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Heat stress occurring during rice (Oryza sativa) grain development reduces grain quality, which often manifests as increased grain chalkiness. Although the impact of heat stress on grain yield is well-studied, the genetic basis of rice grain quality under heat stress is less explored as quantifying grain quality is less tractable than grain yield. To address this, we used an image-based colorimetric assay (Red, R; and Green, G) for genome-wide association analysis to identify genetic loci underlying the phenotypic variation in rice grains exposed to heat stress. We found the R to G pixel ratio (RG) derived from mature grain images to be effective in distinguishing chalky grains from translucent grains derived from control (28/24°C) and heat stressed (36/32°C) plants. Our analysis yielded a novel gene, riceChalky Grain 5(OsCG5) that regulates natural variation for grain chalkiness under heat stress.OsCG5encodes a grain-specific, expressed protein of unknown function. Accessions with lower transcript abundance ofOsCG5exhibit higher chalkiness, which correlates with higher RG values under stress. These findings are supported by increased chalkiness ofOsCG5knock-out (KO) mutants relative to wildtype (WT) under heat stress. Grains from plants overexpressingOsCG5are less chalky than KOs but comparable to WT under heat stress. Compared to WT and OE, KO mutants exhibit greater heat sensitivity for grain size and weight relative to controls. Collectively, these results show that the natural variation atOsCG5may contribute towards rice grain quality under heat stress. 

Abstract The asymmetric increase in average nighttime temperatures relative to increase in average daytime temperatures due to climate change is decreasing grain yield and quality in rice. Therefore, a better genome-level understanding of the impact of higher night temperature stress on the weight of individual grains is essential for future development of more resilient rice. We investigated the utility of metabolites obtained from grains to classify high night temperature (HNT) conditions of genotypes, and metabolites and single-nucleotide polymorphisms (SNPs) to predict grain length, width, and perimeter phenotypes using a rice diversity panel. We found that the metabolic profiles of rice genotypes alone could be used to classify control and HNT conditions with high accuracy using random forest or extreme gradient boosting. Best linear unbiased prediction and BayesC showed greater metabolic prediction performance than machine learning models for grain-size phenotypes. Metabolic prediction was most effective for grain width, resulting in the highest prediction performance. Genomic prediction performed better than metabolic prediction. Integrating metabolites and genomics simultaneously in a prediction model slightly improved prediction performance. We did not observe a difference in prediction between the control and HNT conditions. Several metabolites were identified as auxiliary phenotypes that could be used to enhance the multi-trait genomic prediction of grain-size phenotypes. Our results showed that, in addition to SNPs, metabolites collected from grains offer rich information to perform predictive analyses, including classification modeling of HNT responses and regression modeling of grain-size-related phenotypes in rice. 

Abstract It is challenging to identify the smallest microexons (≤15-nt) due to their small size. Consequently, these microexons are often misannotated or missed entirely during genome annotation. Here, we develop a pipeline to accurately identify 2,398 small microexons in 10 diverse plant species using 990 RNA-seq datasets, and most of them have not been annotated in the reference genomes. Analysis reveals that microexons tend to have increased detained flanking introns that require post-transcriptional splicing after polyadenylation. Examination of 45 conserved microexon clusters demonstrates that microexons and associated gene structures can be traced back to the origin of land plants. Based on these clusters, we develop an algorithm to genome-wide model coding microexons in 132 plants and find that microexons provide a strong phylogenetic signal for plant organismal relationships. Microexon modeling reveals diverse evolutionary trajectories, involving microexon gain and loss and alternative splicing. Our work provides a comprehensive view of microexons in plants. 

Accurate measurement of seed size parameters is essential for both breeding efforts aimed at enhancing yields and basic research focused on discovering genetic components that regulate seed size. To address this need, we have developed an open-source graphical user interface (GUI) software, SeedExtractor that determines seed size and shape (including area, perimeter, length, width, circularity, and centroid), and seed color with capability to process a large number of images in a time-efficient manner. In this context, our application takes ∼2 s for analyzing an image, i.e., significantly less compared to the other tools. As this software is open-source, it can be modified by users to serve more specific needs. The adaptability of SeedExtractor was demonstrated by analyzing scanned seeds from multiple crops. We further validated the utility of this application by analyzing mature-rice seeds from 231 accessions in Rice Diversity Panel 1. The derived seed-size traits, such as seed length, width, were used for genome-wide association analysis. We identified known loci for regulating seed length ( GS3 ) and width ( qSW5/GW5 ) in rice, which demonstrates the accuracy of this application to extract seed phenotypes and accelerate trait discovery. In summary, we present a publicly available application that can be used to determine key yield-related traits in crops. 

Abstract A transient heat stress occurring during early seed development in rice (Oryza sativa) reduces seed size by altering endosperm development. However, the relationship between the timing of the stress and specific developmental stage on heat sensitivity is not well‐understood. To address this, we imposed a series of non‐overlapping heat stress treatments and found that young seeds are most sensitive during the first two days after flowering. Temporal transcriptome analysis of developing, heat stressed (35°C) seeds during this window shows thatInositol‐requiring enzyme 1 (IRE1)‐mediated endoplasmic reticulum (ER) stress response and jasmonic acid (JA) pathways are the early (1–3 h) drivers of heat stress response. We propose that increased JA levels under heat stress may precede ER stress response as JA application promotes the spliced form ofOsbZIP50,an ER response marker gene linked to IRE1‐specific pathway. This study presents temporal and mechanistic insights into the role of JA and ER stress signalling during early heat stress response of rice seeds that impact both grain size and quality. Modulating the heat sensitivity of the early sensing pathways and downstream endosperm development genes can enhance rice resilience to transient heat stress events. 

Summary A higher minimum (night‐time) temperature is considered a greater limiting factor for reduced rice yield than a similar increase in maximum (daytime) temperature. While the physiological impact of high night temperature (HNT) has been studied, the genetic and molecular basis of HNT stress response remains unexplored.We examined the phenotypic variation for mature grain size (length and width) in a diverse set of rice accessions under HNT stress. Genome‐wide association analysis identified several HNT‐specific loci regulating grain size as well as loci that are common for optimal and HNT stress conditions.A novel locus contributing to grain width under HNT conditions colocalized withFie1, a component of the FIS‐PRC2 complex. Our results suggest that the allelic difference controlling grain width under HNT is a result of differential transcript‐level response ofFie1in grains developing under HNT stress.We present evidence to support the role ofFie1in grain size regulation by testing overexpression (OE) and knockout mutants under heat stress. The OE mutants were either unaltered or had a positive impact on mature grain size under HNT, while the knockouts exhibited significant grain size reduction under these conditions.