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Abstract Background In response to drought stress (DS), plants undergo complex processes that entail significant transcriptome reprogramming. However, the intricate relationship between the dynamic alterations in the three-dimensional (3D) genome and the modulation of gene co-expression in drought responses remains a relatively unexplored area. Results In this study, we reconstruct high-resolution 3D genome maps based on genomic regions marked by H3K9ac, an active histone modification that dynamically responds to soil water variations in rice. We discover a genome-wide disconnection of 3D genome contact upon DS with over 10,000 chromatin loops lost, which are partially recovered in the subsequent re-watering. Loops integrating promoter–promoter interactions (PPI) contribute to gene expression in addition to basal H3K9ac modifications. Moreover, H3K9ac-marked promoter regions with high affinities in mediating PPIs, termed as super-promoter regions (SPRs), integrate spatially clustered PPIs in a super-enhancer-like manner. Interestingly, the knockout mutation of OsbZIP23, a well-defined DS-responsive transcription factor, leads to the disassociation of over 80% DS-specific PPIs and decreased expression of the corresponding genes under DS. As a case study, we show how OsbZIP23 integrates the PPI cluster formation and the co-expression of four dehydrin genes, RAB16A–D, through targeting the RAB16C SPR in a stress signaling-dependent manner. Conclusions Our high-resolution 3D genome maps unveil the principles and details of dynamic genome folding in response to water supply variations and illustrate OsbZIP23 as an indispensable integrator of the yet unique 3D genome organization that is essential for gene co-expression under DS in rice.

Abstract Drought and heat are major abiotic stresses frequently coinciding to threaten rice production. Despite hundreds of stress-related genes being identified, only a few have been confirmed to confer resistance to multiple stresses in crops. Here we report ONAC023, a hub stress regulator that integrates the regulations of both drought and heat tolerance in rice. ONAC023 positively regulates drought and heat tolerance at both seedling and reproductive stages. Notably, the functioning of ONAC023 is obliterated without stress treatment and can be triggered by drought and heat stresses at two layers. The expression of ONAC023 is induced in response to stress stimuli. We show that overexpressed ONAC23 is translocated to the nucleus under stress and evidence from protoplasts suggests that the dephosphorylation of the remorin protein OSREM1.5 can promote this translocation. Under drought or heat stress, the nuclear ONAC023 can target and promote the expression of diverse genes, such as OsPIP2;7, PGL3, OsFKBP20-1b, and OsSF3B1, which are involved in various processes including water transport, reactive oxygen species homeostasis, and alternative splicing. These results manifest that ONAC023 is fine-tuned to positively regulate drought and heat tolerance through the integration of multiple stress-responsive processes. Our findings provide not only an underlying connection between drought and heat responses, but also a promising candidate for engineering multi-stress-resilient rice.

Summary: Understanding molecular mechanisms of plant cellular response to heat stress will help to improve crop tolerance and yield in the global warming era. Here, we show that deacetylation of non-histone proteins mediated by cytoplasmic histone deacetylase HDA714 is required for plant tolerance to heat stress in rice. Heat stress reduces overall protein lysine acetylation, which depends on HDA714. Being induced by heat stress, HDA714 loss of function reduces, but its overexpression enhances rice tolerance to heat stress. Under heat stress, HDA714-mediated deacetylation of metabolic enzymes stimulates glycolysis. In addition, HDA714 protein is found within heat-induced stress granules (SGs), and many SG proteins are acetylated under normal temperature. HDA714 interacts with and deacetylates several SG proteins. HDA714 loss of function increases SG protein acetylation levels and impairs SG formation. Collectively, these results indicate that HDA714 responds to heat stress to deacetylate cellular proteins, control metabolic activities, stimulate SG formation, and confer heat tolerance in rice.

Abstract Background Epigenetic marks are reprogrammed during sexual reproduction. In flowering plants, DNA methylation is only partially remodeled in the gametes and the zygote. However, the timing and functional significance of the remodeling during plant gametogenesis remain obscure. Results Here we show that DNA methylation remodeling starts after male meiosis in rice, with non-CG methylation, particularly at CHG sites, being first enhanced in the microspore and subsequently decreased in sperm. Functional analysis of rice CHG methyltransferase genes CMT3a and CMT3b indicates that CMT3a functions as the major CHG methyltransferase in rice meiocyte, while CMT3b is responsible for the increase of CHG methylation in microspore. The function of the two histone demethylases JMJ706 and JMJ707 that remove H3K9me2 may contribute to the decreased CHG methylation in sperm. During male gametogenesis CMT3a mainly silences TE and TE-related genes while CMT3b is required for repression of genes encoding factors involved in transcriptional and translational activities. In addition, CMT3b functions to repress zygotic gene expression in egg and participates in establishing the zygotic epigenome upon fertilization. Conclusion Collectively, the results indicate that DNA methylation is dynamically remodeled during male gametogenesis, distinguish the function of CMT3a and CMT3b in sex cells, and underpin the functional significance of DNA methylation remodeling during rice reproduction.

Abstract 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.

Abstract Plants orchestrate drought responses at metabolic level but the genetic basis remains elusive in rice. In this study, 233 drought-responsive metabolites (DRMs) were quantified in a large rice population comprised of 510 diverse accessions at the reproductive stage. Large metabolic variations in drought responses were detected, and little correlation of metabolic levels between drought and normal conditions were observed. Interestingly, most of these DRMs could predict drought resistance in high accuracy. Genome-wide association study revealed 2522 significant association signals for 233 DRMs, and 98% (2471/2522) of the signals were co-localized with the association loci for drought-related phenotypic traits in the same population or the linkage-mapped QTLs for drought resistance in other populations. Totally, 10 candidate genes were efficiently identified for nine DRMs, seven of which harbored cis-eQTLs under drought condition. Based on comparative GWAS of common DRMs in rice and maize, representing irrigated and upland crops, we have identified three pairs of homologous genes associated with three DRMs between the two crops. Among the homologous genes, a transferase gene responsible for metabolic variation of N-feruloylputrescine was confirmed to confer enhanced drought resistance in rice. Our study provides not only genetic architecture of metabolic responses to drought stress in rice but also metabolic data resources to reveal the common and specific metabolite-mediated drought responses in different crops.

Abstract Epigenetic reprogramming occurs during reproduction to reset the genome for early development. In flowering plants, mechanistic details of parental methylation remodeling in zygote remain elusive. Here we analyze allele-specific DNA methylation in rice hybrid zygotes and during early embryo development and show that paternal DNA methylation is predominantly remodeled to match maternal allelic levels upon fertilization, which persists after the first zygotic division. The DNA methylation remodeling pattern supports the predominantly maternal-biased gene expression during zygotic genome activation (ZGA) in rice. However, parental allelic-specific methylations are reestablished at the globular embryo stage and associate with allelic-specific histone modification patterns in hybrids. These results reveal that paternal DNA methylation is remodeled to match the maternal pattern during zygotic genome reprogramming and suggest existence of a chromatin memory allowing parental allelic-specific methylation to be maintained in the hybrid.

Knowledge of rice (Oryza sativa L.) genes and various DNA markers can be used in genomic breeding programs aimed at developing improved elite rice cultivars. We used an efficient genomic breeding approach to pyramid four resistance genes (Pi2, Xa23, Bph14, and Bph15) in the popular photoperiod- and thermo-sensitive genic male sterile (PTGMS) rice line Feng39S. We performed foreground selection for the target genes, followed by recombinant selection and background selection. This process reduced the sizes of the genomic segments harboring the target genes (566.8 kb for Pi2, 1143.9 kb for Xa23, 774.7 kb for Bph14, and 1574.9 kb for Bph15) and accelerated the recovery of the recurrent parent genome to proportions ranging from 98.77% to 99.16%, thus resulting in four near-isogenic lines. To assemble the four resistance genes in Feng39S, we performed a double-way cross combined with foreground and background selection to generate two improved lines of Feng39S (Pi2 + Xa23 + Bph14 + Bph15) with a recurrent parent genome recovery of 98.98%. The two lines showed agronomic performance, grain quality, and fertility–sterility transition characteristics similar to those of the original Feng39S line. The newly developed PTGMS lines and corresponding hybrid combinations were resistant to various field blast isolates and seven representative isolates of bacterial blight. At the seedling stage, the lines also showed resistance against brown planthopper. This study provides an efficient and accurate genomic breeding approach for introducing desirable traits into PTGMS lines.

Abstract Background Drought threatens the food supply of the world population. Dissecting the dynamic responses of plants to drought will be beneficial for breeding drought-tolerant crops, as the genetic controls of these responses remain largely unknown. Results Here we develop a high-throughput multiple optical phenotyping system to noninvasively phenotype 368 maize genotypes with or without drought stress over a course of 98 days, and collected multiple optical images, including color camera scanning, hyperspectral imaging, and X-ray computed tomography images. We develop high-throughput analysis pipelines to extract image-based traits (i-traits). Of these i-traits, 10,080 were effective and heritable indicators of maize external and internal drought responses. An i-trait-based genome-wide association study reveals 4322 significant locus-trait associations, representing 1529 quantitative trait loci (QTLs) and 2318 candidate genes, many that co-localize with previously reported maize drought responsive QTLs. Expression QTL (eQTL) analysis uncovers many local and distant regulatory variants that control the expression of the candidate genes. We use genetic mutation analysis to validate two new genes, ZmcPGM2 and ZmFAB1A, which regulate i-traits and drought tolerance. Moreover, the value of the candidate genes as drought-tolerant genetic markers is revealed by genome selection analysis, and 15 i-traits are identified as potential markers for maize drought tolerance breeding. Conclusion Our study demonstrates that combining high-throughput multiple optical phenotyping and GWAS is a novel and effective approach to dissect the genetic architecture of complex traits and clone drought-tolerance associated genes.

Panicle number is directly related to rice yield, so panicle detection and counting has always been one of the most important scientific research topics. Panicle counting is a challenging task due to many factors such as high density, high occlusion, and large variation in size, shape, posture et.al. Deep learning provides state-of-the-art performance in object detection and counting. Generally, the large images need to be resized to fit for the video memory. However, small panicles would be missed if the image size of the original field rice image is extremely large. In this paper, we proposed a rice panicle detection and counting method based on deep learning which was especially designed for detecting rice panicles in rice field images with large image size. Different object detectors were compared and YOLOv5 was selected with MAPE of 3.44% and accuracy of 92.77%. Specifically, we proposed a new method for removing repeated detections and proved that the method outperformed the existing NMS methods. The proposed method was proved to be robust and accurate for counting panicles in field rice images of different illumination, rice accessions, and image input size. Also, the proposed method performed well on UAV images. In addition, an open-access and user-friendly web portal was developed for rice researchers to use the proposed method conveniently.

Rice panicle phenotyping is required in rice breeding for high yield and grain quality. To fully evaluate spikelet and kernel traits without threshing and hulling, using X-ray and RGB scanning, we developed an integrated rice panicle phenotyping system and a corresponding image analysis pipeline. We compared five methods of counting spikelets and found that Faster R-CNN achieved high accuracy (R2 of 0.99) and speed. Faster R-CNN was also applied to indica and japonica classification and achieved 91% accuracy. The proposed integrated panicle phenotyping method offers benefit for rice functional genetics and breeding.

The traits of rice panicles play important roles in yield assessment, variety classification, rice breeding, and cultivation management. Most traditional grain phenotyping methods require threshing and thus are time-consuming and labor-intensive; moreover, these methods cannot obtain 3D grain traits. In this work, based on X-ray computed tomography, we proposed an image analysis method to extract twenty-two 3D grain traits. After 104 samples were tested, the R2 values between the extracted and manual measurements of the grain number and grain length were 0.980 and 0.960, respectively. We also found a high correlation between the total grain volume and weight. In addition, the extracted 3D grain traits were used to classify the rice varieties, and the support vector machine classifier had a higher recognition accuracy than the stepwise discriminant analysis and random forest classifiers. In conclusion, we developed a 3D image analysis pipeline to extract rice grain traits using X-ray computed tomography that can provide more 3D grain information and could benefit future research on rice functional genomics and rice breeding.

Abstract 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.

Abstract With progress of genetic sequencing technology, plant genomics has experienced rapid development and subsequently triggered the progress of plant phenomics. In this study, a high-throughput hyperspectral imaging system (HHIS) was developed to obtain 1,540 hyperspectral indices at whole-plant level during tillering, heading, and ripening stages. These indices were used to quantify traditional agronomic traits and to explore genetic variation. We performed genome-wide association study (GWAS) of these indices and traditional agronomic traits in a global rice collection of 529 accessions. With the genome-level suggestive P-value threshold, 989 loci were identified. Of the 1,540 indices, we detected 502 significant indices (designated as hyper-traits) that exhibited phenotypic and genetic relationship with traditional agronomic traits and had high heritability. Many hyper-trait-associated loci could not be detected using traditional agronomic traits. For example, we identified a candidate gene controlling chlorophyll content (Chl). This gene, which was not identified based on Chl, was significantly associated with a chlorophyll-related hyper-trait in GWAS and was demonstrated to control Chl. Moreover, our study demonstrates that red edge (680–760 nm) is vital for rice research for phenotypic and genetic insights. Thus, combination of HHIS and GWAS provides a novel platform for dissection of complex traits and for crop breeding.

Miniature inverted-repeat transposable elements (MITEs) are common components of eukaryotic genomes. Here the authors show that a stowaway-like MITE in the 3′-untranslated region of the riceGhd2gene can suppress translation in a DCL3a-dependent manner representing a novel role for transposons in translation.

Drought stress can cause huge crop production losses. Drought resistance consists of complex traits, and is regulated by arrays of unclear networks at the molecular level. A stress-responsive NAC transcription factor gene SNAC1 has been reported for its function in the positive regulation of drought resistance in rice, and several downstream SNAC1 targets have been identified. However, a complete regulatory network mediated by SNAC1 in drought response remains unknown. In this study, we performed Chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA-Seq of SNAC1-overexpression transgenic rice (SNAC1-OE) lines and wild-type under normal and moderate drought stress conditions, to identify all SNAC1 target genes at a genome-wide scale by RNA-Seq analyses. We detected 980 differentially expressed genes (DEGs) in the SNAC1-OE lines compared to the wild-type control under drought stress conditions. By ChIP-Seq analyses, we identified 4,339 SNAC1-binding genes under drought stress conditions (SNAC1BGDs). By combining the DEGs and SNAC1BGDs, we identified 93 SNAC1-targeted genes involved in drought responses (SNAC1TGDs). Most SNAC1TGDs are involved in transcriptional regulation, response to water loss, and other processes related to stress responses. Moreover, the major motifs in the SNAC1BGDs promoters include a NAC recognition sequence (NACRS) and an ABA responsive element (ABRE). SNAC1-OE lines are more sensitive to ABA than wild-type. SNAC1 can bind to the OsbZIP23 promoter, an important ABA signaling regulator, and positively regulate the expression of several ABA signaling genes.

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.

The homeodomain-leucine zipper (HD-Zip) transcription factor family plays vital roles in plant development and morphogenesis as well as responses to biotic and abiotic stresses. In barley, a recessive mutation in Vrs1 (HvHox1) changes two-rowed barley to six-rowed barley, which improves yield considerably. The Vrs1 gene encodes an HD-Zip subfamily I transcription factor. Phylogenetic analysis has shown that the rice HD-Zip I genes Oshox12 and Oshox14 are the closest homologues of Vrs1. Here, we show that Oshox12 and Oshox14 are ubiquitously expressed with higher levels in developing panicles. Trans-activation assays in yeast and rice protoplasts demonstrated that Oshox12 and Oshox14 can bind to a specific DNA sequence, AH1 (CAAT(A/T)ATTG), and activate reporter gene expression. Overexpression of Oshox12 and Oshox14 in rice resulted in reduced panicle length and a dwarf phenotype. In addition, Oshox14 overexpression lines showed a deficiency in panicle exsertion. Our findings suggest that Oshox12 and Oshox14 may be involved in the regulation of panicle development. This study provides a significant advancement in understanding the functions of HD-Zip transcription factors in rice.

Jasmonates (JAs) and abscisic acid (ABA) are phytohormones known play important roles in plant response and adaptation to various abiotic stresses including salinity, drought, wounding, and cold. JAZ (JASMONATE ZIM-domain) proteins have been reported to play negative roles in JA signaling. However, direct evidence is still lacking that JAZ proteins regulate drought resistance. In this study, OsJAZ1 was investigated for its role in drought resistance in rice. Expression of OsJAZ1 was strongly responsive to JA treatment, and it was slightly responsive to ABA, salicylic acid, and abiotic stresses including drought, salinity, and cold. The OsJAZ1-overexpression rice plants were more sensitive to drought stress treatment than the wild-type (WT) rice Zhonghua 11 (ZH11) at both the seedling and reproductive stages, while the jaz1 T-DNA insertion mutant plants showed increased drought tolerance compared to the WT plants. The OsJAZ1-overexpression plants were hyposensitive to MeJA and ABA, whereas the jaz1 mutant plants were hypersensitive to MeJA and ABA. In addition, there were significant differences in shoot and root length between the OsJAZ1 transgenic and WT plants under the MeJA and ABA treatments. A subcellular localization assay indicated that OsJAZ1 was localized in both the nucleus and cytoplasm. Transcriptome profiling analysis by RNA-seq revealed that the expression levels of many genes in the ABA and JA signaling pathways exhibited significant differences between the OsJAZ1-overexpression plants and WT ZH11 under drought stress treatment. Quantitative real-time PCR confirmed the expression profiles of some of the differentially expressed genes, including OsNCED4, OsLEA3, RAB21, OsbHLH006, OsbHLH148, OsDREB1A, OsDREB1B, SNAC1, and OsCCD1. These results together suggest that OsJAZ1 plays a role in regulating the drought resistance of rice partially via the ABA and JA pathways.

Plant phenomics has the potential to accelerate progress in understanding gene functions and environmental responses. Progress has been made in automating high-throughput plant phenotyping. However, few studies have investigated automated rice panicle counting. This paper describes a novel method for automatically and nonintrusively determining rice panicle numbers during the full heading stage by analyzing color images of rice plants taken from multiple angles. Pot-grown rice plants were transferred via an industrial conveyer to an imaging chamber. Color images from different angles were automatically acquired as a turntable rotated the plant. The images were then analyzed and the panicle number of each plant was determined. The image analysis pipeline consisted of extracting the i2 plane from the original color image, segmenting the image, discriminating the panicles from the rest of the plant using an artificial neural network, and calculating the panicle number in the current image. The panicle number of the plant was taken as the maximum of the panicle numbers extracted from all 12 multi-angle images. A total of 105 rice plants during the full heading stage were examined to test the performance of the method. The mean absolute error of the manual and automatic count was 0.5, with 95.3% of the plants yielding absolute errors within ± 1. The method will be useful for evaluating rice panicles and will serve as an important supplementary method for high-throughput rice phenotyping.

A variety of adverse conditions including drought stress severely affect rice production. Root system plays a critical role in drought avoidance, which is one of the major mechanisms of drought resistance. In this study, we adopted genome-wide association study (GWAS) to dissect the genetic basis controlling various root traits by using a natural population consisting of 529 representative rice accessions. A total of 413 suggestive associations, containing 143 significant associations, were identified for 21 root traits, such as maximum root length, root volume, and root dry weight under normal and drought stress conditions at the maturation stage. More than 80 percent of the suggestive loci were located in the region of reported QTLs for root traits, while about 20 percent of suggestive loci were novel loci detected in this study. Besides, 11 reported root-related genes, including DRO1, WOX11, and OsPID, were found to co-locate with the association loci. We further proved that the association results can facilitate the efficient identification of causal genes for root traits by the two case studies of Nal1 and OsJAZ1. These loci and their candidate causal genes provide an important basis for the genetic improvement of root traits and drought resistance.

Pigments absorb light, transform it into energy, and provide reaction sites for photosynthesis; thus, the quantification of pigment distribution is vital to plant research. Traditional methods for the quantification of pigments are time-consuming and not suitable for the high-throughput digitization of rice pigment distribution. In this study, using a hyperspectral imaging system, we developed an integrated image analysis pipeline for automatically processing enormous amounts of hyperspectral data. We also built models for accurately quantifying 4 pigments (chlorophyll a, chlorophyll b, total chlorophyll and carotenoid) from rice leaves and determined the important bands (700-760 nm) associated with these pigments. At the tillering stage, the R2 values and mean absolute percentage errors of the models were 0.827–0.928 and 6.94–12.84%, respectively. The hyperspectral data and these models can be combined for digitizing the distribution of the chlorophyll with high resolution (0.11 mm/pixel). In summary, the integrated hyperspectral image analysis pipeline and selected models can be used to quantify the chlorophyll distribution in rice leaves. The use of this technique will benefit rice functional genomics and rice breeding.

Drought is one of the major abiotic stresses threatening rice (Oryza sativa) production worldwide. Drought resistance is controlled by multiple genes, and therefore, a multi-gene genetic engineering strategy is theoretically useful for improving drought resistance. However, the experimental evidence for such a strategy is still lacking. In this study, a few drought-responsive genes from rice were assembled by a multiple-round site-specific assembly system, and the constructs were introduced into the rice cultivar KY131 via Agrobacterium-mediated transformation. The transgenic lines of the multi-gene and corresponding single-gene constructs were pre-evaluated for drought resistance. We found that the co-overexpression of two genes, encoding a constitutively active form of a bZIP transcription factor (OsbZIP46CA1) and a protein kinase (SAPK6) involved in the abscisic acid signaling pathway, showed significantly enhanced drought resistance compared with the single-gene transgenic lines and the negative transgenic plants. Single-copy lines of this bi-gene combination (named XL22) and the corresponding single-gene lines were further evaluated for drought resistance in the field using agronomical traits. The results showed that XL22 exhibited greater yield, biomass, spikelet number, and grain number under moderate drought stress conditions. The seedling survival rate of XL22 and the single-gene overexpressors after drought stress treatment also supported the drought resistance results. Furthermore, expression profiling by RNA-Seq revealed that many genes involved in the stress response were specifically up-regulated in the drought-treated XL22 lines and some of the stress-related genes activated in CA1-OE and SAPK6-OE were distinct, which could partially explain the different performances of these lines with respect to drought resistance. In addition, the XL22 seedlings showed improved tolerance to heat and cold stresses. Our results demonstrate that the multi-gene assembly in an appropriate combination may be a promising approach in the genetic improvement of drought resistance.

For many tiller crops, the plant architecture (PA), including the plant fresh weight, plant height, number of tillers, tiller angle and stem diameter, significantly affects the grain yield. In this study, we propose a method based on volumetric reconstruction for high-throughput three-dimensional (3D) wheat PA studies. The proposed methodology involves plant volumetric reconstruction from multiple images, plant model processing and phenotypic parameter estimation and analysis. This study was performed on 80 Triticum aestivum plants, and the results were analyzed. Comparing the automated measurements with manual measurements, the mean absolute percentage error (MAPE) in the plant height and the plant fresh weight was 2.71% (1.08cm with an average plant height of 40.07cm) and 10.06% (1.41g with an average plant fresh weight of 14.06g), respectively. The root mean square error (RMSE) was 1.37cm and 1.79g for the plant height and plant fresh weight, respectively. The correlation coefficients were 0.95 and 0.96 for the plant height and plant fresh weight, respectively. Additionally, the proposed methodology, including plant reconstruction, model processing and trait extraction, required only approximately 20s on average per plant using parallel computing on a graphics processing unit (GPU), demonstrating that the methodology would be valuable for a high-throughput phenotyping platform.

Total green leaf area (GLA) is an important trait for agronomic studies. However, existing methods for estimating the GLA of individual rice plants are destructive and labor-intensive. A nondestructive method for estimating the total GLA of individual rice plants based on multi-angle color images is presented. Using projected areas of the plant in images, linear, quadratic, exponential and power regression models for estimating total GLA were evaluated. Tests demonstrated that the side-view projected area had a stronger relationship with the actual total leaf area than the top-projected area. And power models fit better than other models. In addition, the use of multiple side-view images was an efficient method for reducing the estimation error. The inclusion of the top-view projected area as a second predictor provided only a slight improvement of the total leaf area estimation. When the projected areas from multi-angle images were used, the estimated leaf area (ELA) using the power model and the actual leaf area had a high correlation coefficient (R2 > 0.98), and the mean absolute percentage error (MAPE) was about 6%. The method was capable of estimating the total leaf area in a nondestructive, accurate and efficient manner, and it may be used for monitoring rice plant growth.

Identification of host genes involved in defense responses is one of most critical steps leading to the elucidation of disease resistance mechanisms in plants. In this study, two different cloning strategies were employed to identify defense-related genes from a tropical japonica rice cultivar (Oryza sativa cv. Drew). With the use of bacterial colony arrays, differential screening of a blast fungus (Pyricularia grisea)-induced rice cDNA library led to the isolation of 22 distinct rice genes that are expressed differentially in response to blast infection. Sequence analysis indicates that most of them are full-length cDNAs encoding pathogenesis-related proteins or other relatively abundant proteins. In combination with treatments of cycloheximide plus jasmonic acid (JA) or benzothiadiazole (BTH) in rice seedlings, the polymerase chain reaction-based suppression subtractive hybridization also was conducted to search for immediate early (IE) defense-related genes whose transcription is independent of de novo protein synthesis. The initial screening of only 768 subtracted clones resulted in the identification of 34 distinct IE genes that are induced by JA, BTH, and/or blast infection. Database searches revealed that these IE genes encode putative mitogen-activated protein kinase, diacylglycerol kinase, zinc finger protein, RelA-SpoT protein, ankyrin-containing protein, ABC transporter, β-ketoacyl-CoA synthase, and other potential defense-signaling components. Further characterization of these novel IE genes will likely facilitate the elucidation of defense signal transduction in rice plants.