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.

Genomic analysis for heat and combined heat-drought resilience in bread wheat under field conditions.

KEY MESSAGE: GWAS on a bread wheat panel with high D genome diversity identified novel alleles and QTLs associated with resilience to combined heat and drought stress under natural field conditions. As heat (H) and drought stresses occur concurrently under field conditions, studying them separately offers limited opportunities for wheat improvement. Here, a wheat diversity panel containing Aegilops tauschii introgressions was evaluated under H and combined heat-drought (HD) stresses to identify quantitative trait loci (QTLs) associated with resilience to the stresses, and to assess the practicability of harnessing Ae. tauschii diversity for breeding for combined stress resilience. Using genome-wide analysis, we identified alleles and QTLs on chromosomes 3D, 5D, and 7A controlling grain yield (GY), kernel number per spike, and thousand-kernel weight, and on 3D (521-549 Mbp) controlling GY alone. A strong marker-trait association (MTA) for GY stability on chromosome 3D (508.3 Mbp) explained 20.3% of the variation. Leaf traits-canopy temperature, vegetation index, and carbon isotope composition-were controlled by five QTLs on 2D (23-96, 511-554, and 606-614 Mbp), 3D (155-171 Mbp), and 5D (407-413 Mbp); some of them were pleiotropic for GY and yield-related traits. Further analysis revealed candidate genes, including GA20ox, regulating GY stability, and CaaX prenyl protease 2, regulating canopy temperature at the flowering stage, under H and HD stresses. As genome-wide association studies under HD in field conditions are scarce, our results provide genomic landmarks for wheat breeding to improve adaptation to H and HD conditions under climate change.

Chemical Fingerprinting of Heat Stress Responses in the Leaves of Common Wheat by Fourier Transform Infrared Spectroscopy.

Wheat (Triticum aestivum L.) is known to be negatively affected by heat stress, and its production is threatened by global warming, particularly in arid regions. Thus, efforts to better understand the molecular responses of wheat to heat stress are required. In the present study, Fourier transform infrared (FTIR) spectroscopy, coupled with chemometrics, was applied to develop a protocol that monitors chemical changes in common wheat under heat stress. Wheat plants at the three-leaf stage were subjected to heat stress at a 42 °C daily maximum temperature for 3 days, and this led to delayed growth in comparison to that of the control. Measurement of FTIR spectra and their principal component analysis showed partially overlapping features between heat-stressed and control leaves. In contrast, supervised machine learning through linear discriminant analysis (LDA) of the spectra demonstrated clear discrimination of heat-stressed leaves from the controls. Analysis of LDA loading suggested that several wavenumbers in the fingerprinting region (400-1800 cm-1) contributed significantly to their discrimination. Novel spectrum-based biomarkers were developed using these discriminative wavenumbers that enabled the successful diagnosis of heat-stressed leaves. Overall, these observations demonstrate the versatility of FTIR-based chemical fingerprints for use in heat-stress profiling in wheat.

Stage-Specific Characterization of Physiological Response to Heat Stress in the Wheat Cultivar Norin 61.

Bread wheat (Triticum aestivum) is less adaptable to high temperatures than other major cereals. Previous studies of the effects of high temperature on wheat focused on the reproductive stage. There are few reports on yield after high temperatures at other growth stages. Understanding growth-stage-specific responses to heat stress will contribute to the development of tolerant lines suited to high temperatures at various stages. We exposed wheat cultivar "Norin 61" to high temperature at three growth stages: seedling-tillering (GS1), tillering-flowering (GS2), and flowering-maturity (GS3). We compared each condition based on agronomical traits, seed maturity, and photosynthesis results. Heat at GS2 reduced plant height and number of grains, and heat at GS3 reduced the grain formation period and grain weight. However, heat at GS1 reduced senescence and prolonged grain formation, increasing grain weight without reducing yield. These data provide fundamental insights into the biochemical and molecular adaptations of bread wheat to high-temperature stresses and have implications for the development of wheat lines that can respond to high temperatures at various times of the year.

Metabolome Profiling of Heat Priming Effects, Senescence, and Acclimation of Bread Wheat Induced by High Temperatures at Different Growth Stages.

Our previous study described stage-specific responses of 'Norin 61' bread wheat to high temperatures from seedling to tillering (GS1), tillering to flowering (GS2), flowering to full maturity stage (GS3), and seedling to full maturity stage (GS1-3). The grain development phase lengthened in GS1 plants; source tissue decreased in GS2 plants; rapid senescence occurred in GS3 plants; all these effects occurred in GS1-3 plants. The present study quantified 69 flag leaf metabolites during early grain development to reveal the effects of stage-specific high-temperature stress and identify markers that predict grain weight. Heat stresses during GS2 and GS3 showed the largest shifts in metabolite contents compared with the control, followed by GS1-3 and GS1. The GS3 plants accumulated nucleosides related to the nucleotide salvage pathway, beta-alanine, and serotonin. Accumulation of these compounds in GS1 plants was significantly lower than in the control, suggesting that the reduction related to the high-temperature priming effect observed in the phenotype (i.e., inhibition of senescence). The GS2 plants accumulated a large quantity of free amino acids, indicating residual effects of the previous high-temperature treatment and recovery from stress. However, levels in GS1-3 plants tended to be close to those in the control, indicating an acclimation response. Beta-alanine, serotonin, tryptophan, proline, and putrescine are potential molecular markers that predict grain weight due to their correlation with agronomic traits.

Exploitation of Tolerance of Wheat Kernel Weight and Shape-Related Traits from Aegilops tauschii under Heat and Combined Heat-Drought Stresses.

Kernel weight and shape-related traits are inherited stably and increase wheat yield. Narrow genetic diversity limits the progress of wheat breeding. Here, we evaluated kernel weight and shape-related traits and applied genome-wide association analysis to a panel of wheat multiple synthetic derivative (MSD) lines. The MSD lines harbored genomic fragments from Aegilops tauschii. These materials were grown under optimum conditions in Japan, as well as under heat and combined heat-drought conditions in Sudan. We aimed to explore useful QTLs for kernel weight and shape-related traits under stress conditions. These can be useful for enhancing yield under stress conditions. MSD lines possessed remarkable genetic variation for all traits under all conditions, and some lines showed better performance than the background parent Norin 61. We identified 82 marker trait associations (MTAs) under the three conditions; most of them originated from the D genome. All of the favorable alleles originated from Ae. tauschii. For the first time, we identified markers on chromosome 5D associated with a candidate gene encoding a RING-type E3 ubiquitin-protein ligase and expected to have a role in regulating wheat seed size. Our study provides important knowledge for the improvement of wheat yield under optimum and stress conditions. The results emphasize the importance of Ae. tauschii as a gene reservoir for wheat breeding.

Seed maturation associated transcriptional programs and regulatory networks underlying genotypic difference in seed dormancy and size/weight in wheat (Triticum aestivum L.).

BACKGROUND: Maturation forms one of the critical seed developmental phases and it is characterized mainly by programmed cell death, dormancy and desiccation, however, the transcriptional programs and regulatory networks underlying acquisition of dormancy and deposition of storage reserves during the maturation phase of seed development are poorly understood in wheat. The present study performed comparative spatiotemporal transcriptomic analysis of seed maturation in two wheat genotypes with contrasting seed weight/size and dormancy phenotype. RESULTS: The embryo and endosperm tissues of maturing seeds appeared to exhibit genotype-specific temporal shifts in gene expression profile that might contribute to the seed phenotypic variations. Functional annotations of gene clusters suggest that the two tissues exhibit distinct but genotypically overlapping molecular functions. Motif enrichment predicts genotypically distinct abscisic acid (ABA) and gibberellin (GA) regulated transcriptional networks contribute to the contrasting seed weight/size and dormancy phenotypes between the two genotypes. While other ABA responsive element (ABRE) motifs are enriched in both genotypes, the prevalence of G-box-like motif specifically in tissues of the dormant genotype suggests distinct ABA mediated transcriptional mechanisms control the establishment of dormancy during seed maturation. In agreement with this, the bZIP transcription factors that co-express with ABRE enriched embryonic genes differ with genotype. The enrichment of SITEIIATCYTC motif specifically in embryo clusters of maturing seeds irrespective of genotype predicts a tissue specific role for the respective TCP transcription factors with no or minimal contribution to the variations in seed dormancy. CONCLUSION: The results of this study advance our understanding of the seed maturation associated molecular mechanisms underlying variation in dormancy and weight/size in wheat seeds, which is a critical step towards the designing of molecular strategies for enhancing seed yield and quality.

Characterization of a mutation that results in independence of oxidosqualene cyclase (Erg7) activity from the downstream 3-ketoreductase (Erg27) in the yeast ergosterol biosynthetic pathway.

In yeast, deletion of ERG27, which encodes the sterol biosynthetic enzyme, 3-keto-reductase, results in a concomitant loss of the upstream enzyme, Erg7p, an oxidosqualene cyclase (OSC). However, this phenomenon occurs only in fungi, as mammalian Erg27p orthologues are unable to rescue yeast Erg7p activity. In this study, an erg27 mutant containing the mouse ERG27 orthologue was isolated that was capable of growing without sterol supplementation (FGerg27). GC/MS analysis of this strain showed an accumulation of squalene epoxides, 3-ketosterones, and ergosterol. This strain which was crossed to a wildtype and daughter segregants showed an accumulation of squalene epoxides as well as ergosterol indicating that the mutation entailed a leaky block at ERG7. Upon sequencing the yeast ERG7 gene an A598S alteration was found in a conserved alpha helical region. We theorize that this mutation stabilizes Erg7p in a conformation that mimics Erg27p binding. This mutation, while decreasing OSC activity still retains sufficient residual OSC activity such that the strain in the presence of the mammalian 3-keto reductase enzyme functions and no longer requires the yeast Erg27p. Because sterol biosynthesis occurs in the ER, a fusion protein was synthesized combining Erg7p and Erg28p, a resident ER protein and scaffold of the C-4 demethyation complex. Both FGerg27 and erg27 strains containing this fusion plasmid and the mouse ERG27 orthologue showed restoration of ergosterol biosynthesis with minimal accumulation of squalene epoxides. These results indicate retention of Erg7p in the ER increases its activity and suggest a novel method of regulation of ergosterol biosynthesis.

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.

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.

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.

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.

Stochastic variational variable selection for high-dimensional microbiome data.

BACKGROUND: The rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results. However, the huge dimensionality of microbial metagenomics datasets is a major challenge for the existing methods such as Dirichlet multinomial mixture (DMM) models. In the approach of the existing methods, the computational burden of identifying a small number of representative species from a large number of observed species remains a challenge. RESULTS: We propose a novel approach to improve the performance of the widely used DMM approach by combining three ideas: (i) we propose an indicator variable to identify representative operational taxonomic units that substantially contribute to the differentiation among clusters; (ii) to address the computational burden of high-dimensional microbiome data, we propose a stochastic variational inference, which approximates the posterior distribution using a controllable distribution called variational distribution, and stochastic optimization algorithms for fast computation; and (iii) we extend the finite DMM model to an infinite case by considering Dirichlet process mixtures and estimating the number of clusters as a variational parameter. Using the proposed method, stochastic variational variable selection (SVVS), we analyzed the root microbiome data collected in our soybean field experiment, the human gut microbiome data from three published datasets of large-scale case-control studies and the healthy human microbiome data from the Human Microbiome Project. CONCLUSIONS: SVVS demonstrates a better performance and significantly faster computation than those of the existing methods in all cases of testing datasets. In particular, SVVS is the only method that can analyze massive high-dimensional microbial data with more than 50,000 microbial species and 1000 samples. Furthermore, a core set of representative microbial species is identified using SVVS that can improve the interpretability of Bayesian mixture models for a wide range of microbiome studies. Video Abstract.

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.

Effects of irrigation on root growth and development of soybean: A 3-year sandy field experiment.

Increasing the water use efficiency of crops is an important agricultural goal closely related to the root system -the primary plant organ for water and nutrient acquisition. In an attempt to evaluate the response of root growth and development of soybean to water supply levels, 200 genotypes were grown in a sandy field for 3 years under irrigated and non-irrigated conditions, and 14 root traits together with shoot fresh weight and plant height were investigated. Three-way ANOVA revealed a significant effect of treatments and years on growth of plants, accounting for more than 80% of the total variability. The response of roots to irrigation was consistent over the years as most root traits were improved by irrigation. However, the actual values varied between years because the growth of plants was largely affected by the field microclimatic conditions (i.e., temperature, sunshine duration, and precipitation). Therefore, the best linear unbiased prediction values for each trait were calculated using the original data. Principal component analysis showed that most traits contributed to principal component (PC) 1, whereas average diameter, the ratio of thin and medium thickness root length to total root length contributed to PC2. Subsequently, we focused on selecting genotypes that exhibited significant improvements in root traits under irrigation than under non-irrigated conditions using the increment (I-index) and relative increment (RI-index) indices calculated for all traits. Finally, we screened for genotypes with high stability and root growth over the 3 years using the multi-trait selection index (MTSI).Six genotypes namely, GmJMC130, GmWMC178, GmJMC092, GmJMC068, GmWMC075, and GmJMC081 from the top 10% of genotypes scoring MTSI less than the selection threshold of 7.04 and 4.11 under irrigated and non-irrigated conditions, respectively, were selected. The selected genotypes have great potential for breeding cultivars with improved water usage abilities, meeting the goal of water-saving agriculture.

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.

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.

Genome-Wide Association Study of Morpho-Physiological Traits in Aegilops tauschii to Broaden Wheat Genetic Diversity.

Aegilops tauschii, the D-genome donor of bread wheat, is a storehouse of genetic diversity that can be used for wheat improvement. This species consists of two main lineages (TauL1 and TauL2) and one minor lineage (TauL3). Its morpho-physiological diversity is large, with adaptations to a wide ecological range. Identification of allelic diversity in Ae. tauschii is of utmost importance for efficient breeding and widening of the genetic base of wheat. This study aimed at identifying markers or genes associated with morpho-physiological traits in Ae. tauschii, and at understanding the difference in genetic diversity between the two main lineages. We performed genome-wide association studies of 11 morpho-physiological traits of 343 Ae. tauschii accessions representing the entire range of habitats using 34,829 DArTseq markers. We observed a wide range of morpho-physiological variation among all accessions. We identified 23 marker-trait associations (MTAs) in all accessions, 15 specific to TauL1 and eight specific to TauL2, suggesting independent evolution in each lineage. Some of the MTAs could be novel and have not been reported in bread wheat. The markers or genes identified in this study will help reveal the genes controlling the morpho-physiological traits in Ae. tauschii, and thus in bread wheat even if the plant morphology is different.

Lifestyle changes as a result of COVID-19 containment measures: Bodyweight and glycemic control in patients with diabetes in the Japanese declaration of a state of emergency.

To clarify the association between lifestyle changes as a result of coronavirus disease 2019 containment measures and changes in metabolic and glycemic status in patients with diabetes, a cross-sectional, single-center, observation study was carried out. A self-reported questionnaire was provided to ascertain the frequency of various lifestyle activities before and after the coronavirus disease 2019 containment measures in Japan. Among 463 patients, change in glycated hemoglobin was significantly associated with change in bodyweight. After stratification by age 65 years, binary logistic regression analysis showed that increased frequency of snack eating increased bodyweight (odds ratio 1.709, P = 0.007) and glycated hemoglobin (odds ratio 1.420, P = 0.025) in the younger group, whereas in the older patients, reduced walking activities resulted in weight gain (odds ratio 0.726, P = 0.010). In conclusion, changes in eating behavior and physical activity increased bodyweight and reduced glycemic control among diabetes patients, but by different processes depending on age under the coronavirus disease 2019 containment measures in Japan.

Metabolic and physiological responses to progressive drought stress in bread wheat.

Wheat (Tritium aestivum) is vulnerable to future climate change because it is predominantly grown under rain-fed conditions in drought-prone areas. Thus, in-depth understanding of drought effect on wheat metabolism is essential for developing drought-tolerant wheat varieties. Here, we exposed wheat 'Norin 61' plants to progressive drought stress [0 (before drought), 2, 4, 6, 8, and 10 days after withholding water] during the flowering stage to investigate physiological and metabolomic responses. Transcriptional analyses of key abscisic acid-responsive genes indicated that abscisic acid signalling played a major role in the adaptation of wheat to water deficit. Carbon isotope composition had a higher value than the control while canopy temperature (CT) increased under drought stress. The CT depression was tightly correlated with soil water potential (SWP). Additionally, SWP at - 517 kPa was identified as the critical point for increasing CT and inducing reactive oxygen species. Metabolome analysis identified four potential drought-responsive biomarkers, the enhancement of nitrogen recycling through purine and pyrimidine metabolism, drought-induced senescence based on 1-aminocyclopropane-1-carboxylic acid and Asn accumulation, and an anti-senescence response through serotonin accumulation under severe drought stress. Our findings provide in-depth insight into molecular, physiological and metabolite changes involved in drought response which are useful for wheat breeding programs to develop drought-tolerant wheat varieties.