Biology subjects

Yamasaki, Y.

Publications and source records attributed to Yamasaki, Y..

11 recordsLinked to original sources

bioRxiv2026

A Col22a1-EGFP reporter reveals dynamic three-dimensional organization of the myotendinous junction

The myotendinous junction (MTJ) is a specialized extracellular matrix-rich interface that transmits contractile force generated by skeletal muscle to tendon, but its three-dimensional organization and dynamics remain poorly characterized. Here, we generate a Col22a1-EGFP knock-in mouse that enables direct visualization and quantitative analysis of MTJs from embryonic development to adulthood. Tissue-clearing and three-dimensional imaging reveal extensive variation in MTJ morphology and spatial distribution among skeletal muscles and between myofiber types. During embryonic MTJ formation, COL22A1-EGFP undergoes a striking transition from intracellular puncta within developing myofibers to extracellular accumulation at myofiber tips, associated with polarized Golgi organization. We further identify complex tendon protrusions that emerge during postnatal growth and connect tendon bodies with subsets of MTJs. These interfaces contain morphologically distinct Scx-lineage cells that express Col22a1, identifying a specialized tendon-associated population at the MTJ. Finally, muscle unloading and overload differentially alter COL22A1-EGFP signal intensity and area, respectively, demonstrating that the reporter provides a quantitative readout of MTJ responses to altered mechanical conditions. Thus, Col22a1-EGFP reveals previously unrecognized spatial, developmental, and adaptive features of the muscle-tendon interface and provides an experimental platform for investigating MTJ biology in vivo.

developmental biology↗

bioRxiv2026

An evolvable and functionally partitioned network underlies developmental remodelling in teleosts

Comparative embryology has revealed that early animal development is based on highly conserved genetic programs. Beyond embryogenesis however, animals undergo remarkably diverse post-embryonic developmental transitions such as metamorphosis. Whether these transitions are also organised into conserved genetic programs remains largely unexplored. To identify conserved genetic programs underlying post-embryonic development, we examine metamorphic remodelling in five teleost fishes, spanning 200 million years of evolution. By integrating comparative co-expression networks, evolutionary genomics, phylogenetic modelling, and functional data in zebrafish, we uncovered a conserved post-embryonic developmental network. This conserved network comprises components involved in core cellular processes, and involved in development and physiology, both of which are under the control of thyroid hormone during metamorphosis. Despite evolutionary conservation at the coding sequence level, this network exhibits significant turnover in gene family copy number following the teleost-specific whole-genome duplication as well as lineage-specific expansions and contractions. The variation in gene copy numbers is associated with macroevolutionary variation in a number of ecological and morphological traits, including swimming performance, and trophic level. Cross-species tissue expression, zebrafish single-cell transcriptomics, and zebrafish perturbation phenotypes placed the implicated genes in biological contexts relevant to these traits. Together, our results provide evidence of an ancient, functionally partitioned post-embryonic developmental network that has diversified throughout teleost evolution. The heterochronic variation in network function, changes in endocrine activity, and gene-family turnover provide potential routes through which a shared developmental architecture contributed to the evolution of phenotypic diversity in teleosts.

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↗

bioRxiv2026

Cardiac-Detargeted MyoAAV Enables Systemic Nrl-Mediated Fast Myofiber Remodeling and Hypertrophy Across Multiple Skeletal Muscles

Inducing fast myofiber programs offers therapeutic potential for skeletal muscle disorders such as sarcopenia, where fast myofibers are preferentially lost. Engineered muscle-specific AAV (MyoAAV) vectors enable efficient transduction of skeletal muscles after systemic administration; however, cardiac transgene expression limits applications requiring skeletal muscle-selective delivery. We generated modified MyoAAV vectors by incorporating cardiac-specific miR-208a target sequences into the transgene 3'UTR. This design markedly suppressed cardiac expression while preserving skeletal muscle output, with target-site variation enabling tunable trade-offs between cardiac detargeting and skeletal muscle expression levels. We validated this platform using neural retina leucine zipper (Nrl), a large Maf transcription factor regulating type IIb myofiber identity. Systemic delivery of conventional MyoAAV-Nrl caused severe cardiac hypertrophy and uniform lethality within one month. Conversely, incorporating miR-208a target sequences prevented detectable hypertrophy and eliminated mortality during the experimental observation period. This modification significantly reduced cardiac Nrl expression while maintaining skeletal muscle levels, successfully promoting type IIb myofiber formation and hypertrophy across multiple skeletal muscles. These findings demonstrate that miR-208a-mediated cardiac detargeting combined with MyoAAV-Nrl enables safe systemic induction of fast myofiber remodeling and hypertrophy, establishing a platform for gene therapies targeting skeletal muscle disorders associated with fast myofiber loss.

cell 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

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

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↗

bioRxiv2021

Stochastic variational variable selection for high-dimensional microbiome data

BackgroundThe 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 framework 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. ResultsWe proposed a novel framework to improve the performance of the widely used DMM approach by combining three ideas: (i) we extended the finite DMM model to an infinite case by considering Dirichlet process mixtures and estimating the number of clusters as a random variables; (ii) we proposed an indicator variable to identify representative operational taxonomic units that substantially contribute to the differentiation among clusters; and (iii) to address the computational burden of high-dimensional microbiome data, we proposed a stochastic variational inference, which approximates the posterior distribution using a controllable distribution called variational distribution, and stochastic optimization algorithms for fast computation. 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. ConclusionsSVVS demonstrated 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 1,000 samples. Furthermore, recent microbiome studies have suggested that selection of the microbial species used as a core set is important.

bioinformatics↗

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↗