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The major environmental factors limiting rice growth and production are osmotic stresses such as drought and high salinity. High osmotic stresses directly disrupt cellular activities, leading to plant growth retardation or death. Plants have various response mechanisms to survive under such stresses. Understanding rice's stress response mechanisms is necessary to enhance the osmotic stress tolerance of rice. However, assessing specific physiological responses to osmotic stresses is difficult because multiple environmental factors affect rice growth. Here, we describe a simple method for analyzing the osmotic stress responses of rice plants using a hydroponic culture system. This method allows comprehensive gene expression and phenotypic analyses under osmotic stress conditions in rice. Various osmotic stress conditions and samples can be tested simultaneously because this method is small-scale. In addition, the procedure is easy, and highly reproductive results can be obtained., (© 2025. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Plant roots are essential for water and nutrient uptake, as well as resistance to abiotic stresses. While measuring root systems under field conditions is labor-intensive, most quantitative trait loci (QTLs) related to root traits have been detected under artificial conditions. However, QTLs identified under artificial conditions may not always manifest the expected effects that are observed under field conditions. To address this issue, we developed RSApaddy3D, a rapid phenotyping method for rice root systems, using X-ray computed tomography (CT) volumes of soil blocks collected from paddies. RSApaddy3D employs 2-dimensional kernel filters tailored to extract disk-shaped fragments from the CT volumes. Tubular root fragments are expected to exhibit disk-shaped cross-sections along the x-, y-, or z-axes. By applying these filters along all three axes and integrating the results, 3-dimensional root fragments can be accurately extracted. Furthermore, vectorizing the root system enables geometrical removal of the roots of neighboring individuals. We conducted a genome-wide association study (GWAS) of root diameter, number, and growth angle in 133 Japanese rice varieties and detected three QTLs (qNCR1, qNCR2, and qRGA1) that were associated with each trait. This process was completed within 10 person-days from soil monolith collection in the paddy to the GWAS. Without RSApaddy3D, roots would need to be washed from the soil monolith and measured, which is estimated to require >500 person-days. Therefore, RSApaddy3D was approximately 50× more labor-saving. In summary, we have demonstrated that RSApaddy3D is an efficient method for phenotyping rice root systems under field conditions., (© 2024 Society for Experimental Biology and John Wiley & Sons Ltd.)

Background and Aims: Root system architecture (RSA) plays a key role in plant adaptation to drought, because deep rooting enables better water uptake than shallow rooting under terminal drought. Understanding RSA during early plant development is essential for improving crop yields, because early drought can affect subsequent shoot growth. Herein, we demonstrate that root distribution in the topsoil significantly impacts shoot growth during the early stages of rice (Oryza sativa) development under drought, as assessed through three-dimensional image analysis., Methods: We used 109 F12 recombinant inbred lines obtained from a cross between shallow-rooting lowland rice and deep-rooting upland rice, representing a population with diverse RSA. We applied a moderate drought during the early development of rice grown in a plant pot (25 cm in height) by stopping irrigation 14 days after sowing. Time-series RSA at 14, 21 and 28 days after sowing was visualized by X-ray computed tomography and, subsequently, compared between drought and well-watered conditions. After this analysis, we investigated drought-avoidant RSA further by testing 20 randomly selected recombinant inbred lines in drought conditions., Key Results: We inferred the root location that most influences shoot growth using a hierarchical Bayes approach: the root segment depth that impacted shoot growth positively ranged between 1.7 and 3.4 cm in drought conditions and between 0.0 and 1.7 cm in well-watered conditions. Drought-avoidant recombinant inbred lines had a higher root density in the lower layers of the topsoil compared with the others., Conclusions: Fine classification of soil layers using three-dimensional image analysis revealed that increasing root density in the lower layers of the topsoil, rather than in the subsoil, is advantageous for drought avoidance during the early growth stage of rice., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Transcriptome-based prediction of complex phenotypes is a relatively new statistical method that links genetic variation to phenotypic variation. The selection of large-effect genes based on a priori biological knowledge is beneficial for predicting oligogenic traits; however, such a simple gene selection method is not applicable to polygenic traits because causal genes or large-effect loci are often unknown. Here, we used several gene-level features and tested whether it was possible to select a gene subset that resulted in better predictive ability than using all genes for predicting a polygenic trait., Results: Using the phenotypic values of shoot and root traits and transcript abundances in leaves and roots of 57 rice accessions, we evaluated the predictive abilities of the transcriptome-based prediction models. Leaf transcripts predicted shoot phenotypes, such as plant height, more accurately than root transcripts, whereas root transcripts predicted root phenotypes, such as crown root length, more accurately than leaf transcripts. Furthermore, we used the following three features to train the prediction model: (1) tissue specificity of the transcripts, (2) ontology annotations, and (3) co-expression modules for selecting gene subsets. Although models trained by a gene subset often resulted in lower predictive abilities than the model trained by all genes, some gene subsets showed improved predictive ability. For example, using genes expressed in roots but not in leaves, the predictive ability for crown root diameter was improved by more than 10% (R 2  = 0.59 when using all genes; R 2  = 0.66, using 1,554 root-specifically expressed genes). Similarly, genes annotated as "gibberellic acid sensitivity" showed higher predictive ability than using all genes for root dry weight., Conclusions: Our results highlight both the possibility and difficulty of selecting an appropriate gene subset to predict polygenic traits from transcript abundance, given the current biological knowledge and information. Further integration of multiple sources of information, as well as improvements in gene characterization, may enable the selection of an optimal gene set for the prediction of polygenic phenotypes., (© 2024. The Author(s).)

Background: X-ray computed tomography (CT) is a powerful tool for measuring plant root growth in soil. However, a rapid scan with larger pots, which is required for throughput-prioritized crop breeding, results in high noise levels, low resolution, and blurred root segments in the CT volumes. Moreover, while plant root segmentation is essential for root quantification, detailed conditional studies on segmenting noisy root segments are scarce. The present study aimed to investigate the effects of scanning time and deep learning-based restoration of image quality on semantic segmentation of blurry rice (Oryza sativa) root segments in CT volumes., Results: VoxResNet, a convolutional neural network-based voxel-wise residual network, was used as the segmentation model. The training efficiency of the model was compared using CT volumes obtained at scan times of 33, 66, 150, 300, and 600 s. The learning efficiencies of the samples were similar, except for scan times of 33 and 66 s. In addition, The noise levels of predicted volumes differd among scanning conditions, indicating that the noise level of a scan time ≥ 150 s does not affect the model training efficiency. Conventional filtering methods, such as median filtering and edge detection, increased the training efficiency by approximately 10% under any conditions. However, the training efficiency of 33 and 66 s-scanned samples remained relatively low. We concluded that scan time must be at least 150 s to not affect segmentation. Finally, we constructed a semantic segmentation model for 150 s-scanned CT volumes, for which the Dice loss reached 0.093. This model could not predict the lateral roots, which were not included in the training data. This limitation will be addressed by preparing appropriate training data., Conclusions: A semantic segmentation model can be constructed even with rapidly scanned CT volumes with high noise levels. Given that scanning times ≥ 150 s did not affect the segmentation results, this technique holds promise for rapid and low-dose scanning. This study offers insights into images other than CT volumes with high noise levels that are challenging to determine when annotating., (© 2024. The Author(s).)

Nutrient availability in hydroponic solutions must be accurately monitored to maintain crop productivity; however, few cost-effective, accurate, real-time, and long-term monitoring technologies have been developed. In this study, we describe the development and application of cation-/anion-exchange chromatography with a neutral eluent (20-mmol/L sodium formate, pH 7.87) for the simultaneous separation (within 50 min) of ionic nutrients, including K+, NH4+, NO2-, NO3-, and phosphate ion, in a hydroponic fertilizer solution. Using the neutral eluent avoided degradation of the separation column during precipitation of metal ion species, such as hydroxides, with an alkaline eluent and oxidation of NO2- to NO3- with an acidic eluent. The suitability of the current method for monitoring ionic components in a hydroponic fertilizer solution was confirmed. Based on our data, we propose a controlled fertilizer strategy to optimize fertilizer consumption and reduce the chemical load of drained fertilizer solutions., (© The Author(s) 2024. Published by Oxford University Press on behalf of Japan Society for Bioscience, Biotechnology, and Agrochemistry.)

Crop genetic engineering for better root systems can offer practical solutions for food security and carbon sequestration; however, soil layers prevent the direct visualization of plant roots, thus posing a challenge to effective phenotyping. Here, we demonstrate an original device with a distributed fiber-optic sensor for fully automated, real-time monitoring of underground root development. We show that spatially encoding an optical fiber with a flexible and durable polymer film in a spiral pattern can significantly enhance sensor detection. After signal processing, the resulting device can detect the penetration of a submillimeter-diameter object in the soil, indicating more than a magnitude higher spatiotemporal resolution than previously reported with underground monitoring techniques. Additionally, we also developed computational models to visualize the roots of tuber crops and monocotyledons and then applied them to radish and rice to compare the results with those of X-ray computed tomography. The device's groundbreaking sensitivity and spatiotemporal resolution enable seamless and laborless phenotyping of root systems that are otherwise invisible underground., (© 2024. The Author(s).)

Field-grown rice plants are exposed to various stresses at different stages of their life cycle, but little is known about the effects of stage-specific stresses on phenomes and transcriptomes. In this study, we performed integrated time-course multiomics on rice at 3-d intervals from seedling to heading stage under six drought conditions in a well-controlled growth chamber. Drought stress at seedling and reproductive stages reduced yield performance by reducing seed number and setting rate, respectively. High temporal resolution analysis revealed that drought response occurred in two steps: a rapid response via the abscisic acid (ABA) signaling pathway and a slightly delayed DEHYDRATION-RESPONSIVE ELEMENT-BINDING PROTEIN (DREB) pathway, allowing plants to respond flexibly to deteriorating soil water conditions. Our long-term time-course multiomics showed that temporary drought stress delayed flowering due to prolonged expression of the flowering repressor gene GRAIN NUMBER, PLANT HEIGHT AND HEADING DATE 7 (Ghd7) and delayed expression of the florigen genes HEADING DATE 3a (Hd3a) and RICE FLOWERING LOCUS T 1 (RFT1). Our life-cycle multiomics dataset on rice shoots under drought conditions provides a valuable resource for further functional genomic studies to improve crop resilience to drought stress., (© The Author(s) 2023. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Root system architecture plays a crucial role in nutrient and water absorption during rice production. Genetic improvement of the rice root system requires elucidating its genetic control. Genome-wide association studies (GWASs) have identified genomic regions responsible for rice root phenotypes. However, candidate gene prioritization around the peak region often suffers from low statistical power and resolution. Transcriptomics enables other statistical mappings, such as transcriptome-wide association study (TWAS) and expression GWAS (eGWAS), which improve candidate gene identification by leveraging the natural variation of the expression profiles. To explore the genes responsible for root phenotypes, we conducted GWAS, TWAS, and eGWAS for 12 root phenotypes in 57 rice accessions using 427,751 single nucleotide polymorphisms (SNPs) and the expression profiles of 16,901 genes expressed in the roots. The GWAS identified three significant peaks, of which the most significant peak responsible for seven root phenotypes (crown root length, crown root surface area, number of crown root tips, lateral root length, lateral root surface area, lateral root volume, and number of lateral root tips) was detected at 6,199,732 bp on chromosome 8. In the most significant GWAS peak region, OsENT1 was prioritized as the most plausible candidate gene because its expression profile was strongly negatively correlated with the seven root phenotypes. In addition to OsENT1, OsEXPA31, OsSPL14, OsDEP1, and OsDEC1 were identified as candidate genes responsible for root phenotypes using TWAS. Furthermore, a cis-eGWAS peak SNP was detected for OsDjA6, which showed the eighth strongest association with lateral root volume in the TWAS. The cis-eGWAS peak SNP for OsDjA6 was in strong linkage disequilibrium (LD) with a GWAS peak SNP on the same chromosome for lateral root volume and in perfect LD with another SNP variant in a putative cis-element at the 518 bp upstream of the gene. These candidate genes provide new insights into the molecular breeding of root system architecture., (© 2023. The Author(s).)

Osmotic stresses, such as drought and high salinity, adversely affect plant growth and productivity. The phytohormone abscisic acid (ABA) accumulates in response to osmotic stress and enhances stress tolerance in plants by triggering multiple physiological responses through ABA signaling. Subclass III SNF1-related protein kinases 2 (SnRK2s) are key regulators of ABA signaling. Although SnRK2s have long been considered to be self-activated by autophosphorylation after release from PP2C-mediated inhibition, they were recently revealed to be activated by two independent subfamilies of group B Raf-like kinases, B2-RAFs and B3-RAFs, under osmotic stress conditions. However, the relationship between SnRK2 phosphorylation by these RAFs and SnRK2 autophosphorylation and the individual physiological roles of each RAF subfamily remain unknown. In this study, we indicated that B2-RAFs are constantly active and activate SnRK2s when released from PP2C-mediated inhibition by ABA-binding ABA receptors, whereas B3-RAFs are activated only under stress conditions in an ABA-independent manner and enhance SnRK2 activity. Autophosphorylation of subclass III SnRK2s is not sufficient for ABA responses, and B2-RAFs are needed to activate SnRK2s in an ABA-dependent manner. Using plants grown in soil, we found that B2-RAFs regulate subclass III SnRK2s at the early stage of drought stress, whereas B3-RAFs regulate SnRK2s at the later stage. Thus, B2-RAFs are essential kinases for the activation of subclass III SnRK2s in response to ABA under mild osmotic stress conditions, and B3-RAFs function as enhancers of SnRK2 activity under severe stress conditions.

Root system architecture affects the efficient uptake of water and nutrients in plants. The root growth angle, which is a critical component in determining root system architecture, is affected by root gravitropism; however, the mechanism of root gravitropism in rice remains largely unknown. In this study, we conducted a time-course transcriptome analysis of rice roots under conditions of simulated microgravity using a three-dimensional clinostat and following gravistimulation to detect candidate genes associated with the gravitropic response. We found that HEAT SHOCK PROTEIN ( HSP ) genes, which are involved in the regulation of auxin transport, were preferentially up-regulated during simulated microgravity conditions and rapidly down-regulated by gravistimulation. We also found that the transcription factor HEAT STRESS TRANSCRIPTION FACTOR A2s ( HSFA2 s) and HSFB2 s, showed the similar expression patterns with the HSP s. A co-expression network analysis and an in silico motif search within the upstream regions of the co-expressed genes revealed possible transcriptional control of HSP s by HSFs. Because HSFA2s are transcriptional activators, whereas HSFB2s are transcriptional repressors, the results suggest that the gene regulatory networks governed by HSFs modulate the gravitropic response through transcriptional control of HSP s in rice roots., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kuya, Nishijima, Kitomi, Kawakatsu and Uga.)

Many agronomic traits that are important in rice breeding are controlled by multiple genes. The extensive time and effort devoted so far to identifying and selecting such genes are still not enough to target multiple agronomic traits in practical breeding in Japan because of a lack of suitable plant materials in which to efficiently detect and validate beneficial alleles from diverse genetic resources. To facilitate the comprehensive analysis of genetic variation in agronomic traits among Asian cultivated rice, we developed 12 sets of chromosome segment substitution lines (CSSLs) with the japonica background, 11 of them in the same genetic background, using donors representing the genetic diversity of Asian cultivated rice. Using these materials, we overviewed the chromosomal locations of 1079 putative QTLs for seven agronomic traits and their allelic distribution in Asian cultivated rice through multiple linear regression analysis. The CSSLs will allow the effects of putative QTLs in the highly homogeneous japonica background to be validated., (Copyright © 2023 by JAPANESE SOCIETY OF BREEDING.)

Improving crop yield potential through an enhanced response to rising atmospheric CO 2 levels is an effective strategy for sustainable crop production in the face of climate change. Large-sized panicles (containing many spikelets per panicle) have been a recent ideal plant architecture (IPA) for high-yield rice breeding. However, few breeding programs have proposed an IPA under the projected climate change. Here, we demonstrate through the cloning of the rice (Oryza sativa) quantitative trait locus for MORE PANICLES 3 (MP3) that the improvement in panicle number increases grain yield at elevated atmospheric CO 2 levels. MP3 is a natural allele of OsTB1/FC1, previously reported as a negative regulator of tiller bud outgrowth. The temperate japonica allele advanced the developmental process in axillary buds, moderately promoted tillering, and increased the panicle number without negative effects on the panicle size or culm thickness in a high-yielding indica cultivar with large-sized panicles. The MP3 allele, containing three exonic polymorphisms, was observed in most accessions in the temperate japonica subgroups but was rarely observed in the indica subgroup. No selective sweep at MP3 in either the temperate japonica or indica subgroups suggested that MP3 has not been involved and utilized in artificial selection during domestication or breeding. A free-air CO 2 enrichment experiment revealed a clear increase of grain yield associated with the temperate japonica allele at elevated atmospheric CO 2 levels. Our findings show that the moderately increased panicle number combined with large-sized panicles using MP3 could be a novel IPA and contribute to an increase in rice production under climate change with rising atmospheric CO 2 levels., (© 2023 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.)

Rice is susceptible to abiotic stresses such as drought stress. To enhance drought resistance, elucidating the mechanisms by which rice plants adapt to intermittent drought stress that may occur in the field is an important requirement. Roots are directly exposed to changes in the soil water condition, and their responses to these environmental changes are driven by photosynthates. To visualize the distribution of photosynthates in the root system of rice plants under drought stress and recovery from drought stress, we combined X-ray computed tomography (CT) with open type positron emission tomography (OpenPET) and positron-emitting tracer imaging system (PETIS) with 11 C tracer. The short half-life of 11 C (20.39 min) allowed us to perform multiple experiments using the same plant, and thus photosynthate translocation was visualized as the same plant was subjected to drought stress and then re-irrigation for recovery. The results revealed that when soil is drier, 11 C-photosynthates mainly translocated to the seminal roots, likely to promote elongation of the root with the aim of accessing water stored in the lower soil layers. The photosynthates translocation to seminal roots immediately stopped after rewatering then increased significantly in crown roots. We suggest that when rice plant experiencing drought is re-irrigated from the bottom of pot, the destination of 11 C-photosynthates translocation immediately switches from seminal root to crown roots. We reveal that rice roots are responsive to changes in soil water conditions and that rice plants differentially adapts the dynamics of photosynthates translocation to crown roots and seminal roots depending on soil conditions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Miyoshi, Soma, Yin, Suzui, Noda, Enomoto, Nagao, Yamaguchi, Kawachi, Yoshida, Tashima, Yamaya, Kuya, Teramoto and Uga.)

Background: Root system architecture (RSA) is an essential characteristic for efficient water and nutrient absorption in terrestrial plants; its plasticity enables plants to respond to different soil environments. Better understanding of root plasticity is important in developing stress-tolerant crops. Non-invasive techniques that can measure roots in soils nondestructively, such as X-ray computed tomography (CT), are useful to evaluate RSA plasticity. However, although RSA plasticity can be measured by tracking individual root growth, only a few methods are available for tracking individual roots from time-series three-dimensional (3D) images., Results: We developed a semi-automatic workflow that tracks individual root growth by vectorizing RSA from time-series 3D images via two major steps. The first step involves 3D alignment of the time-series RSA images by iterative closest point registration with point clouds generated by high-intensity particles in potted soils. This alignment ensures that the time-series RSA images overlap. The second step consists of backward prediction of vectorization, which is based on the phenomenon that the root length of the RSA vector at the earlier time point is shorter than that at the last time point. In other words, when CT scanning is performed at time point A and again at time point B for the same pot, the CT data and RSA vectors at time points A and B will almost overlap, but not where the roots have grown. We assumed that given a manually created RSA vector at the last time point of the time series, all RSA vectors except those at the last time point could be automatically predicted by referring to the corresponding RSA images. Using 21 time-series CT volumes of a potted plant of upland rice (Oryza sativa), this workflow revealed that the root elongation speed increased with age. Compared with a workflow that does not use backward prediction, the workflow with backward prediction reduced the manual labor time by 95%., Conclusions: We developed a workflow to efficiently generate time-series RSA vectors from time-series X-ray CT volumes. We named this workflow 'RSAtrace4D' and are confident that it can be applied to the time-series analysis of RSA development and plasticity., (© 2022. The Author(s).)

To explore the genetic resources that could be utilized to help improve root system architecture phenotypes in rice ( Oryza sativa ), we have conducted genome-wide association studies to investigate maximum root length and crown root number in 135 10-day-old Japanese rice accessions grown hydroponically. We identified a quantitative trait locus for crown root number at approximately 32.7 Mbp on chromosome 4 and designated it qNCR1 ( quantitative trait locus for Number of Crown Root 1 ). A linkage disequilibrium map around qNCR1 suggested that three candidate genes are involved in crown root number: a cullin ( LOC_Os04g55030 ), a gibberellin 20 oxidase 8 ( LOC_Os04g55070 ), and a cyclic nucleotide-gated ion channel ( LOC_Os04g55080 ). The combination of haplotypes for each gene was designated as a haploblock, and haploblocks 1, 2, and 3 were defined. Compared to haploblock 1, the accessions with haploblocks 2 and 3 had fewer crown roots; approximately 5% and 10% reductions in 10-day-old plants and 15% and 25% reductions in 42-day-old plants, respectively. A Japanese leading variety Koshihikari and its progenies harbored haploblock 3. Their crown root number could potentially be improved using haploblocks 1 and 2., (Copyright © 2022 by JAPANESE SOCIETY OF BREEDING.)

Soil-surface roots (SORs) in rice are primary roots that elongate over or near the soil surface. SORs help avoid excessive reduction of stress that occurs in paddy, such as in saline conditions. SORs may also be beneficial for rice growth in phosphorus-deficient paddy fields. Thus, SOR is a useful trait for crop adaptation to certain environmental stresses. To identify a promising genetic material showing SOR, we established methods for evaluating SOR under different growth conditions. We introduced procedures to evaluate the genetic diversity of SOR in various growth stages and conditions: the Cup method allowed us to quantify SOR at the seedling stage, and the Basket method, using a basket buried in a pot or field, is useful in quantifying SOR at the adult stage. These protocols are expected to contribute not only to the evaluation of the genetic diversity of SOR, but also the isolation of related genes in rice., Competing Interests: Competing interestsThe authors declare no competing interest., (Copyright © 2022 The Authors; exclusive licensee Bio-protocol LLC.)

Root system architecture (RSA) determines unevenly distributed water and nutrient availability in soil. Genetic improvement of RSA, therefore, is related to crop production. However, RSA phenotyping has been carried out less frequently than above-ground phenotyping because measuring roots in the soil is difficult and labor intensive. Recent advancements have led to the digitalization of plant measurements; this digital phenotyping has been widely used for measurements of both above-ground and RSA traits. Digital phenotyping for RSA is slower and more difficult than for above-ground traits because the roots are hidden underground. In this review, we summarized recent trends in digital phenotyping for RSA traits. We classified the sample types into three categories: soil block containing roots, section of soil block, and root sample. Examples of the use of digital phenotyping are presented for each category. We also discussed room for improvement in digital phenotyping in each category., (Copyright © 2022 by JAPANESE SOCIETY OF BREEDING.)

A cultivation facility that can assist users in controlling the soil water condition is needed for accurately phenotyping plants under drought stress in an artificial environment. Here we report the Internet of Things-based pot system controlling optional treatment of soil water condition (iPOTs), an automatic irrigation system that mimics the drought condition in a growth chamber. The Wi-Fi-enabled iPOTs system allows water supply from the bottom of the pot, based on the soil water level set by the user, and automatically controls the soil water level at a desired depth. The iPOTs also allows users to monitor environmental parameters, such as soil temperature, air temperature, humidity, and light intensity, in each pot. To verify whether the iPOTs mimics the drought condition, we conducted a drought stress test on rice (Oryza sativa L.) varieties and near-isogenic lines, with diverse root system architecture, using the iPOTs system installed in a growth chamber. Similar to the results of a previous drought stress field trial, the growth of shallow-rooted rice accessions was severely affected by drought stress compared with that of deep-rooted accessions. The microclimate data obtained using the iPOTs system increased the accuracy of plant growth evaluation. Transcriptome analysis revealed that pot positions in the growth chamber had little impact on plant growth. Together, these results suggest that the iPOTs system is a reliable platform for phenotyping plants under drought stress., (© 2021 Society for Experimental Biology and John Wiley & Sons Ltd.)

Background: The root distribution in the soil is one of the elements that comprise the root system architecture (RSA). In monocots, RSA comprises radicle and crown roots, each of which can be basically represented by a single curve with lateral root branches or approximated using a polyline. Moreover, RSA vectorization (polyline conversion) is useful for RSA phenotyping. However, a robust software that can enable RSA vectorization while using noisy three-dimensional (3D) volumes is unavailable., Results: We developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping. It manages the single root (radicle or crown root) as a polyline (a vector), and the set of the polylines represents the entire RSA. RSAtrace3D vectorizes root segments between the two ends of a single root. By utilizing several base points on the root, RSAtrace3D suits noisy images if it is difficult to vectorize it using only two end nodes of the root. Additionally, by employing a simple tracking algorithm that uses the center of gravity (COG) of the root voxels to determine the tracking direction, RSAtrace3D efficiently vectorizes the roots. Thus, RSAtrace3D represents the single root shape more precisely than straight lines or spline curves. As a case study, rice (Oryza sativa) RSA was vectorized from X-ray computed tomography (CT) images, and RSA traits were calculated. In addition, varietal differences in RSA traits were observed. The vector data were 32,000 times more compact than raw X-ray CT images. Therefore, this makes it easier to share data and perform re-analyses. For example, using data from previously conducted studies. For monocot plants, the vectorization and phenotyping algorithm are extendable and suitable for numerous applications., Conclusions: RSAtrace3D is an RSA vectorization software for 3D RSA phenotyping for monocots. Owing to the high expandability of the RSA vectorization and phenotyping algorithm, RSAtrace3D can be applied not only to rice in X-ray CT images but also to other monocots in various 3D images. Since this software is written in Python language, it can be easily modified and will be extensively applied by researchers in this field., (© 2021. The Author(s).)

Improved phosphorus (P) use efficiency for crop production is needed, given the depletion of phosphorus ore deposits, and increasing ecological concerns about its excessive use. Root system architecture (RSA) is important in efficiently capturing immobile P in soils, while agronomically, localized P application near the roots is a potential approach to address this issue. However, the interaction between genetic traits of RSA and localized P application has been little understood. Near-isogenic lines (NILs) and their parent of rice (qsor1-NIL, Dro1-NIL, and IR64, with shallow, deep, and intermediate root growth angles (RGA), respectively) were grown in flooded pots after placing P near the roots at transplanting (P-dipping). The experiment identified that the P-dipping created an available P hotspot at the plant base of the soil surface layer where the qsor1-NIL had the greatest root biomass and root surface area despite no genotyipic differences in total values, whereby the qsor1-NIL had significantly greater biomass and P uptake than the other genotypes in the P-dipping. The superior surface root development of qsor1-NIL could have facilitated P uptakes from the P hotspot, implying that P-use efficiency in crop production can be further increased by combining genetic traits of RSA and localized P application.

Root system architecture affects plant drought resistance and other key agronomic traits such as lodging. However, although phenotypic and genomic variation has been extensively analyzed, few field studies have integrated phenotypic and transcriptomic information, particularly for below-ground traits such as root system architecture. Here, we report the phenotypic and transcriptomic landscape of 61 rice (Oryza sativa) accessions with highly diverse below-ground traits grown in an upland field. We found that four principal components explained the phenotypic variation and that accessions could be classified into four subpopulations (indica, aus, japonica and admixed) based on their tiller numbers and crown root diameters. Transcriptome analysis revealed that differentially expressed genes associated with specific subpopulations were enriched with stress response-related genes, suggesting that subpopulations have distinct stress response mechanisms. Root growth was negatively correlated with auxin-inducible genes, suggesting an association between auxin signaling and upland field conditions. A negative correlation between crown root diameter and stress response-related genes suggested that thicker crown root diameter is associated with resistance to mild drought stress. Finally, co-expression network analysis implemented with DNA affinity purification followed by sequencing analysis identified phytohormone signaling networks and key transcription factors negatively regulating crown root diameter. Our datasets provide a useful resource for understanding the genomic and transcriptomic basis of phenotypic variation under upland field conditions., (© 2021 Society for Experimental Biology and John Wiley & Sons Ltd.)

Roots are essential organs for capturing water and nutrients from the soil. In particular, root system architecture (RSA) determines the extent of the region of the soil where water and nutrients can be gathered. As global climate change accelerates, it will be important to improve belowground plant parts, as well as aboveground ones, because roots are front-line organs in the response to abiotic stresses such as drought, flooding, and salinity stress. However, using conventional breeding based on phenotypic selection, it is difficult to select breeding lines possessing promising RSAs to adapted to abiotic stress because roots remain hidden underground. Therefore, new breeding strategies that do not require phenotypic selection are necessary. Recent advances in molecular biology and biotechnology can be applied to the design-oriented breeding of RSA without phenotypic selection. Here I summarize recent progress in RSA ideotypes as "design" and RSA-related gene resources as "materials" that will be needed in leveraging these technologies for the RSA breeding. I also highlight the future challenges to design-oriented breeding of RSA and explore solutions to these challenges., (Copyright © 2021 by JAPANESE SOCIETY OF BREEDING.)

The root system architecture (RSA) of crops can affect their production, particularly in abiotic stress conditions, such as with drought, waterlogging, and salinity. Salinity is a growing problem worldwide that negatively impacts on crop productivity, and it is believed that yields could be improved if RSAs that enabled plants to avoid saline conditions were identified. Here, we have demonstrated, through the cloning and characterization of qSOR1 ( quantitative trait locus for SOIL SURFACE ROOTING 1 ), that a shallower root growth angle (RGA) could enhance rice yields in saline paddies. qSOR1 is negatively regulated by auxin, predominantly expressed in root columella cells, and involved in the gravitropic responses of roots. qSOR1 was found to be a homolog of DRO1 ( DEEPER ROOTING 1 ), which is known to control RGA. CRISPR-Cas9 assays revealed that other DRO1 homologs were also involved in RGA. Introgression lines with combinations of gain-of-function and loss-of-function alleles in qSOR1 and DRO1 demonstrated four different RSAs (ultra-shallow, shallow, intermediate, and deep rooting), suggesting that natural alleles of the DRO1 homologs could be utilized to control RSA variations in rice. In saline paddies, near-isogenic lines carrying the qSOR1 loss-of-function allele had soil-surface roots (SOR) that enabled rice to avoid the reducing stresses of saline soils, resulting in increased yields compared to the parental cultivars without SOR. Our findings suggest that DRO1 homologs are valuable targets for RSA breeding and could lead to improved rice production in environments characterized by abiotic stress., Competing Interests: The authors declare no competing interest, (Copyright © 2020 the Author(s). Published by PNAS.)

Water and nitrogen availability limit crop productivity globally more than most other environmental factors. Plant availability of macronutrients such as nitrate is, to a large extent, regulated by the amount of water available in the soil, and, during drought episodes, crops can become simultaneously water and nitrogen limited. In this review, we explore the intricate relationship between water and nitrogen transport in plants, from transpiration-driven mass flow in the soil to uptake by roots via membrane transporters and channels and transport to aerial organs. We discuss the roles of root architecture and of suberized hydrophobic root barriers governing apoplastic water and nitrogen movement into the vascular system. We also highlight the need to identify the signalling cascades regulating water and nitrogen transport, as well as the need for targeted physiological analyses of plant traits influencing water and nitrogen uptake. We further advocate for incorporation of new phenotyping technologies, breeding strategies, and agronomic practices to improve crop yield in water- and nitrogen-limited production systems., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Experimental Biology.)

Background: X-ray computed tomography (CT) allows us to visualize root system architecture (RSA) beneath the soil, non-destructively and in a three-dimensional (3-D) form. However, CT scanning, reconstruction processes, and root isolation from X-ray CT volumes, take considerable time. For genetic analyses, such as quantitative trait locus mapping, which require a large population size, a high-throughput RSA visualization method is required., Results: We have developed a high-throughput process flow for the 3-D visualization of rice ( Oryza sativa ) RSA (consisting of radicle and crown roots), using X-ray CT. The process flow includes use of a uniform particle size, calcined clay to reduce the possibility of visualizing non-root segments, use of a higher tube voltage and current in the X-ray CT scanning to increase root-to-soil contrast, and use of a 3-D median filter and edge detection algorithm to isolate root segments. Using high-performance computing technology, this analysis flow requires only 10 min (33 s, if a rough image is acceptable) for CT scanning and reconstruction, and 2 min for image processing, to visualize rice RSA. This reduced time allowed us to conduct the genetic analysis associated with 3-D RSA phenotyping. In 2-week-old seedlings, 85% and 100% of radicle and crown roots were detected, when 16 cm and 20 cm diameter pots were used, respectively. The X-ray dose per scan was estimated at < 0.09 Gy, which did not impede rice growth. Using the developed process flow, we were able to follow daily RSA development, i.e., 4-D RSA development, of an upland rice variety, over 3 weeks., Conclusions: We developed a high-throughput process flow for 3-D rice RSA visualization by X-ray CT. The X-ray dose assay on plant growth has shown that this methodology could be applicable for 4-D RSA phenotyping. We named the RSA visualization method 'RSAvis3D' and are confident that it represents a potentially efficient application for 3-D RSA phenotyping of various plant species., Competing Interests: Competing interestsThe authors declare that they have no competing interests., (© The Author(s) 2020.)

IR64 is a rice variety with high-yield that has been widely cultivated around the world. IR64 has been replaced by modern varieties in most growing areas. Given that modern varieties are mostly progenies or relatives of IR64, genetic analysis of IR64 is valuable for rice functional genomics. However, chromosome-level genome sequences of IR64 have not been available previously. Here, we sequenced the IR64 genome using synthetic long reads obtained by linked-read sequencing and ultra-long reads obtained by nanopore sequencing. We integrated these data and generated the de novo assembly of the IR64 genome of 367 Mb, equivalent to 99% of the estimated size. Continuity of the IR64 genome assembly was improved compared with that of a publicly available IR64 genome assembly generated by short reads only. We annotated 41,458 protein-coding genes, including 657 IR64-specific genes, that are missing in other high-quality rice genome assemblies IRGSP-1.0 of japonica cultivar Nipponbare or R498 of indica cultivar Shuhui498. The IR64 genome assembly will serve as a genome resource for rice functional genomics as well as genomics-driven and/or molecular breeding., (Copyright © 2020 Tanaka et al.)

Root system architecture (RSA) is one of the most important traits determining water and nutrient availability for plants. Modification of RSA is known to be a useful approach for improving root performance of crops. However, for conducting root phenotyping, there are few alternatives for the rapid collection of root samples from a constant soil volume. In this report, we propose a rapid root-sampling method, which uses a steel cylinder known as round monolith and backhoes to reduce the physical effort. The monolith was set on the ground surrounding individual rice plants and vertically driven back by a backhoe. Soil samples with 20 cm width and 25 cm depth were excavated by the monolith, from which root samples were then isolated. This backhoe-assisted monolith method requires at most five minutes to collect root samples from one plant. Using this method, we quantified the root traits of three rice lines, reported to form different types of root system such as shallow-, intermediate-, and deep-roots, using a root image analysis software. The data obtained through this method, which showed the same trend as previously reported, clearly demonstrated that this method is useful for quantitative evaluation of roots in the soil., (Copyright © 2019 by JAPANESE SOCIETY OF BREEDING.)

Background: Genetic improvement of root system architecture is a promising approach for improved uptake of water and mineral nutrients distributed unevenly in the soil. To identify genomic regions associated with the length of different root types in rice, we quantified root system architecture in a set of 26 chromosome segment substitution lines derived from a cross between lowland indica rice, IR64, and upland tropical japonica rice, Kinandang Patong, (IK-CSSLs), using 2D & 3D root phenotyping platforms., Results: Lengths of seminal and crown roots in the IK-CSSLs grown under hydroponic conditions were measured by 2D image analysis (RootReader2D). Twelve CSSLs showed significantly longer seminal root length than the recurrent parent IR64. Of these, 8 CSSLs also exhibited longer total length of the three longest crown roots compared to IR64. Three-dimensional image analysis (RootReader3D) for these CSSLs grown in gellan gum revealed that only one CSSL, SL1003, showed significantly longer total root length than IR64. To characterize the root morphology of SL1003 under soil conditions, SL1003 was grown in Turface, a soil-like growth media, and roots were quantified using RootReader3D. SL1003 had larger total root length and increased total crown root length than did IR64, although its seminal root length was similar to that of IR64. The larger TRL in SL1003 may be due to increased crown root length., Conclusions: SL1003 carries an introgression from Kinandang Patong on the long arm of chromosome 1 in the genetic background of IR64. We conclude that this region harbors a QTL controlling crown root elongation.

The volume that the root system can occupy is associated with the efficiency of water and nutrient uptake from soil. Genetic improvement of root length, which is a limiting factor for root distribution, is necessary for increasing crop production. In this report, we describe identification of two quantitative trait loci (QTLs) for maximal root length, QUICK ROOTING 1 ( QRO1 ) on chromosome 2 and QRO2 on chromosome 6, in cultivated rice ( Oryza sativa L.). We measured the maximal root length in 26 lines carrying chromosome segments from the long-rooted upland rice cultivar Kinandang Patong in the genetic background of the short-rooted lowland cultivar IR64. Five lines had longer roots than IR64. By rough mapping of the target regions in BC 4 F 2 populations, we detected putative QTLs for maximal root length on chromosomes 2, 6, and 8. To fine-map these QTLs, we used BC 4 F 3 recombinant homozygous lines. QRO1 was mapped between markers RM5651 and RM6107, which delimit a 1.7-Mb interval on chromosome 2, and QRO2 was mapped between markers RM20495 and RM3430-1, which delimit an 884-kb interval on chromosome 6. Both QTLs may be promising gene resources for improving root system architecture in rice., (Copyright © 2018 Kitomi et al.)

Abiotic stresses such as, drought, heat, salinity, and flooding threaten global food security. Crop genetic improvement with increased resilience to abiotic stresses is a critical component of crop breeding strategies. Wheat is an important cereal crop and a staple food source globally. Enhanced drought tolerance in wheat is critical for sustainable food production and global food security. Recent advances in drought tolerance research have uncovered many key genes and transcription regulators governing morpho-physiological traits. Genes controlling root architecture and stomatal development play an important role in soil moisture extraction and its retention, and therefore have been targets of molecular breeding strategies for improving drought tolerance. In this systematic review, we have summarized evidence of beneficial contributions of root and stomatal traits to plant adaptation to drought stress. Specifically, we discuss a few key genes such as, DRO1 in rice and ERECTA in Arabidopsis and rice that were identified to be the enhancers of drought tolerance via regulation of root traits and transpiration efficiency. Additionally, we highlight several transcription factor families, such as, ERF (ethylene response factors), DREB (dehydration responsive element binding), ZFP (zinc finger proteins), WRKY, and MYB that were identified to be both positive and negative regulators of drought responses in wheat, rice, maize, and/or Arabidopsis. The overall aim of this review is to provide an overview of candidate genes that have been identified as regulators of drought response in plants. The lack of a reference genome sequence for wheat and non-transgenic approaches for manipulation of gene functions in wheat in the past had impeded high-resolution interrogation of functional elements, including genes and QTLs, and their application in cultivar improvement. The recent developments in wheat genomics and reverse genetics, including the availability of a gold-standard reference genome sequence and advent of genome editing technologies, are expected to aid in deciphering of the functional roles of genes and regulatory networks underlying adaptive phenological traits, and utilizing the outcomes of such studies in developing drought tolerant cultivars.

We developed a new method of using seedling trays to evaluate root angle distribution in rice ( Oryza sativa . L), and found a wide genetic variation among cultivars. The seedling tray method can be used to evaluate in detail the growth angles of rice crown roots at the seedling stage by allocating nine scores (10° to 90°). Unlike basket methods, it can handle large plant populations over a short growth period (only 14 days). By using the method, we characterized the root angle distributions of 97 accessions into two cluster groups: A and B. The numbers of accessions in group A were limited, and these were categorized as shallow rooting types including soil-surface root. Group B included from shallow to deep rooting types; both included Indica and Japonica Group cultivars, lowland and upland cultivars, and landraces and improved types. No relationship between variation in root vertical angle and total root number was found. The variation in root angle distribution was not related to differentiation between the Japonica and Indica Groups, among ecosystems used for rice cultivation, or among degrees of genetic improvement. The new evaluation method and associated information on genetic variation of rice accessions will be useful in root architecture breeding of rice.

Background: Heading date, a crucial factor determining regional and seasonal adaptation in rice (Oryza sativa L.), has been a major selection target in breeding programs. Although considerable progress has been made in our understanding of the molecular regulation of heading date in rice during last two decades, the previously isolated genes and identified quantitative trait loci (QTLs) cannot fully explain the natural variation for heading date in diverse rice accessions., Results: To genetically dissect naturally occurring variation in rice heading date, we collected QTLs in advanced-backcross populations derived from multiple crosses of the japonica rice accession Koshihikari (as a common parental line) with 11 diverse rice accessions (5 indica, 3 aus, and 3 japonica) that originate from various regions of Asia. QTL analyses of over 14,000 backcrossed individuals revealed 255 QTLs distributed widely across the rice genome. Among the detected QTLs, 128 QTLs corresponded to genomic positions of heading date genes identified by previous studies, such as Hd1, Hd6, Hd3a, Ghd7, DTH8, and RFT1. The other 127 QTLs were detected in different chromosomal regions than heading date genes., Conclusions: Our results indicate that advanced-backcross progeny allowed us to detect and confirm QTLs with relatively small additive effects, and the natural variation in rice heading date could result from combinations of large- and small-effect QTLs. We also found differences in the genetic architecture of heading date (flowering time) among maize, Arabidopsis, and rice.

Shape is an important morphological characteristic both in animals and plants. In the present study, we examined a method for predicting biological contour shapes based on genome-wide marker polymorphisms. The method is expected to contribute to the acceleration of genetic improvement of biological shape via genomic selection. Grain shape variation observed in rice (Oryza sativa L.) germplasms was delineated using elliptic Fourier descriptors (EFDs), and was predicted based on genome-wide single nucleotide polymorphism (SNP) genotypes. We applied four methods including kernel PLS (KPLS) regression for building a prediction model of grain shape, and compared the accuracy of the methods via cross-validation. We analyzed multiple datasets that differed in marker density and sample size. Datasets with larger sample size and higher marker density showed higher accuracy. Among the four methods, KPLS showed the highest accuracy. Although KPLS and ridge regression (RR) had equivalent accuracy in a single dataset, the result suggested the potential of KPLS for the prediction of high-dimensional EFDs. Ordinary PLS, however, was less accurate than RR in all datasets, suggesting that the use of a non-linear kernel was necessary for accurate prediction using the PLS method. Rice grain shape can be predicted accurately based on genome-wide SNP genotypes. The proposed method is expected to be useful for genomic selection in biological shape.

Background: The functional allele of the rice gene DEEPER ROOTING 1 (DRO1) increases the root growth angle (RGA). However, wide natural variation in RGA is observed among rice cultivars with the functional DRO1 allele. To elucidate genetic factors related to such variation, we quantitatively measured RGA using the basket method and analyzed quantitative trait loci (QTLs) for RGA in three F2 mapping populations derived from crosses between the large RGA-type cultivar Kinandang Patong and each of three accessions with varying RGA: Momiroman has small RGA and was used to produce the MoK-F2 population; Yumeaoba has intermediate RGA (YuK-F2 population); Tachisugata has large RGA (TaK-F2 population). All four accessions belong to the same haplotype group of functional DRO1 allele., Results: We detected the following statistically significant QTLs: one QTL on chromosome 4 in MoK-F2, three QTLs on chromosomes 2, 4, and 6 in YuK-F2, and one QTL on chromosome 2 in TaK-F2. Among them, the two QTLs on chromosome 4 were located near DRO2, which has been previously reported as a major QTL for RGA, whereas the two major QTLs for RGA on chromosomes 2 (DRO4) and 6 (DRO5) were novel. With the LOD threshold reduced to 3.0, several minor QTLs for RGA were also detected in each population., Conclusion: Natural variation in RGA in rice cultivars carrying functional DRO1 alleles may be controlled by a few major QTLs and by several additional minor QTLs.

The root system is an essential organ for taking up water and nutrients and anchoring shoots to the ground. On the other hand, the root system has rarely been regarded as breeding target, possibly because it is more laborious and time-consuming to evaluate roots (which require excavation) in a large number of plants than aboveground tissues. The root growth angle (RGA), which determines the direction of root elongation in the soil, affects the area in which roots capture water and nutrients. In this review, we describe the significance of RGA as a potential trait to improve crop production, and the physiological and molecular mechanisms that regulate RGA. We discuss the prospects for breeding to improve RGA based on current knowledge of quantitative trait loci for RGA in rice.