IQ67 DOMAIN protein 21 is critical for indentation formation in pavement cell morphogenesis.

In plants, cortical microtubules anchor to the plasma membrane in arrays and play important roles in cell shape. However, the molecular mechanism of microtubule binding proteins, which connect the plasma membrane and cortical microtubules in cell morphology remains largely unknown. Here, we report that a plasma membrane and microtubule dual-localized IQ67 domain protein, IQD21, is critical for cotyledon pavement cell (PC) morphogenesis in Arabidopsis. iqd21 mutation caused increased indentation width, decreased lobe length, and similar lobe number of PCs, whereas IQD21 overexpression had a different effect on cotyledon PC shape. Weak overexpression led to increased lobe number, decreased indentation width, and similar lobe length, while moderate or great overexpression resulted in decreased lobe number, indentation width, and lobe length of PCs. Live-cell observations revealed that IQD21 accumulation at indentation regions correlates with lobe initiation and outgrowth during PC development. Cell biological and genetic approaches revealed that IQD21 promotes transfacial microtubules anchoring to the plasma membrane via its polybasic sites and bundling at the indentation regions in both periclinal and anticlinal walls. IQD21 controls cortical microtubule organization mainly through promoting Katanin 1-mediated microtubule severing during PC interdigitation. These findings provide the genetic evidence that transfacial microtubule arrays play a determinant role in lobe formation, and the insight into the molecular mechanism of IQD21 in transfacial microtubule organization at indentations and puzzle-shaped PC development.

Combining UAV-RGB high-throughput field phenotyping and genome-wide association study to reveal genetic variation of rice germplasms in dynamic response to drought stress.

Accurate and high-throughput phenotyping of the dynamic response of a large rice population to drought stress in the field is a bottleneck for genetic dissection and breeding of drought resistance. Here, high-efficiency and high-frequent image acquisition by an unmanned aerial vehicle (UAV) was utilized to quantify the dynamic drought response of a rice population under field conditions. Deep convolutional neural networks (DCNNs) and canopy height models were applied to extract highly correlated phenotypic traits including UAV-based leaf-rolling score (LRS_uav), plant water content (PWC_uav) and a new composite trait, drought resistance index by UAV (DRI_uav). The DCNNs achieved high accuracy (correlation coefficient R = 0.84 for modeling set and R = 0.86 for test set) to replace manual leaf-rolling rating. PWC_uav values were precisely estimated (correlation coefficient R = 0.88) and DRI_uav was modeled to monitor the drought resistance of rice accessions dynamically and comprehensively. A total of 111 significantly associated loci were detected by genome-wide association study for the three dynamic traits, and 30.6% of them were not detected in previous mapping studies using nondynamic drought response traits. Unmanned aerial vehicle and deep learning are confirmed effective phenotyping techniques for more complete genetic dissection of rice dynamic responses to drought and exploration of valuable alleles for drought resistance improvement.

A double-stranded RNA binding protein enhances drought resistance via protein phase separation in rice.

Drought stress significantly impacts global rice production, highlighting the critical need to understand the genetic basis of drought resistance in rice. Here, through a genome-wide association study, we reveal that natural variations in DROUGHT RESISTANCE GENE 9 (DRG9), encoding a double-stranded RNA (dsRNA) binding protein, contribute to drought resistance. Under drought stress, DRG9 condenses into stress granules (SGs) through liquid-liquid phase separation via a crucial α-helix. DRG9 recruits the mRNAs of OsNCED4, a key gene for the biosynthesis of abscisic acid, into SGs and protects them from degradation. In drought-resistant DRG9 allele, natural variations in the coding region, causing an amino acid substitution (G267F) within the zinc finger domain, increase DRG9's binding ability to OsNCED4 mRNA and enhance drought resistance. Introgression of the drought-resistant DRG9 allele into the elite rice Huanghuazhan significantly improves its drought resistance. Thus, our study underscores the role of a dsRNA-binding protein in drought resistance and its promising value in breeding drought-resistant rice.

Serine protease NAL1 exerts pleiotropic functions through degradation of TOPLESS-related corepressor in rice.

NARROW LEAF 1 (NAL1) is a breeding-valuable pleiotropic gene that affects multiple agronomic traits in rice, although the molecular mechanism is largely unclear. Here, we report that NAL1 is a serine protease and displays a novel hexameric structure consisting of two ATP-mediated doughnut-shaped trimeric complexes. Moreover, we identified TOPLESS-related corepressor OsTPR2 involved in multiple growth and development processes as the substrate of NAL1. We found that NAL1 degraded OsTPR2, thus modulating the expression of downstream genes related to hormone signalling pathways, eventually achieving its pleiotropic physiological function. An elite allele, NAL1A, which may have originated from wild rice, could increase grain yield. Furthermore, the NAL1 homologues in different crops have a similar pleiotropic function to NAL1. Our study uncovers a NAL1-OsTPR2 regulatory module and provides gene resources for the design of high-yield crops.

Genome-wide association study revealed genetic variations of ABA sensitivity controlled by multiple stress-related genes in rice.

Abscisic acid (ABA) is a critical phytohormone that regulates multiple physiological processes including plant growth and stress tolerance. The core ABA signaling pathway has been well established, but genetic variations mediating ABA responses remain largely unknown. In this study, we performed genome-wide association study (GWAS) to identify loci and genes associated with ABA sensitivity (reflected by seed germination inhibition by ABA) in a panel of 425 rice accessions. The seed germination assay revealed that Aus and indica rice had stronger ABA sensitivity than japonica rice. A total of 48 non-redundant association loci were detected in the indica subpopulation and whole population, and 386 genes in these loci were responsive to ABA or abiotic stresses. Eight association loci were overlapped with previously reported loci for yield under drought stress or for drought-indicative image traits. Haplotype analyses of important candidate genes such as OsSAPK6, a key component in the ABA signaling core, were performed to identify key SNPs/InDels that may affect gene functions through promoter activity regulation, amino acid variation, or gene splicing. These results provide insights into the genetic basis of ABA sensitivity related to stress responses.

Reversible Histone H2B Monoubiquitination Fine-Tunes Abscisic Acid Signaling and Drought Response in Rice.

Histone H2B monoubiquitination (H2Bub1) plays important roles in several physiological and developmental processes, but its roles in the regulation of plant stress responses remain elusive. Here, we report that H2Bub1 is crucially involved in abscisic acid (ABA) signaling and drought response in rice. We found that rice HISTONE MONOUBIQUITINATION2 (OsHUB2), an E3 ligase for H2Bub1, interacted with OsbZIP46, a key transcription factor regulating ABA signaling and drought response in rice. Genetic analyses suggest that OsHUB2, upregulated by drought and ABA, positively modulates ABA sensitivity and drought resistance. The H2Bub1 levels were increased in the target genes of OsbZIP46 under the drought stress and ABA treatments, which were positively correlated with their increased expression levels. Interestingly, MODD, a reported suppressor of ABA signaling and drought resistance by mediating OsbZIP46 deactivation and degradation, could reduce the H2Bub1 levels in the target genes of OsbZIP46 by recruiting a putative deubiquitinase OsOTLD1. Suppression of OsOTLD1 in vivo resulted in increased H2Bub1 levels and expression of OsbZIP46 target genes. Collectively, these findings established an elaborate mechanism of histone monoubiquitination in the fine-turning of ABA signaling and drought response by balancing H2Bub1 deposition and removal.

Novel Digital Features Discriminate Between Drought Resistant and Drought Sensitive Rice Under Controlled and Field Conditions.

Dynamic quantification of drought response is a key issue both for variety selection and for functional genetic study of rice drought resistance. Traditional assessment of drought resistance traits, such as stay-green and leaf-rolling, has utilized manual measurements, that are often subjective, error-prone, poorly quantified and time consuming. To relieve this phenotyping bottleneck, we demonstrate a feasible, robust and non-destructive method that dynamically quantifies response to drought, under both controlled and field conditions. Firstly, RGB images of individual rice plants at different growth points were analyzed to derive 4 features that were influenced by imposition of drought. These include a feature related to the ability to stay green, which we termed greenness plant area ratio (GPAR) and 3 shape descriptors [total plant area/bounding rectangle area ratio (TBR), perimeter area ratio (PAR) and total plant area/convex hull area ratio (TCR)]. Experiments showed that these 4 features were capable of discriminating reliably between drought resistant and drought sensitive accessions, and dynamically quantifying the drought response under controlled conditions across time (at either daily or half hourly time intervals). We compared the 3 shape descriptors and concluded that PAR was more robust and sensitive to leaf-rolling than the other shape descriptors. In addition, PAR and GPAR proved to be effective in quantification of drought response in the field. Moreover, the values obtained in field experiments using the collection of rice varieties were correlated with those derived from pot-based experiments. The general applicability of the algorithms is demonstrated by their ability to probe archival Miscanthus data previously collected on an independent platform. In conclusion, this image-based technology is robust providing a platform-independent tool for quantifying drought response that should be of general utility for breeding and functional genomics in future.

Genome-Wide Association Studies of Image Traits Reveal Genetic Architecture of Drought Resistance in Rice.

Understanding how plants respond to drought can benefit drought resistance (DR) breeding. Using a non-destructive phenotyping facility, 51 image-based traits (i-traits) for 507 rice accessions were extracted. These i-traits can be used to monitor drought responses and evaluate DR. High heritability and large variation of these traits was observed under drought stress in the natural population. A genome-wide association study (GWAS) of i-traits and traditional DR traits identified 470 association loci, some containing known DR-related genes. Of these 470 loci, 443 loci (94%) were identified using i-traits, 437 loci (93%) co-localized with previously reported DR-related quantitative trait loci, and 313 loci (66.6%) were reproducibly identified by GWAS in different years. Association networks, established based on GWAS results, revealed hub i-traits and hub loci. This demonstrates the feasibility and necessity of dissecting the complex DR trait into heritable and simple i-traits. As proof of principle, we illustrated the power of this integrated approach to identify previously unreported DR-related genes. OsPP15 was associated with a hub i-trait, and its role in DR was confirmed by genetic transformation experiments. Furthermore, i-traits can be used for DR linkage analyses, and 69 i-trait locus associations were identified by both GWAS and linkage analysis of a recombinant inbred line population. Finally, we confirmed the relevance of i-traits to DR in the field. Our study provides a promising novel approach for the genetic dissection and discovery of causal genes for DR.

Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization.

BACKGROUND: Rice panicle phenotyping is important in rice breeding, and rice panicle segmentation is the first and key step for image-based panicle phenotyping. Because of the challenge of illumination differentials, panicle shape deformations, rice accession variations, different reproductive stages and the field's complex background, rice panicle segmentation in the field is a very large challenge. RESULTS: In this paper, we propose a rice panicle segmentation algorithm called Panicle-SEG, which is based on simple linear iterative clustering superpixel regions generation, convolutional neural network classification and entropy rate superpixel optimization. To build the Panicle-SEG-CNN model and test the segmentation effects, 684 training images and 48 testing images were randomly selected, respectively. Six indicators, including Qseg, Sr, SSIM, Precision, Recall and F-measure, are employed to evaluate the segmentation effects, and the average segmentation results for the 48 testing samples are 0.626, 0.730, 0.891, 0.821, 0.730, and 76.73%, respectively. Compared with other segmentation approaches, including HSeg, i2 hysteresis thresholding and jointSeg, the proposed Panicle-SEG algorithm has better performance on segmentation accuracy. Meanwhile, the executing speed is also improved when combined with multithreading and CUDA parallel acceleration. Moreover, Panicle-SEG was demonstrated to be a robust segmentation algorithm, which can be expanded for different rice accessions, different field environments, different camera angles, different reproductive stages, and indoor rice images. The testing dataset and segmentation software are available online. CONCLUSIONS: In conclusion, the results demonstrate that Panicle-SEG is a robust method for panicle segmentation, and it creates a new opportunity for nondestructive yield estimation.