed that LOGL1 regulates the thiamin biosynthesis pathway, which is under circadian regulation, which in turn is likely perturbed by HNT stress. These findings provide a genetic source to enhance rice adaptation to warming night temperatures and improve our mechanistic understanding of HNT stress tolerance pathways.

High night temperature stress on rice (Oryza sativa) - insights from phenomics to physiology. A review

Rice (Oryza sativa ) faces challenges to yield and quality due to urbanisation, deforestation and climate change, which has exacerbated high night temperature (HNT). This review explores the impacts of HNT on the physiological, molecular and agronomic aspects of rice growth. Rise in minimum temperature threatens a potential 41% reduction in rice yield by 2100. HNT disrupts rice growth stages, causing reduced seed germination, biomass, spikelet sterility and poor grain development. Recent findings indicate a 4.4% yield decline for every 1°C increase beyond 27°C, with japonica ecotypes exhibiting higher sensitivity than indica. We examine the relationships between elevated CO2 , nitrogen regimes and HNT, showing that the complexity of balancing positive CO2 effects on biomass with HNT challenges. Nitrogen enrichment proves crucial during the vegetative stage but causes disruption to reproductive stages, affecting grain yield and starch synthesis. Additionally, we elucidate the impact of HNT on plant respiration, emphasising mitochondrial respiration, photorespiration and antioxidant responses. Genomic techniques, including CRISPR-Cas9, offer potential for manipulating genes for HNT tolerance. Plant hormones and carbohydrate enzymatic activities are explored, revealing their intricate roles in spikelet fertility, grain size and starch metabolism under HNT. Gaps in understanding genetic factors influencing heat tolerance and potential trade-offs associated with hormone applications remain. The importance of interdisciplinary collaboration is needed to provide a holistic approach. Research priorities include the study of regulatory mechanisms, post-anthesis effects, cumulative HNT exposure and the interaction between climate variability and HNT impact to provide a research direction to enhance rice resilience in a changing climate.

New insights into QTNs and potential candidate genes governing rice yield via a multi-model genome-wide association study

BACKGROUND

Rice (Oryza sativa L.) is one of the globally important staple food crops, and yield-related traits are prerequisites for improved breeding efficiency in rice. Here, we used six different genome-wide association study (GWAS) models for 198 accessions, with 553,229 single nucleotide markers (SNPs) to identify the quantitative trait nucleotides (QTNs) and candidate genes (CGs) governing rice yield.

RESULTS

Amongst the 73 different QTNs in total, 24 were co-localized with already reported QTLs or loci in previous mapping studies. We obtained fifteen significant QTNs, pathway analysis revealed 10 potential candidates within 100kb of these QTNs that are predicted to govern plant height, days to flowering, and plot yield in rice. Based on their superior allelic information in 20 elite and 6 inferior genotypes, we found a higher percentage of superior alleles in the elite genotypes in comparison to inferior genotypes. Further, we implemented expression analysis and enrichment analysis enabling the identification of 73 candidate genes and 25 homologues of Arabidopsis, 19 of which might regulate rice yield traits. Of these candidate genes, 40 CGs were found to be enriched in 60 GO terms of the studied traits for instance, positive regulator metabolic process (GO:0010929), intracellular part (GO:0031090), and nucleic acid binding (GO:0090079). Haplotype and phenotypic variation analysis confirmed that LOC_OS09G15770, LOC_OS02G36710 and LOC_OS02G17520 are key candidates associated with rice yield.

CONCLUSIONS

Overall, we foresee that the QTNs, putative candidates elucidated in the study could summarize the polygenic regulatory networks controlling rice yield and be useful for breeding high-yielding varieties.

Multi-model genome-wide association studies for appearance quality in rice

Improving the quality of the appearance of rice is critical to meet market acceptance. Mining putative quality-related genes has been geared towards the development of effective breeding approaches for rice. In the present study, two SL-GWAS (CMLM and MLM) and three ML-GWAS (FASTmrEMMA, mrMLM, and FASTmrMLM) genome-wide association studies were conducted in a subset of 3K-RGP consisting of 198 rice accessions with 553,831 SNP markers. A total of 594 SNP markers were identified using the mixed linear model method for grain quality traits. Additionally, 70 quantitative trait nucleotides (QTNs) detected by the ML-GWAS models were strongly associated with grain aroma (AR), head rice recovery (HRR, %), and percentage of grains with chalkiness (PGC, %). Finally, 39 QTNs were identified using single- and multi-locus GWAS methods. Among the 39 reliable QTNs, 20 novel QTNs were identified for the above-mentioned three quality-related traits. Based on annotation and previous studies, four functional candidate genes (LOC_Os01g66110, LOC_Os01g66140, LOC_Os07g44910, and LOC_Os02g14120) were found to influence AR, HRR (%), and PGC (%), which could be utilized in rice breeding to improve grain quality traits.

Night-time warming in the field reduces nocturnal stomatal conductance and grain yield but does not alter daytime physiological responses

Global nocturnal temperatures are rising more rapidly than daytime temperatures and have a large effect on crop productivity. In particular, stomatal conductance at night (gsn ) is surprisingly poorly understood and has not been investigated despite constituting a significant proportion of overall canopy water loss. Here, we present the results of 3 yr of field data using 12 spring Triticum aestivum genotypes which were grown in NW Mexico and subjected to an artificial increase in night-time temperatures of 2°C. Under nocturnal heating, grain yields decreased (1.9% per 1°C) without significant changes in daytime leaf-level physiological responses. Under warmer nights, there were significant differences in the magnitude and decrease in gsn , values of which were between 9 and 33% of daytime rates while respiration appeared to acclimate to higher temperatures. Decreases in grain yield were genotype-specific; genotypes categorised as heat tolerant demonstrated some of the greatest declines in yield in response to warmer nights. We conclude the essential components of nocturnal heat tolerance in wheat are uncoupled from resilience to daytime temperatures, raising fundamental questions for physiological breeding. Furthermore, this study discusses key physiological traits such as pollen viability, root depth and irrigation type may also play a role in genotype-specific nocturnal heat tolerance.

Molecular mapping and characterization of QTLs for grain quality traits in a RIL population of US rice under high nighttime temperature stress

Elevated nighttime temperatures resulting from climate change significantly impact the rice crop worldwide. The rice (Oryza sativa L.) plant is highly sensitive to high nighttime temperature (HNT) during grain-filling (reproductive stage). HNT stress negatively affects grain quality traits and has a major impact on the value of the harvested rice crop. In addition, along with grain dimensions determining rice grain market classes, the grain appearance and quality traits determine the rice grain market value. During the last few years, there has been a major concern for rice growers and the rice industry over the prevalence of rice grains opacity and the reduction of grain dimensions affected by HNT stress. Hence, the improvement of heat-stress tolerance to maintain grain quality of the rice crop under HNT stress will bolster future rice value in the market. In this study, 185 F₁₂-recombinant inbred lines (RILs) derived from two US rice cultivars, Cypress (HNT-tolerant) and LaGrue (HNT-sensitive) were screened for the grain quality traits grain length (GL), grain width (GW), and percent chalkiness (%chalk) under control and HNT stress conditions and evaluated to identify the genomic regions associated with the grain quality traits. In total, there were 15 QTLs identified; 6 QTLs represented under control condition explaining 3.33% to 8.27% of the phenotypic variation, with additive effects ranging from - 0.99 to 0.0267 on six chromosomes and 9 QTLs represented under HNT stress elucidating 6.39 to 51.53% of the phenotypic variation, with additive effects ranging from - 8.8 to 0.028 on nine chromosomes for GL, GW, and % chalk. These 15 QTLs were further characterized and scanned for natural genetic variation in a japonica diversity panel (JDP) to identify candidate genes for GL, GW, and %chalk. We found 6160 high impact single nucleotide polymorphisms (SNPs) characterized as such depending on their type, region, functional class, position, and proximity to the gene and/or gene features, and 149 differentially expressed genes (DEGs) in the 51 Mbp genomic region comprising of the 15 QTLs. Out of which, 11 potential candidate genes showed high impact SNP associations. Therefore, the analysis of the mapped QTLs and their genetic dissection in the US grown Japonica rice genotypes at genomic and transcriptomic levels provide deep insights into genetic variation beneficial to rice breeders and geneticists for understanding the mechanisms related to grain quality under heat stress in rice.

Reproductive-Stage Heat Stress in Cereals: Impact, Plant Responses and Strategies for Tolerance Improvement

Reproductive-stage heat stress (RSHS) poses a major constraint to cereal crop production by damaging main plant reproductive structures and hampering reproductive processes, including pollen and stigma viability, pollination, fertilization, grain setting and grain filling. Despite this well-recognized fact, research on crop heat stress (HS) is relatively recent compared to other abiotic stresses, such as drought and salinity, and in particular, RSHS studies in cereals are considerably few in comparison with seedling-stage and vegetative-stage-centered studies. Meanwhile, climate change-exacerbated HS, independently or synergistically with drought, will have huge implications on crop performance and future global food security. Fortunately, due to their sedentary nature, crop plants have evolved complex and diverse transient and long-term mechanisms to perceive, transduce, respond and adapt to HS at the molecular, cell, physiological and whole plant levels. Therefore, uncovering the molecular and physiological mechanisms governing plant response and tolerance to RSHS facilitates the designing of effective strategies to improve HS tolerance in cereal crops. In this review, we update our understanding of several aspects of RSHS in cereals, particularly impacts on physiological processes and yield; HS signal perception and transduction; and transcriptional regulation by heat shock factors and heat stress-responsive genes. We also discuss the epigenetic, post-translational modification and HS memory mechanisms modulating plant HS tolerance. Moreover, we offer a critical set of strategies (encompassing genomics and plant breeding, transgenesis, omics and agronomy) that could accelerate the development of RSHS-resilient cereal crop cultivars. We underline that a judicious combination of all of these strategies offers the best foot forward in RSHS tolerance improvement in cereals. Further, we highlight critical shortcomings to RSHS tolerance investigations in cereals and propositions for their circumvention, as well as some knowledge gaps, which should guide future research priorities. Overall, our review furthers our understanding of HS tolerance in plants and supports the rational designing of RSHS-tolerant cereal crop cultivars for the warming climate.

Selection of Candidate Genes Conferring Blast Resistance and Heat Tolerance in Rice through Integration of Meta-QTLs and RNA-Seq

Due to global warming, high temperature is a significant environmental stress for rice production. Rice (Oryza sativa L.), one of the most crucial cereal crops, is also seriously devastated by Magnaporthe oryzae. Therefore, it is essential to breed new rice cultivars with blast and heat tolerance. Although progress had been made in QTL mapping and RNA-seq analysis in rice in response to blast and heat stresses, there are few reports on simultaneously mining blast-resistant and heat-tolerant genes. In this study, we separately conducted meta-analysis of 839 blast-resistant and 308 heat-tolerant QTLs in rice. Consequently, 7054 genes were identified in 67 blast-resistant meta-QTLs with an average interval of 1.00 Mb. Likewise, 6425 genes were obtained in 40 heat-tolerant meta-QTLs with an average interval of 1.49 Mb. Additionally, using differentially expressed genes (DEGs) in the previous research and GO enrichment analysis, 55 DEGs were co-located on the common regions of 16 blast-resistant and 14 heat-tolerant meta-QTLs. Among, OsChib3H-c, OsJAMyb, Pi-k, OsWAK1, OsMT2b, OsTPS3, OsHI-LOX, OsACLA-2 and OsGS2 were the significant candidate genes to be further investigated. These results could provide the gene resources for rice breeding with excellent resistance to these 2 stresses, and help to understand how plants response to the combination stresses of blast fungus and high temperature.

Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature

BACKGROUND

Rice is a major staple food crop for more than half the world's population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed.

RESULTS

We use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area.

CONCLUSIONS

We have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.