Biology subjects

Toda, Y.

Publications and source records attributed to Toda, Y..

17 recordsLinked to original sources

bioRxiv2026

Early origin of sugar sensing in jawed vertebrates

Sweet taste guides animals to consume carbohydrate-rich foods, and many different vertebrate groups, from fish to mammals, rely on sugar-rich fruits or nectar produced by flowering plants (angiosperms). Although the genes encoding T1R2-T1R3, the receptor pair that mammals use to sense sugars, exist in the genomes of many vertebrates, their functions are unclear--and whether sugar sensing arose once early in vertebrate evolution or independently in different lineages after angiosperms evolved is currently unknown. Here, we combined ancestral reconstruction and receptor functional profiling to examine the evolutionary history of T1R taste receptors--including recently-described non-canonical receptors--across all major vertebrate clades. Our results pinpoint the origin of sugar sensing to before the emergence of angiosperms and uncover a myriad of alternative T1R-based sugar-sensing mechanisms, suggesting multiple independent T1R trajectories and revealing uncharted sensory diversity across vertebrates.

evolutionary biology↗

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↗

bioRxiv2025

Structural basis of stereochemical promiscuity by an umami taste receptor ortholog, Tas1r1/Tas1r3 from pufferfish

Taste receptor type 1 (TAS1R), which consists of sweet and umami receptors in humans, senses nutrients (such as sugars and amino acids) with substrate specificity that is often broad and varies among animals and subtypes. However, the structural basis for achieving diverse specificities remains largely elusive. Here, we present the crystal structure of the ligand-binding domain (LBD) of Tas1r1/Tas1r3 heterodimer from pufferfish, an ortholog of the human umami taste receptor. The overall structure of Tas1r1/Tas1r3LBD resembles previously reported TAS1R structures, indicating a conserved core architecture within the family. Nevertheless, pufferfish Tas1r1/Tas1r3 was found to bind and respond to both L- and D-amino acids, even though TAS1Rs are considered to respond to either enantiomer. Structural and mutational analyses revealed that this non-rigorous stereochemical recognition is attributed to inter- subdomain interactions that latch the cleft containing the amino acid-binding site. These interactions prevent the cleft from fully opening, thereby stabilizing the active conformation, even if the ligand shares a different chirality. These results suggest that different substrate specificities in TAS1Rs can be acquired not only by the gain or loss of direct interactions with the substrate but also by the gain of intramolecular interactions, which alter the conformational equilibrium of the receptor.

biochemistry↗

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

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

Revealing the spatial characteristics of rice heat exposure in Japan through panicle temperature analysis

Elevated temperatures during the flowing stage contribute to heat-induced spikelet sterility in rice, posing a major threat to production considering climate change projections. Developing effective strategies for stable rice production through breeding and crop management is critical; however, our understanding of regional, seasonal, and long-term trends in rice heat exposure remains limited. Previous studies on spikelet sterility revealed that panicle temperature, estimated using a micrometeorological model and common meteorological factors, serves as a reliable indicator of rice heat exposure. In this study, we employed this model to identify the differences between panicle and air temperatures (DPAT) and their causes over the past 45 years in Japan. A gridded daily meteorological dataset covering Japan was interpolated at an hourly time step and used as input data of the micrometeorology model for estimating panicle temperatures during flowering. Statistical analysis of the resulting data revealed an increasing trend in the frequency of rice panicle heat exposure over time across many locations in Japan. During heat-receptive periods, panicle temperature generally exceeded air temperature, indicating the inadequacy of relying solely on air temperature to gauge rice heat stress. DPAT values showed substantial inter-regional variations in both mean values (from -0.5 to 3.0) and seasonality. Through machine learning and statistical methods, the relationship between DPAT and meteorological factors was characterized, delineating the effects of the meteorological factors on regional and seasonal DPAT variations. Focusing on major high-risk regions, we show that mitigation strategies should be adapted to consider regional characteristics and avoid high DPAT conditions during rice heading periods.

plant biology↗

bioRxiv2023

AtDREB2G is a novel regulator of riboflavin biosynthesis under low-temperature stress and abscisic acid treatment in Arabidopsis thaliana

Riboflavin (RF) serves as a precursor to FMN and FAD, crucial cofactors in various metabolic processes. Strict regulation of cellular flavin homeostasis is imperative, yet information regarding the factors governing this regulation remains largely elusive. In this study, we first examined the impact of external flavin treatment on the Arabidopsis transcriptome to identify novel regulators of cellular flavin levels. Our analysis revealed alterations in the expression of 49 putative transcription factors. Subsequent reverse genetic screening highlighted a member of the Dehydration-Responsive Element Binding (DREB) family, AtDREB2G, as a potential regulator of cellular flavin levels. Knockout mutants of AtDREB2G (dreb2g) exhibited reduced flavin levels and decreased expression of RF biosynthetic genes compared to wild-type plants. Conversely, conditional overexpression of AtDREB2G led to an increase in the expression of RF biosynthetic genes and elevated flavin levels. In wild-type plants, exposure to low temperatures and abscisic acid treatment stimulated enhanced flavin levels and upregulated the expression of RF biosynthetic genes, concomitant with the induction of AtDREB2G. Notably, these responses were significantly attenuated in dreb2g mutants. Our findings establish AtDREB2G as a novel positive regulator of flavin biosynthesis in Arabidopsis, particularly under conditions of low temperature and abscisic acid treatment.

plant biology↗

bioRxiv2023

An integrative framework of stochastic variational variable selection for joint analysis of multi-omics microbiome data

High-dimensional multi-omics microbiome data plays an important role in elucidating microbial communities interactions with their hosts and environment in critical diseases and ecological changes. Although Bayesian clustering methods have recently been used for the integrated analysis of multi-omics data, no method designed to analyze multi-omics microbiome data has been proposed. In this study, we propose a novel framework called integrative stochastic variational variable selection (I-SVVS), which is an extension of stochastic variational variable selection for high-dimensional microbiome data. The I-SVVS approach addresses a specific Bayesian mixture model for each type of omics data, such as an infinite Dirichlet multinomial mixture model for microbiome data and an infinite Gaussian mixture model for metabolomic data. This approach is expected to reduce the computational time of the clustering process and improve the accuracy of the clustering results. Additionally, I-SVVS identifies a critical set of representative variables in multi-omics microbiome data. Three datasets from soybean, mice, and humans (each set integrated microbiome and metabolome) were used to demonstrate the potential of I-SVVS. The results indicate that I-SVVS achieved improved accuracy and faster computation compared to existing methods across all test datasets. It effectively identified key microbiome species and metabolites characterizing each cluster. For instance, the computational analysis of soybean dataset, including 377 samples with 16,943 microbiome species and 265 metabolome features, was completed in 2.18 hours using I-SVVS, compared to 2.35 days with Clusternomics and 1.12 days with iClusterPlus. The software for this analysis, written in Python, is freely available at https://github.com/tungtokyo1108/I-SVVS.

bioinformatics↗

bioRxiv2023

Modeling soybean growth: A mixed model approach

The evaluation of plant and animal growth, separately for genetic and environmental effects, is necessary for genetic understanding and genetic improvement of environmental responses of plants and animals. We propose to extend an existing approach that combines nonlinear mixed-effects model (NLMEM) and the stochastic approximation of the Expectation-Maximization algorithm (SAEM) to analyze genetic and environmental effects on plant growth. These tools are widely used in many fields but very rarely in plant biology. During model formulation, a nonlinear function describes the shape of growth, and random effects describe genetic and environmental effects and their variability. Genetic relationships among the varieties were also integrated into the model using a genetic relationship matrix. The SAEM algorithm was chosen as an efficient alternative to MCMC methods, which are more commonly used in the domain. It was implemented to infer the expected growth patterns in the analyzed population and the expected curves for each variety through a maximum-likelihood and a maximum-a-posteriori approaches, respectively. The obtained estimates can be used to predict the growth curves for each variety. We illustrate the strengths of the proposed approach using simulated data and soybean plant growth data obtained from a soybean cultivation experiment conducted at the Arid Land Research Center, Tottori University. In this experiment, plant height was measured daily using drones, and the growth was monitored for approximately 200 soybean cultivars for which whole-genome sequence data were available. The NLMEM approach improved our understanding of the determinants of soybean growth and can be successfully used for the genomic prediction of growth pattern characteristics. Author summaryNonlinear models are useful for modeling animal and plant growth; however, their parameters are influenced by both genetic and environmental factors. If the same model can be applied to data with different genetic and environmental factors by allowing parameter variations, it can be used to understand, predict, and control the genetic and environmental influences of growth models based on parameter variation. In this study, we propose a statistical method based on nonlinear mixed-effects modeling. The simulation and real data analysis results show that the proposed method was effective in modeling the growth of genetically different soybean varieties under different drought conditions. The usefulness of the proposed method is expected to increase, as high-throughput measurements provide growth data for a large number of genotypes in various environments.

genetics↗

bioRxiv2023

Latent taste diversity revealed by a vertebrate-wide catalogue of T1R receptors

Taste is a vital chemical sense for feeding behavior. In mammals, the umami and sweet taste receptors are composed of three members of the taste receptor type 1 (T1R/TAS1R) family: T1R1, T1R2, and T1R3. Because their functional homologs exist in teleosts, only three TAS1R genes generated by gene duplication are believed to have been inherited from the common ancestor of bony vertebrates. Here, we report five previously uncharacterized TAS1R members in vertebrates, named TAS1R4, 5, 6, 7, and TAS1Rcf, through a genome-wide survey of diverse taxa. For TAS1R2 and TAS1R3, mammalian and teleost fish genes were found to be paralogous. Phylogenetic analysis suggests that the bony vertebrate ancestor had nine TAS1Rs due to multiple gene duplications, and some TAS1Rs were lost independently in each lineage; ultimately, mammals and teleosts have retained only three TAS1Rs, whereas other lineages have retained more TAS1Rs. Functional assays and expression analysis in non-teleost fishes suggest that the novel T1Rs form heterodimers in taste receptor cells and contribute to the recognition of a broad range of ligands such as essential amino acids, including branched-chain amino acids, which were not previously considered as T1R ligands. These results highlight an unexpected diversity of taste sensations in both modern and the ancestors of vertebrates. The complex evolution of the taste receptor family might have enabled vertebrates to adapt to diverse habitats on Earth.

evolutionary 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↗

bioRxiv2022

Image-based quantification of Arabidopsis thaliana stomatal aperture from leaf images

The quantification of stomatal pore size has long been a fundamental approach to understand the physiological response of plants in the context of environmental adaptation. Automation of such methodologies not only alleviates human labor and bias, but also realizes new experimental research methods through massive analysis. Here, we present an image analysis pipeline that automatically quantifies stomatal aperture of Arabidopsis thaliana leaves from brightfield microscopy images containing mesophyll tissue as noisy backgrounds. By combining a YOLOX-based stomatal detection submodule and a U-Net-based pore segmentation submodule, we achieved 0.875 mAP50 (mean average precision; stomata detection performance) and 0.745 IoU (intersection of union; pore segmentation performance) against images of leaf discs taken with a brightfield microscope. Moreover, we designed a portable imaging device that allows easy acquisition of stomatal images from detached/undetached intact leaves on-site. We further combined this device with fine-tuned models of the pipeline we generated here and recapitulated manual measurement of stomatal responses against pathogen inoculation. Utilization of our hardware and pipeline for automated stomatal aperture measurements is expected to accelerate research on stomatal biology of model dicots.

plant biology↗

bioRxiv2022

QTL mapping for seed morphology using the instance segmentation neural network in Lactuca spp.

Wild species of lettuce (Lactuca sp.) are thought to have first been domesticated for oilseed contents to provide seed oil for human consumption. Although seed morphology is an important trait contributing to oilseed in lettuce, the underlying genetic mechanisms remain elusive. Since lettuce seeds are small, a manual phenotypic determination required for a genetic dissection of such traits is challenging. In this study, we built and applied an instance segmentation-based seed morphology quantification pipeline to measure traits in seeds generated from a cross between the domesticated oilseed type cultivar Oilseed and the wild species UenoyamaMaruba in an automated manner. Quantitative trait locus (QTL) mapping following ddRAD-seq revealed 11 QTLs linked to 7 seed traits (area, width, length, length-to-width ratio, eccentricity, perimeter length, and circularity). Remarkably, the three QTLs with the highest LOD scores, qLWR-3.1, qECC-3.1, and qCIR-3.1, for length-to-width ratio, eccentricity, and circularity, respectively, mapped to linkage group 3 (LG3) around 161.5 to 214.6 Mb, a region previously reported to be associated with domestication traits from wild species. These results suggest that the oilseed cultivar harbors genes acquired during domestication to control seed shape in this genomic region. This study also provides genetic evidence that domestication arose, at least in part, by selection for the oilseed type from wild species and demonstrates the effectiveness of image-based phenotyping to accelerate discoveries of the genetic basis for small morphological features such as seed size and shape.

plant biology↗

bioRxiv2021

Time-series Multi-spectral Imaging in Soybean for Improving Biomass and Genomic Prediction Accuracy

Multi-spectral (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 above-ground biomass (AGB) and determined which developmental stages should be used for accurate prediction in soybean. 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 to late growth stages. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multi-trait genomic prediction. The accuracy of the prediction model increased starting at an early stage of growth (31 days after sowing). To predict phenotypic values of AGB, we employed multi-kernel genomic prediction. Consequently, the prediction accuracy of phenotypic values reached a maximum at a relatively early growth stage (38 days after sowing). Hence, the optimal timing for MS imaging may depend on the irrigation levels.

plant biology↗

bioRxiv2021

Simple amplicon 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 developed a novel high-throughput method based on a two-step PCR amplification protocol, involving DNA extraction using magnetic beads and PCR purification using exonuclease, for 16S rRNA gene amplicon sequencing of plant root microbiota. This method reduces sample handling and captures microbial diversity comparable to that obtained by the standard method. We found that using a buffer with magnetic beads enabled efficient extraction of microbial DNA directly from plant roots. In addition, we 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. Our method offers a simple and high-throughput solution for maintaining the quality of plant root microbial community profiling.

plant biology↗

bioRxiv2019

Learning from Synthetic Dataset for Crop Seed Instance Segmentation

Incorporating deep learning in the image analysis pipeline has opened the possibility of introducing precision phenotyping in the field of agriculture. However, to train the neural network, a sufficient amount of training data must be prepared, which requires a time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segmentation neural network (Mask R-CNN) aimed to phenotype the barley seed morphology of various cultivars, can be sufficiently trained purely by a synthetically generated dataset. Our attempt is based on the concept of domain randomization, where a large amount of image is generated by randomly orienting the seed object to a virtual canvas. After training with such a dataset, performance based on recall and the average Precision of the real-world test dataset achieved 96% and 95%, respectively. Applying our pipeline enables extraction of morphological parameters at a large scale, enabling precise characterization of the natural variation of barley from a multivariate perspective. Importantly, we show that our approach is effective not only for barley seeds but also for various crops including rice, lettuce, oat, and wheat, and thus supporting the fact that the performance benefits of this technique is generic. We propose that constructing and utilizing such synthetic data can be a powerful method to alleviate human labor costs needed to prepare the training dataset for deep learning in the agricultural domain.

bioinformatics↗

bioRxiv2018

DeepStomata: Facial Recognition Technology for Automated Stomatal Aperture Measurement

Stomata are an attractive model for studying the physiological responses of plants to various environmental stimuli1-3. Of the morphological parameters that represent the degree of stomatal opening, the length of the minor axis of the stomatal pore (the stomatal aperture) has been most commonly used to dissect the molecular basis of its regulation. Measuring stomatal apertures is time consuming and labour intensive, preventing their use in large-scale studies. Here, we completely automated this process by developing a program called DeepStomata, which combines stomatal region detection and pore isolation by image segmentation. The former, which comprises histograms of oriented gradients (HOG)-based stomatal detection and the convolutional neural network (CNN)-based classification of open/closed-state stomata, acts as an efficient conditional branch in the workflow to selectively quantify the pores of open stomata. An analysis of batches of images showed that the accuracy of our automated aperture measurements was equivalent to that of manual measurements, however had higher sensitivity (i,e., lower false negative rate) and the process speed was at least 80 times faster. The outstanding performance of our proposed method for automating a laborious and repetitive task will allow researchers to focus on deciphering complex phenomena.

bioinformatics↗