Biology subjects

Takanashi, H.

Publications and source records attributed to Takanashi, H..

9 recordsLinked to original sources

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

Natural variation in IBF1 disrupts its interaction with CHS1 and affects metabolism of hulls in rice

Secondary metabolites in plants have various physiological functions, including antioxidant and antibacterial activities. Previous studies have suggested genes and associated molecular mechanisms involved in the production of diverse secondary metabolites. However, much less is known about the genetic bases underlying within-species diversity in metabolite accumulation patterns, particularly in less focused tissues such as rice hulls. In this study, we aimed to identify the causal variant that affects flavonoid accumulation in rice hulls. We identified an F-box containing protein IBF1 is causal for genotypic differences in hull color through positional cloning. The variety IR64, with straw-white hulls, harbors functional IBF1 proteins that interact with a chalcone synthase, CHS1. Conversely, frame-shift mutations of IBF1 in the variety DJ123, which has pigmented hull color, resulted in a lack of a Kelch domain essential for the IBF1-CHS1 interaction. As a result, the DJ123 variant of IBF1 (IBF1DJ123) no longer interacted with CHS1, which was further supported by deep learning-based protein structural modeling. Further metabolome and transcriptome analyses using IR64 and an IR64-based chromosomal segment substitution line (CSSL) carrying IBF1DJ123 revealed an increase in the content of multiple flavonoids (such as naringenin and luteolin), while suppressing the expression of CAD involved in lignin synthesis. Metabolites in the CSSL carrying IBF1DJ123 suppressed the growth and siderophore generation activity of Pantoea species, which can act as beneficial or pathogenic endophytes. This study highlights the impact of a single gene on diverse metabolite accumulation patterns and suggests that this change may provide defense against pathogens.

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

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

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↗

bioRxiv2023

TALE-based C-to-T base editor for multiple homologous genes with flexible precision

Recently a cytidine deaminase-based method for highly efficient C-to-T targeted base editing was developed and has been used with CRISPR-mediated systems. It is a powerful method for genome engineering, although it is prone to off-target effects and has limited targeting scope. Transcription activator-like effector (TALE) -based tools which allow longer recognition sequences than do CRISPR/Cas9 systems, can also be used for targeted C-to-T base editing. Here, we describe a method that efficiently achieved targeted C-to-T substitutions in Arabidopsis nuclear genes using cytidine deaminase fused to a TALE DNA-binding domain (TALECD). We used a single pair of TALEs with a novel TALE-repeat unit that can recognize all four DNA bases, especially to allow for variations in the third base of codons in homologous genes. This targeting strategy makes it possible to simultaneously base edit almost identical sites in multiple isoforms of a gene while suppressing off-target substitutions.

synthetic biology↗

bioRxiv2021

Dominant Awn Inhibitor, which encodes an ALOG protein in sorghum, suppresses awn formation in rice

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

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↗