Characterization of quantitative trait loci from DJ123 (aus) independently affecting panicle structure traits in indica rice cultivar IR64.

The rice panicle is the principal organ to influence productivity and traits affecting panicle architecture determine sink size and yield potential. Improving panicle architecture may be effective in increasing yield under low-input conditions, but which traits are of importance under such conditions and how they are genetically controlled is not well understood. Using recombinant inbred lines (RILs) derived from a cross between a modern variety IR64 and a low fertility tolerant accession DJ123, quantitative trait locus (QTL) mapping was conducted under high soil fertility in Japan and low fertility in Madagascar. Among QTL for panicle length (PL) detected, the DJ123 allele increased rachis length at qCL1 and qPL9, while the IR64 allele increased primary branch length at qPL7. DJ123 further contributed two QTL for grain width whereas IR64 contributed two grain length QTL. Analysis of lines carrying different combinations of detected QTL indicates that rachis and primary branch lengths are independently regulated, explaining strong transgressive segregation for PL. The positive effects of PL-related QTL were further confirmed by a genome-wide analysis of allelic states in two breeding lines that had been selected repeatedly for total panicle weight per plant under low input conditions. This study provides the genetic basis for complex panicle architecture in rice and will aid in designing an ideal panicle architecture that leads to increased yield under low fertility conditions. Supplementary information: The online version contains supplementary material available at 10.1007/s11032-024-01494-5.

High-Throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models.

High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of 5 conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.

Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean.

KEY MESSAGE: We proposed models to predict the effects of genomic and environmental factors on daily soybean growth and applied them to soybean growth data obtained with unmanned aerial vehicles. Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G × E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G × E and has been implemented in genomic prediction models, no reaction norm models have been applied to plant growth data. Here, we propose a novel reaction norm model for plant growth using spline and random forest models, in which daily growth is explained by environmental factors one day prior. The proposed model was applied to soybean canopy area and height to evaluate the influence of drought stress levels. Changes in the canopy area and height of 198 cultivars were measured by remote sensing using unmanned aerial vehicles. Multiple drought stress levels were set as treatments, and their time-series soil moisture was measured. The models were evaluated using three cross-validation schemes. Although accuracy of the proposed models did not surpass that of single-trait genomic prediction, the results suggest that our model can capture G × E, especially the latter growth period for the random forest model. Also, significant variations in the G × E of the canopy height during the early growth period were visualized using the spline model. This result indicates the effectiveness of the proposed models on plant growth data and the possibility of revealing G × E in various growth stages in plant breeding by applying statistical or machine learning models to time-series phenotype data.

Genetic Dissection of Seasonal Changes in a Greening Plant Based on Time-Series Multispectral Imaging.

Good appearance throughout the year is important for perennial ornamental plants used for rooftop greenery. However, the methods for evaluating appearance throughout the year, such as plant color and growth activity, are not well understood. In this study, evergreen and winter-dormant parents of Phedimus takesimensis and 94 F1 plants were used for multispectral imaging. We took 16 multispectral image measurements from March 2019 to April 2020 and used them to calculate 15 vegetation indices and the area of plant cover. QTL analysis was also performed. Traits such as the area of plant cover and vegetation indices related to biomass were high during spring and summer (growth period), whereas vegetation indices related to anthocyanins were high in winter (dormancy period). According to the PCA, changes in the intensity of light reflected from the plants at different wavelengths over the course of a year were consistent with the changes in plant color and growth activity. Seven QTLs were found to be associated with major seasonal growth changes. This approach, which monitors not only at a single point in time but also over time, can reveal morphological changes during growth, senescence, and dormancy throughout the year.

Novel QTL for Lateral Root Density and Length Improve Phosphorus Uptake in Rice (Oryza sativa L.).

The rice root system consists of two types of lateral roots, indeterminate larger L-types capable of further branching, and determinate, short, unbranched S-types. L-type laterals correspond to the typical lateral roots of cereals whereas S-type laterals are unique to rice. Both types contribute to nutrient and water uptake and genotypic variation for density and length of these laterals could be exploited in rice improvement to enhance adaptations to nutrient and water-limited environments. Our objectives were to determine how best to screen for lateral root density and length and to identify markers linked to genotypic variation for these traits. Using different growing media showed that screening in nutrient solution exposed genotypic variation for S-type and L-type density, but only the lateral roots of soil-grown plants varied for their lengths. A QTL mapping population developed from parents contrasting for lateral root traits was grown in a low-P field, roots were sampled, scanned and density and length of lateral roots measured. One QTL each was detected for L-type density (LDC), S-type density on crown root (SDC), S-type density on L-type (SDL), S-type length on L-type (SLL), and crown root number (RNO). The QTL for LDC on chromosome 5 had a major effect, accounting for 46% of the phenotypic variation. This strong positive effect was confirmed in additional field experiments, showing that lines with the donor parent allele at qLDC5 had 50% higher LDC. Investigating the contribution of lateral root traits to P uptake using stepwise regressions indicated LDC and RNO were most influential, followed by SDL. Simulating effects of root trait differences conferred by the main QTL in a P uptake model confirmed that qLDC5 was most effective in improving P uptake followed by qRNO9 for RNO and qSDL9 for S-type lateral density on L-type laterals. Pyramiding qLDC5 with qRNO9 and qSDL9 would be possible given that trade-offs between traits were not detected. Phenotypic selection for the RNO trait during variety development would be feasible, however, the costs of doing so reliably for lateral root density traits is prohibitive and markers identified here therefore provide the first opportunity to incorporate such traits into a breeding program.

Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data.

Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (Glycine max (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant's stability under drought stresses. We built a genomic prediction model (MTRRM model) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In 2020 and 2021,the mean prediction accuracies of MTRRM models built in the changing irrigation treatments (r = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (r = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the MTRRM model across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (r = 0.46) was 42% higher than that of the simple genomic prediction model (r =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought.

Multispectral Phenotyping and Genetic Analyses of Spring Appearance in Greening Plant, Phedimus spp.

The change in appearance during the seasonal transitions in ornamental greening plants is an important characteristic. In particular, the early onset of green leaf color is a desirable trait for a cultivar. In this study, we established a method for phenotyping leaf color change by multispectral imaging and performed genetic analysis based on the phenotypes to clarify the potential of the approach in breeding greening plants. We performed multispectral phenotyping and quantitative trait locus (QTL) analysis of an F1 population derived from 2 parental lines of Phedimus takesimensis, known to be a drought and heat-tolerant rooftop plant species. The imaging was conducted in April of 2019 and 2020 when dormancy breakage occurs and growth extension begins. Principal component analysis of 9 different wavelength values showed a high contribution from the first principal component (PC1), which captured variation in the visible light range. The high interannual correlation in PC1 and in the intensity of visible light indicated that the multispectral phenotyping captured genetic variation in the color of leaves. We also performed restriction site-associated DNA sequencing and obtained the first genetic linkage map of Phedimus spp. QTL analysis revealed 2 QTLs related to early dormancy breakage. Based on the genotypes of the markers underlying these 2 QTLs, the F1 phenotypes with early (late) dormancy break, green (red or brown) leaves, and a high (low) degree of vegetative growth were classified. The results suggest the potential of multispectral phenotyping in the genetic dissection of seasonal leaf color changes in greening plants.

Genetic control of morphological traits useful for improving sorghum.

Global climate change and global warming, coupled with the growing population, have raised concerns about sustainable food supply and bioenergy demand. Sorghum [Sorghum bicolor (L.) Moench] ranks fifth among cereals produced worldwide; it is a C4 crop with a higher stress tolerance than other major cereals and has a wide range of uses, such as grains, forage, and biomass. Therefore, sorghum has attracted attention as a promising crop for achieving sustainable development goals (SDGs). In addition, sorghum is a suitable genetic model for C4 grasses because of its high morphological diversity and relatively small genome size compared to other C4 grasses. Although sorghum breeding and genetic studies have lagged compared to other crops such as rice and maize, recent advances in research have identified several genes and many quantitative trait loci (QTLs) that control important agronomic traits in sorghum. This review outlines traits and genetic information with a focus on morphogenetic aspects that may be useful in sorghum breeding for grain and biomass utilization.

QTL mapping for early root and shoot vigor of upland rice (Oryza sativa L.) under P deficient field conditions in Japan and Madagascar.

Upland rice production is limited by the low phosphorus (P) availability of many highly weathered tropical soils and P deficiency is likely to become increasingly limiting in future drier climates because P mobility decreases sharply with soil moisture. Good seedling root development will be crucial to cope with the combined effects of low P and water availability. Upland rice genebank accession DJ123 was used as a donor for P efficiency and root vigor traits in a cross with inefficient local variety Nerica4 and a set of backcross lines were used to characterize the seedling stage response of upland rice to low P availability and to identify associated QTL in field trials in Japan and Madagascar. Ten QTL were detected for crown root number, root, shoot and total dry weight per plant in a highly P deficient field in Japan using the BC1F3 generation. Of these, qPef9 on chromosome 9 affected multiple traits, increasing root number, root weight and total biomass, whereas a neighboring QTL on chromosome 9 (qPef9-2) increased shoot biomass. Field trials with derived BC1F5 lines in a low-P field in Madagascar confirmed a highly influential region on chromosome 9. However, qPef9-2 appeared more influential than qPef9, as the shoot and root biomass contrast between lines carrying DJ123 or Nerica4 alleles at qPef9-2 was +23.8% and +13.5% compared to +19.2% and +14.4% at qPef9. This advantage increased further during the growing season, leading to 46% higher shoot biomass at the late vegetative stage. Results suggest an introgression between 8.0 and 12.9 Mb on chromosome 9 from P efficient donor DJ123 can improve plant performance under P-limited conditions. The QTL identified here have practical relevance because they were confirmed in the target genetic background of the local variety Nerica4 and can therefore be applied directly to improve its performance.

A Wolbachia factor for male killing in lepidopteran insects.

Bacterial symbionts, such as Wolbachia species, can manipulate the sexual development and reproduction of their insect hosts. For example, Wolbachia infection induces male-specific death in the Asian corn borer Ostrinia furnacalis by targeting the host factor Masculinizer (Masc), an essential protein for masculinization and dosage compensation in lepidopteran insects. Here we identify a Wolbachia protein, designated Oscar, which interacts with Masc via its ankyrin repeats. Embryonic expression of Oscar inhibits Masc-induced masculinization and leads to male killing in two lepidopteran insects, O. furnacalis and the silkworm Bombyx mori. Our study identifies a mechanism by which Wolbachia induce male killing of host progeny.

High throughput method of 16S rRNA gene sequencing library preparation for plant root microbial community profiling.

Microbiota are a major component of agroecosystems. Root microbiota, which inhabit the inside and surface of plant roots, play a significant role in plant growth and health. As next-generation sequencing technology allows the capture of microbial profiles without culturing the microbes, profiling of plant microbiota has become a staple tool in plant science and agriculture. Here, we have increased sample handling efficiency in a two-step PCR amplification protocol for 16S rRNA gene sequencing of plant root microbiota, improving DNA extraction using AMPure XP magnetic beads and PCR purification using exonuclease. These modifications reduce sample handling and capture microbial diversity comparable to that obtained by the manual method. We found a buffer with AMPure XP magnetic beads enabled efficient extraction of microbial DNA directly from plant roots. We also demonstrated that purification using exonuclease before the second PCR step enabled the capture of higher degrees of microbial diversity, thus allowing for the detection of minor bacteria compared with the purification using magnetic beads in this step. In addition, our method generated comparable microbiome profile data in plant roots and soils to that of using common commercially available DNA extraction kits, such as DNeasy PowerSoil Pro Kit and FastDNA SPIN Kit for Soil. Our method offers a simple and high-throughput option for maintaining the quality of plant root microbial community profiling.

Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy.

Multispectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of aboveground biomass (AGB) in soybean [Glycine max (L.) Merr.]. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early vegetative to early reproductive stage. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multitrait genomic prediction. The prediction accuracy of the genotypic values of AGB from MS and genomic data largely outperformed that of the genomic data alone before the flowering stage (90% of accessions did not flower), suggesting that it would be possible to determine cross-combinations based on the predicted genotypic values of AGB. We compared the prediction accuracy of a model using the five VIs and a model using only one VI to predict the phenotypic values of AGB and found that the difference in prediction accuracy decreased over time at all irrigation levels except for the most severe drought. The difference in the most severe drought was not as small as that in the other treatments. Only the prediction accuracy of a model using the five VIs in the most severe droughts gradually increased over time. Therefore, the optimal timing for MS imaging may depend on the irrigation levels.

DOMINANT AWN INHIBITOR Encodes the ALOG Protein Originating from Gene Duplication and Inhibits AWN Elongation by Suppressing Cell Proliferation and Elongation in Sorghum.

The awn, a needle-like structure extending from the tip of the lemma in grass species, plays a role in environmental adaptation and fitness. In some crops, awns appear to have been eliminated during domestication. Although numerous genes involved in awn development have been identified, several dominant genes that eliminate awns are also known to exist. For example, in sorghum (Sorghum bicolor), the dominant awn-inhibiting gene has been known since 1921; however, its molecular features remain uncharacterized. In this study, we conducted quantitative trait locus analysis and a genome-wide association study of awn-related traits in sorghum and identified DOMINANT AWN INHIBITOR (DAI), which encodes the ALOG family protein on chromosome 3. DAI appeared to be present in most awnless sorghum cultivars, likely because of its effectiveness. Detailed analysis of the ALOG protein family in cereals revealed that DAI originated from a duplication of its twin paralog (DAIori) on chromosome 10. Observations of immature awns in near-isogenic lines revealed that DAI inhibits awn elongation by suppressing both cell proliferation and elongation. We also found that only DAI gained a novel function to inhibit awn elongation through an awn-specific expression pattern distinct from that of DAIori. Interestingly, heterologous expression of DAI with its own promoter in rice inhibited awn elongation in the awned cultivar Kasalath. We found that DAI originated from gene duplication, providing an interesting example of gain-of-function that occurs only in sorghum but shares its functionality with rice and sorghum.

Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations.

With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.

Sorghum Ionomics Reveals the Functional SbHMA3a Allele that Limits Excess Cadmium Accumulation in Grains.

Understanding uptake and redistribution of essential minerals or sequestering of toxic elements is important for optimized crop production. Although the mechanisms controlling mineral transport have been elucidated in rice and other species, little is understood in sorghum-an important C4 cereal crop. Here, we assessed the genetic factors that govern grain ionome profiles in sorghum using recombinant inbred lines (RILs) derived from a cross between BTx623 and NOG (Takakibi). Pairwise correlation and clustering analysis of 22 elements, measured in sorghum grains harvested under greenhouse conditions, indicated that the parental lines, as well as the RILs, show different ionomes. In particular, BTx623 accumulated significantly higher levels of cadmium (Cd) than NOG, because of differential root-to-shoot translocation factors between the two lines. Quantitative trait locus (QTL) analysis revealed a prominent QTL for grain Cd concentration on chromosome 2. Detailed analysis identified SbHMA3a, encoding a P1B-type ATPase heavy metal transporter, as responsible for low Cd accumulation in grains; the NOG allele encoded a functional HMA3 transporter (SbHMA3a-NOG) whose Cd-transporting activity was confirmed by heterologous expression in yeast. BTx623 possessed a truncated, loss-of-function SbHMA3a allele. The functionality of SbHMA3a in NOG was confirmed by Cd concentrations of F2 grains derived from the reciprocal cross, in which the NOG allele behaved in a dominant manner. We concluded that SbHMA3a-NOG is a Cd transporter that sequesters excess Cd in root tissues, as shown in other HMA3s. Our findings will facilitate the isolation of breeding cultivars with low Cd in grains or in exploiting high-Cd cultivars for phytoremediation.

Corrigendum: QTL mapping for early root and shoot vigor of upland rice (Oryza sativa L.) under P deficient field conditions in Japan and Madagascar.

[This corrects the article DOI: 10.3389/fpls.2022.1017419.].

Spatial kernel models capturing field heterogeneity for accurate estimation of genetic potential.

According to Fisher's principles, an experimental field is typically divided into multiple blocks for local control. Although homogeneity is supposed within a block, this assumption may not be practical for large blocks, such as those including hundreds of plots. In line evaluation trials, which are essential in plant breeding, field heterogeneity must be carefully treated, because it can cause bias in the estimation of genetic potential. To more accurately estimate genotypic values in a large field trial, we developed spatial kernel models incorporating genome-wide markers, which consider continuous heterogeneity within a block and over the field. In the simulation study, the spatial kernel models were robust under various conditions. Although heritability, spatial autocorrelation range, replication number, and missing plots directly affected the estimation accuracy of genotypic values, the spatial kernel models always showed superior performance over the classical block model. We also employed these spatial kernel models for quantitative trait locus mapping. Finally, using field experimental data of bioenergy sorghum lines, we validated the performance of the spatial kernel models. The results suggested that a spatial kernel model is effective for evaluating the genetic potential of lines in a heterogeneous field.

NB-LRR-encoding genes conferring susceptibility to organophosphate pesticides in sorghum.

Organophosphate is the commonly used pesticide to control pest outbreak, such as those by aphids in many crops. Despite its wide use, however, necrotic lesion and/or cell death following the application of organophosphate pesticides has been reported to occur in several species. To understand this phenomenon, called organophosphate pesticide sensitivity (OPS) in sorghum, we conducted QTL analysis in a recombinant inbred line derived from the Japanese cultivar NOG, which exhibits OPS. Mapping OPS in this population identified a prominent QTL on chromosome 5, which corresponded to Organophosphate-Sensitive Reaction (OSR) reported previously in other mapping populations. The OSR locus included a cluster of three genes potentially encoding nucleotide-binding leucine-rich repeat (NB-LRR, NLR) proteins, among which NLR-C was considered to be responsible for OPS in a dominant fashion. NLR-C was functional in NOG, whereas the other resistant parent, BTx623, had a null mutation caused by the deletion of promoter sequences. Our finding of OSR as a dominant trait is important not only in understanding the diversified role of NB-LRR proteins in cereals but also in securing sorghum breeding free from OPS.

Targeted base editing in the plastid genome of Arabidopsis thaliana.

Bacterial cytidine deaminase fused to the DNA binding domains of transcription activator-like effector nucleases was recently reported to transiently substitute a targeted C to a T in mitochondrial DNA of mammalian cultured cells1. We applied this system to targeted base editing in the Arabidopsis thaliana plastid genome. The targeted Cs were homoplasmically substituted to Ts in some plantlets of the T1 generation and the mutations were inherited by their offspring independently of their nuclear-introduced vectors.

Genetic dissection of QTLs associated with spikelet-related traits and grain size in sorghum.

Although spikelet-related traits such as size of anther, spikelet, style, and stigma are associated with sexual reproduction in grasses, no QTLs have been reported in sorghum. Additionally, there are only a few reports on sorghum QTLs related to grain size, such as grain length, width, and thickness. In this study, we performed QTL analyses of nine spikelet-related traits (length of sessile spikelet, pedicellate spikelet, pedicel, anther, style, and stigma; width of sessile spikelet and stigma; and stigma pigmentation) and six grain-related traits (length, width, thickness, length/width ratio, length/thickness ratio, and width/thickness ratio) using sorghum recombinant inbred lines. We identified 36 and 7 QTLs for spikelet-related traits and grain-related traits, respectively, and found that most sorghum spikelet organ length- and width-related traits were partially controlled by the dwarf genes Dw1 and Dw3. Conversely, we found that these Dw genes were not strongly involved in the regulation of grain size. The QTLs identified in this study aid in understanding the genetic basis of spikelet- and grain-related traits in sorghum.