Three-dimensional image analysis specifies the root distribution for drought avoidance in the early growth stage of rice.

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, as 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 (3D) image analysis. METHODS: We used 109 F12 recombinant inbred lines (RILs) 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 height) by stopping irrigation 14 days after sowing (DAS). Time-series RSA at 14, 21, and 28 DAS was visualized by X-ray computed tomography, and subsequently compared between drought and well-watered conditions. Following this analysis, we further investigated drought-avoidant RSA by testing 20 randomly selected RILs under drought conditions. KEY RESULTS: We inferred the root location that most influences shoot growth using a hierarchical Bayes approach: the root segment depth, which positively impacted shoot growth, ranged between 1.7-3.4 cm under drought conditions and between 0.0-1.7 cm under well-watered conditions. Drought-avoidant RILs had a higher root density in the lower layers of the topsoil compared to the others. CONCLUSIONS: Fine classification of soil layers using 3D 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.

Convolutional neural networks combined with conventional filtering to semantically segment plant roots in rapidly scanned X-ray computed tomography volumes with high noise levels.

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.

Non-destructive real-time monitoring of underground root development with distributed fiber optic sensing.

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.

Life-Cycle Multiomics of Rice Shoots Reveals Growth Stage-Specific Effects of Drought Stress and Time-Lag Drought Responses.

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.

Genome- and Transcriptome-wide Association Studies to Discover Candidate Genes for Diverse Root Phenotypes in Cultivated Rice.

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.

Development of 12 sets of chromosome segment substitution lines that enhance allele mining in Asian cultivated rice.

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.

Constitutively active B2 Raf-like kinases are required for drought-responsive gene expression upstream of ABA-activated SnRK2 kinases.

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.

Transcriptome profiles of rice roots under simulated microgravity conditions and following gravistimulation.

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 (HSFA2s) and HSFB2s, showed the similar expression patterns with the HSPs. 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 HSPs 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 HSPs in rice roots.

MORE PANICLES 3, a natural allele of OsTB1/FC1, impacts rice yield in paddy fields at elevated CO2 levels.

Improving crop yield potential through an enhanced response to rising atmospheric CO2 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 CO2 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 CO2 enrichment experiment revealed a clear increase of grain yield associated with the temperate japonica allele at elevated atmospheric CO2 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 CO2 levels.

Four-dimensional measurement of root system development using time-series three-dimensional volumetric data analysis by backward prediction.

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.

Identification of a unique allele in the quantitative trait locus for crown root number in japonica rice from Japan using genome-wide association studies.

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.

Improving the efficiency of plant root system phenotyping through digitization and automation.

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.

Quantification of Soil-surface Roots in Seedlings and Mature Rice Plants.

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.

Rice immediately adapts the dynamics of photosynthates translocation to roots in response to changes in soil water environment.

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 11C tracer. The short half-life of 11C (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, 11C-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 11C-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.

RSAtrace3D: robust vectorization software for measuring monocot root system architecture.

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.

iPOTs: Internet of Things-based pot system controlling optional treatment of soil water condition for plant phenotyping under drought stress.

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.

Synergy between a shallow root system with a DRO1 homologue and localized P application improves P uptake of lowland rice.

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.

The transcriptomic landscapes of rice cultivars with diverse root system architectures grown in upland field conditions.

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.