Biology subjects

Fujiwara, T.

Publications and source records attributed to Fujiwara, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Analytical methodology for unveiling the physical effect of detergents on the function of full-length membrane proteins using a single-molecule tracking system

G protein-coupled receptors (GPCRs) are important targets for drug discovery because they are the largest and most diverse family of membrane proteins in the human body. As exemplified by {beta}2-adrenergic receptor ({beta}2AR), which is a typical class-A GPCRs, they are prone to denaturation and inactivation after solubilization. Although diverse techniques have been developed, current structural and functional analyses are still limited to proteins with relatively stable and high expression. However, some mutant variants and misfolded proteins involved in diseases have extremely low expression levels and may be difficult to analyze. To overcome these limitations, we established a novel analytical platform for evaluating the ligand-binding ability of full-length {beta}2AR as a model at the single-molecule level without purification and addressing challenges such as membrane proteins with low expression levels and structural instability. This method enables the direct use of unpurified receptors immediately after solubilization and allows us to use only a small amount of sample ([~]10 ng) for observation and to distinguish between specific and nonspecific ligand binding by fitting. Furthermore, we unveiled the physical properties of detergents on the structural stability of solubilized receptors and found that the lateral pressure within detergent micelles affects ligand-binding ability. Detergents that provided a fluid microenvironment were able to maintain ligand-binding ability for several days even after solubilization; conversely, detergents that provided a rigid microenvironment caused the protein to lose its activity earlier. Our method could be a promising tool for the structural and functional analysis of membrane proteins untargeted until now. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/740883v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@3d274corg.highwire.dtl.DTLVardef@defd0forg.highwire.dtl.DTLVardef@1b81eb4org.highwire.dtl.DTLVardef@e7c275_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2026

Metabolome genome-wide association study reveals hierarchical and epistatic genetic control of flavonoid metabolism in soybean

Metabolic phenotypes are often governed by complex genetic architectures involving both additive and non-additive effects. However, the extent to which epistatic interactions contribute to the pathway-level regulation of plant metabolism remains unclear. In this study, we investigated the genetic architecture of flavonoid-related metabolites using metabolomic and genomic data from 200 soybean accessions cultivated under multiple environmental conditions. Broad-sense heritability estimates revealed that many metabolites were under strong genetic control, particularly flavonoid-related metabolites. Principal component analysis-based metabolome-wide genome-wide association studies identified four major loci associated with flavonoid metabolic variation, including a locus corresponding to flavonoid 3'-hydroxylase. Conditional analyses based on multilocus genetic backgrounds demonstrated that the effects of downstream loci were highly dependent on upstream genotypes. In particular, single-nucleotide polymorphism effects were frequently detectable only in specific allelic backgrounds defined by the major flavonoid 3'-hydroxylase locus, consistent with strong epistatic interactions among loci. Bayesian network analyses further supported a hierarchical genetic structure consistent with upstream regulation of downstream loci across the flavonoid biosynthetic pathway. These results demonstrate that highly heritable metabolic phenotypes can be controlled by a few loci exhibiting both additive and context-dependent non-additive effects. Our findings provide evidence that pathway-level metabolic diversity in soybean is generated through hierarchical and epistatic genetic control involving a limited set of key loci.

plant biology↗

bioRxiv2026

Axolotl regeneration reveals a dormant cis-regulatory grammar conserved across vertebrate genomes

Unlike most mammals, which lack the capacity to regenerate complex tissues following injury, other vertebrates such as the axolotl rebuild complete limbs throughout life, yet the regulatory mechanisms underlying this striking difference have remained elusive. Here, we define the core cis-regulatory motif grammar driving axolotl limb regeneration and demonstrate that this grammar is conserved within the syntenic neighborhoods of regeneration-gene orthologs in human and mouse genomes, despite being epigenetically sealed in adult mammalian tissues. Integrating this cross-species grammar projection with AlphaGenome, a multimodal genomic AI capable of predicting epigenomic states from long sequence context, we find that the highest-ranking candidate loci are predicted to occupy a state of bivalent dormancy marked by the co-enrichment of poising and repressive histone modifications alongside suppression of transcriptional activity and chromatin accessibility. Systematic in silico motif perturbation further predicts that this dormant state is actively enforced by specific dormancy-stabilizing sequence elements, and that disrupting these elements shifts candidate loci toward a regeneration-competent chromatin configuration. Our findings support a model in which the regenerative blueprint has not been erased from the mammalian genome but locked within it, opening new avenues for understanding the evolution of regenerative competence and for the rational reactivation of latent regenerative programs.

systems biology↗

bioRxiv2026

AI-enabled rhodopsin design for blue-light enhanced bacterial growth

We developed an AI-guided design pipeline that generated and validated non-natural microbial rhodopsins with spectral properties not yet known in nature. The pipeline comprised a three-stage in silico design, a genetic algorithm (GA) for sequence generation, a stacked LASSO and XGBoost machine-learning (ML) regressor for spectral prediction and fitness ranking, and a Markov-based sequence plausibility filter to enforce proton pumping like characteristics. Four candidate rhodopsins (APR1, APR2, APR6, and APR7) targeting blue light absorption were designed and AlphaFold3 structural modelling predicted retinal binding pocket architecture consistent with outward proton-pumping function. Experimental characterisation confirmed that all four variants absorbed light at [~]410 nm and significantly promoted the growth of Cupriavidus necator under blue light illumination. This study demonstrates that AI-enabled design can engineer proteins with no natural precedent, generating light-harvesting rhodopsins with novel spectral properties while preserving biological function, marking a significant advance in programmable synthetic biology.

Synthetic Biology↗

bioRxiv2026

De novo design of therapeutic scFvs and multi-specific engagers from sequence alone

The discovery of functional antibody therapeutics is fundamentally constrained by the astronomical complexity of the sequence landscape and the inefficiency of empirical screening. While structure-based computational design has made significant strides, its reliance on high-resolution co-complex data restricts its utility against the vast undruggable proteome and rapidly evolving pathogens. Here, we introduce IASO, a sequence-centric generative framework for the de novo design of diverse biotherapeutic modalities, including single-chain variable fragments and multi-specific engagers, requiring only antigen sequence as input. IASO integrates an evolutionary-informed generative engine that constructs antigen-steered candidate libraries with a high-fidelity interaction module capturing synergistic physicochemical motifs. We demonstrate the frameworks robustness by identifying novel binders against diverse clinical targets, where IASO successfully discriminates atomic-level mutations to overcome established drug resistance in EGFR and distinguishes single-residue variants of SARS-CoV-2. Furthermore, we extend this approach to the in silico construction of a complex bispecific T-cell engager that exhibits developability profiles comparable to clinically approved therapeutics, including enhanced solubility and minimal immunogenicity. By transforming serendipitous exploration into predictable high-resolution engineering, IASO establishes a scalable foundation for the rapid development of next-generation biopharmaceuticals directly from sequence data alone.

bioinformatics↗

bioRxiv2025

Sexual life cycle establishes the unicellular red algae Cyanidiophyceae as a genetically tractable model lineage for eukaryotic evolution

The thermo-acidophilic unicellular algal class Cyanidiophyceae is the earliest-branching lineage in red algae, which diverged from Viridiplantae lineage (green algae and land plants) soon after chloroplast establishment in the common ancestor of Archaeplastida. Cyanidiophyceae possess extremely simple genomes (8.7-17.8 Mb; approximately 4,800- 7,800 genes), and the cell-wall-less, genetically tractable strain Cyanidioschyzon merolae 10D has served as a model organism. However, its unknown sexual life cycle has limited its utility in studies of evolution and genetics. Inspired by the recent discovery of sexual reproduction in the cyanidiophycean genus Galdieria, we identified similar life cycles in the other cyanidiophycean genera Cyanidioschyzon, Cyanidiococcus, and Cyanidium. In these genera, the cell-walled diploid form, exclusively observed in nature, produces a cell-wall-less haploid form when the culture pH is lowered, and both proliferate asexually. In addition, the cell-wall-less Cyanidioschyzon merolae 10D strain has been shown to be a haploid clone that forms a cell-walled diploid through mating with other haploid clones. Building on these findings, we generated high-quality genomic resources with phase-specific transcriptomes and developed genetic manipulation systems using the cell-wall-less haploids of these genera. We further uncovered phase-specific distribution of histone H3 lysine 27 trimethylation linked to haploid- and diploid-specific gene expression, including transcription factors involved in differentiation associated with sexual reproduction in plants. Additionally, biparental inheritance of organelle DNA occurs following isogamous mating of haploid cells but resolves into uniparental inheritance during diploid proliferation. These advances position Cyanidiophyceae as a powerful model lineage for studying early Archaeplastida evolution, the shared mechanisms of photosynthetic eukaryotes, and their environmental adaptation.

plant biology↗

bioRxiv2025

Chromosome-level genome assembly of the gerbera (Gerbera hybrida) using HiFi long-read and Hi-C technologies

Gerbera hybrida is one of the most popular ornamental plants and also serves as a valuable model plant within the Asteraceae family. Here, we report both the nuclear and organellar genome assemblies and annotations of G. hybrida, which was developed through hybridization of two wild species. Sequencing was performed using a combination of PacBio high fidelity (HiFi) reads and chromatin capture reads (Omni-C). The total span of the nuclear genome assembly is 2.32 gigabases, and 99.3% of the sequence assembled into 25 scaffolds, consistent with the known chromosome number. Genome annotation of the nuclear genome identified 36,160 protein-coding genes and 11,572 non-coding transcripts. The mitochondrial genome had 363,511 bp and contains 36 protein-coding genes, 3 rRNAs, and 21 tRNAs, while the chloroplast genome is 151,898 bp in length and includes 85 protein-coding genes, 8 rRNAs, and 37 tRNAs. This reference genome provides a foundational resource for future molecular breeding and genetic research in Gerbera and the broader Asteraceae family.

plant biology↗

bioRxiv2025

Integration of Proxy Intermediate Omics traits into a Nonlinear Two-Step model for accurate phenotypic prediction

Intermediate omics traits, which mediate the effects of genetic variation on phenotypic traits, are increasingly recognised as valuable components of genetic evaluation. In particular, rhizosphere microbiota play a crucial role in plant health and productivity; however, their complex interactions with host genetics remain challenging to model. Although two-step modeling frameworks have been proposed to integrate intermediate omics traits into phenotype prediction, existing approaches do not incorporate nonlinear relationships between different omics layers. To address this, we have proposed a two-step phenotype prediction framework that integrates genomic, rhizosphere microbiome, and metabolome (meta-metabolome) data, while explicitly capturing omicsomics nonlinearities. The first step is to predict meta-metabolome traits from genetic and microbial features, thus effectively isolating them from the environmental noise. In this process, intermediate "proxy" omics traits are generated as general biological information to provide robust models. The second step utilises this "proxy" to enhance the accuracy of the phenotype prediction. We compared the linear model (Best Linear Unbiased Prediction, BLUP) and the nonlinear model (Random Forest, RF) at each step, as demonstrated through simulations and empirical analysis of a multi-omics soybean dataset in which nonlinear modeling captures intricate omics interactions. Notably, our approach enables phenotype prediction without requiring the original meta-metabolome data used in model training, thereby reducing reliance on costly omics measurements. This framework integrates intermediate omics traits into genomic prediction to improve prediction accuracy and provide solutions for deeper insights into plant-microbiome interactions.

bioinformatics↗

bioRxiv2024

Implication of S-1 biomarkers on aging and prognosis in elderly patients with lung cancer treated by adjuvant S-1 chemotherapy: Accompanying results of the Setouchi Lung Cancer Group Study 1201

BACKGROUNDSThe management of elderly patients presents several challenges due to age-related declines; however recent recommendations advocate for age not being the sole determinant for adjuvant treatment decisions in patients with non-small cell lung cancer (NSCLC). Aging may alter expression levels of 5-fluorouracil (5-FU) biomarkers. METHODSExpression changes with aging were explored using The Cancer Genome Atlas (TCGA) database. 5-FU-related biomarker expressions, including thymidylate synthase (TS), dihydropyrimidine dehydrogenase (DPD), orotate phosphoribosyltransferase, epidermal growth factor receptor (EGFR), and excision repair cross-complementation group-1 (ERCC1), were assessed by the quantitative reverse-transcription PCR assays in 89 NSCLCs elderly patients ([≥] 75 years old) receiving adjuvant S-1, an oral fluoropyrimidine agent, therapy in a SLCG1201 trial. RESULTS: TCGA database analysis (n=955) indicated decreased TS expression with aging, particularly in those 75 or older. In the SCLG1201 trial, univariate analysis revealed that high EGFR and low TS expressions correlated with favorable recurrence-free survival (RFS)(p=0.0264) and overall survival (OS)(p=0.0365), respectively. Multivariable analysis confirmed pathological stage as an independent prognostic factor for both RFS and OS (p=0.0052 and 0.0352, respectively). EGFR mutant tumors (n=27) showed significantly higher DPD (p=0.0066) and EGFR (p<0.0001) expressions, and lower TS(p=0.0125) and ERCC1(p=0.0015) expressions. CONCLUSIONDespite pathological stage being an independent prognostic factor, high EGFR and low TS expressions may predict better clinical outcomes in elderly NSCLC patients using adjuvant S-1. The age-related decrease in TS expression supports the potential benefit of 5-FU-based therapy in them compared to younger patients. Further research is warranted to validate these clinical implications.

pharmacology and toxicology↗

bioRxiv2024

Carotenoid pigments enhance rhodopsin-mediated phototrophy by light-harvesting and photocycle-accelerating

Microbial rhodopsins, photoreceptor proteins widely distributed in marine microorganisms, receive large amount of light energy that sustains marine ecosystems. Although rhodopsins generally harbor retinal as their only chromophore, a recent study reported that carotenoid antennae transfer light energy to the retinal in proteorhodopsins, proton pump rhodopsins abundant in marine environments. Here, using marine bacterial isolates, we detected energy transfer from a carotenoid (myxol) to retinal not only in proteorhodopsin but also in the chloride ion-pumping rhodopsin. Carotenoid binding improved the light utilization efficiency of the proteorhodopsin by accelerating the photocycle, together with facilitating light-harvesting. The carotenoid-binding ability is conserved in rhodopsins of the phylum Bacteroidota, which are widely transcribed in the photic zone. These findings suggest that the distribution of carotenoid-binding rhodopsins is more taxon-specific than previously thought, thus underscoring the importance of carotenoid-binding rhodopsins that provide an extended light utilization strategy in the environmental adaptation of marine bacteria.

microbiology↗

bioRxiv2024

Structural characterization of pyruvic oxime dioxygenase, a key enzyme of heterotrophic nitrification

Nitrification by heterotrophic microorganisms is an important part of the nitrogen cycle in the environment. The enzyme responsible for the core function of heterotrophic nitrification is pyruvic oxime dioxygenase (POD). POD is a non-heme Fe(II)-dependent enzyme that catalyzes the dioxygenation of pyruvic oxime to produce pyruvate and nitrite. To analyze the catalytic mechanism of POD, the crystal structure of POD from Alcaligenes faecalis (AfPOD) was determined at 1.76 [A] resolution. The enzyme is a homo-tetramer and the subunit structure is homologous to those of class II aldolases, in particular a zinc-dependent L-fuculose-1-phosphate aldolase. The active site of the subunit is located at the bottom of a cleft formed with an adjacent subunit. The iron ion at the active site is coordinated by three histidines and three water molecules in an octahedral geometry. The putative oxygen tunnel was connected between the active site and the central cavity of the tetramer. The N-terminal region of AfPOD, which is essential for catalytic activity, is disordered in the crystal. Structure prediction with AlphaFold2 combined with mutational experiments suggested that the disordered N-terminal region adopts an -helix conformation and participates in the formation of the active site. The catalytic mechanism of the dioxygenase reaction by POD is discussed on the basis of the molecular docking model. IMPORTANCEOur knowledge of nitrification has increased considerably in recent decades with the discovery of new nitrifying microorganisms and the characterization of their biochemical processes. Some heterotrophic bacteria and fungi are known to show nitrification activities, but the molecular mechanisms had been poorly understood. Here, we performed a structural characterization of POD, a key enzyme in heterotrophic nitrification that produces nitrite from ammonia using pyruvic oxime as an intermediate. Structural and enzymatic analyses revealed that POD is a unique dioxygenase with features such as an aldolase backbone, an N-terminal -helix, and an oxygen tunnel. Our results provide insights not only into the molecular mechanisms but also into the design of specific inhibitors of heterotrophic nitrification.

microbiology↗

bioRxiv2024

Soil nutrition-dependent dynamics of the root-associated microbiome in paddy rice

O_LIPlants accommodate diverse microbial communities (microbiomes), which can change dynamically during plant adaptation to varying environmental conditions. However, the direction of these changes and the underlying mechanisms driving them, particularly in crops adapting to the field conditions, remain poorly understood. C_LIO_LIWe investigate the root-associated microbiome of rice (Oryza sativa L.) using 16S rRNA gene amplicon and metagenome sequencing, across four consecutive cultivation seasons in a high-yield, non-fertilized, and pesticide-free paddy field, compared to a neighboring fertilized and pesticide-treated field. C_LIO_LIOur findings reveal that root microbial community shifts and diverges based on soil fertilization status and plant developmental stages. Notably, nitrogen-fixing bacteria such as Telmatospirillum, Bradyrhizobium and Rhizomicrobium were over-represented in rice grown in the non-fertilized field, implying that the assembly of these microbes supports rice adaptation to nutrient-deficient environments. C_LIO_LIA machine learning model trained on the microbiome data successfully predicted soil fertilization status, highlighting the potential of root microbiome analysis in forecasting soil nutrition levels. Additionally, we observed significant changes in the root microbiome of ccamk mutants, which lack a master regulator of mycorrhizal symbiosis, under laboratory conditions but not in the field, suggesting a condition-dependent role for CCaMK in establishing microbiomes in paddy rice. C_LI

plant biology↗

bioRxiv2024

A fluorophore-ligand conjugate for the rapid and reversible staining of native AMPA receptors in living neurons

The subcellular localizations of neurotransmitter receptors are strictly regulated in neurons. Changes in the trafficking of -amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA)-type glutamate receptors (AMPARs) play an essential role in synaptic plasticity, which is the cellular basis of learning and memory. To explore receptor trafficking, genetically encoded approaches (e.g., the fusion of fluorescent proteins to receptors) are often used. However, concerns remain that genetic approaches cannot fully reproduce the receptor function that is inherent to neurons. Herein, we report a chemical probe, PFQX1(AF488), for the visualization of cell-surface AMPARs without any genetic manipulation to neurons. The rapid and reversible staining features of this probe allowed the visualization of AMPAR accumulation in dendritic spines during synaptic plasticity in living hippocampal neurons. Moreover, the mechanisms of this synaptic accumulation, for which genetically encoded approaches have given controversial results, were revealed by integrating two chemical methods: PFQX1(AF488) and covalent chemical labeling.

neuroscience↗

bioRxiv2024

Single-molecule detection of transient dimerization of opioid receptors 1: Homodimers' effect on signaling and internalization

Opioid receptors (ORs) are critical for endogenous and synthetic analgesics. Their homodimerization is considered important for their pharmacological diversities, but whether they form homodimers remains controversial. Here, we established that the three classical ORs, -, {kappa}-, and {delta}-ORs (MOR, KOR, and DOR, respectively) undergo repeated transient (120-180 ms) homodimerizations every few seconds. This was done by using single-molecule imaging and developing theories for analyzing single-molecule colocalization data, which provide the key parameters, homodimer-monomer dissociation equilibrium constants and rate constants. Their 9-26 amino-acid C-terminal cytoplasmic domains, without sequence similarities, are involved in specific homodimerization, whereas the transmembrane domains provide less specific affinities. Using the membrane-permeable peptides mimicking the C-terminal homodimerization sequences which block homodimerizations, functions of monomers and homodimers were dissected. KOR and DOR homodimers, but not MOR homodimers, activate downstream G-proteins differently from monomers upon agonist addition, without influencing OR internalization. These findings could guide strategies to enhance OR-based analgesia.

biophysics↗

bioRxiv2024

Single-molecule detection of transient dimerization of opioid receptors 2: Heterodimer blockage reduces morphine tolerance

Heterodimerization of opioid receptors (ORs), MOR, KOR, and DOR, is implied in their functional regulation and diversification, and thus its understanding is crucial for developing better analgesic treatments. However, our knowledge on OR heterodimerization/heterodimers remains limited. Here, using single-molecule imaging and functional analysis, we found that MOR, the main morphine receptor, repeatedly forms transient ({approx}250 ms) heterodimers with DOR every 1-10 seconds, but not with KOR, whereas DOR and KOR also form transient heterodimers. We obtained all the heterodimer-monomer equilibrium constants and rate constants with/without agonists. We identified the critical heterodimer binding sites in the extracellular domains, in addition to the less-specific transmembrane domains, and developed soluble peptide blockers for MOR-DOR and DOR-KOR heterodimerization, using amino-acid sequences mimicking the extracellular binding sites. With these peptide blockers, we dissected the monomer/dimer roles in OR internalization and signaling. The soluble MOR-DOR heterodimer blocker reduced the development of long-term morphine tolerance in mice.

biophysics↗

bioRxiv2024

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 two irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of five 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.

plant biology↗

bioRxiv2023

StayGold Photostability under Different Illumination Modes

StayGold is a bright fluorescent protein (FP) that is over one order of magnitude more photostable than any of the currently available FPs across the full range of illumination intensities used in widefield microscopy and structured illumination microscopy, the latter of which is a widefield illumination-based technique. To compare the photostability of StayGold under other illumination modes with that of three other green-emitting FPs, namely EGFP, mClover3, and mNeonGreen, we expressed all four FPs as fusions to histone 2B in HeLa cells. Unlike the case of widefield microscopy, the photobleaching behavior of these FPs in laser scanning confocal microscopy (LSCM) is complicated. The outstanding photostability of StayGold observed in multi-beam LSCM was variably attenuated in single-beam LSCM, which produces intermittent and instantaneously strong illumination. We systematically examined the effects of different single-beam LSCM beam-scanning patterns on the photostability of the FPs. This study offers relevant guidelines for researchers who aim to achieve sustainable imaging by resolving problems related to FP photostability.

cell biology↗

bioRxiv2023

Evaluating drought tolerance stability in soybean by theresponse of irrigation change captured from time-seriesmultispectral data

This study investigated a method to evaluate the drought tolerance stability of a genotype in a single environmental trial by capturing the plant response to irrigation changes. Genotypes that exhibit stable phenotypes under various drought stress conditions are required for stable crop production. However, considerable time and money are required to evaluate the environmental stability of a genotype through multiple environmental trials. As an index of drought tolerance stability, we calculated the coefficient of variation (CV) of shoot fresh weight of 178 soybean (Glycine max (L.) Merr.) accessions in a total of nine types of drought treatments, including changing irrigation treatments (every five or ten days) over 3-year trials. To capture the plant responses to irrigation changes, time-series multispectral (MS) data were collected, including the timings of the irrigation/non-irrigation switch in the changing irrigation treatments. We built a random regression model (RRM) for each of the nine treatments using the time-series MS data. 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 two out of the three years, the prediction accuracy of MTRRM models built in the changing irrigation treatment was higher than that in the continuous drought treatment in the same year. When the CV was predicted using the MTRRM model across years in the changing irrigation treatment, the prediction accuracy was 61% higher than that of the simple genomic prediction model. These results suggest that drought tolerance stability can be evaluated in a single environmental trial, which may reduce the time and cost of selecting genotypes with high drought tolerance stability.

genetics↗