Biology subjects

Hirai, M. Y.

Publications and source records attributed to Hirai, M. Y..

8 recordsLinked to original sources

bioRxiv2026

From photoprotection to growth: shifts in high-light acclimation strategies associated with molecular evolutionary rates in duckweeds

Molecular evolutionary rates vary widely among lineages, yet the biological processes generating this variation remain poorly understood. In plants, which lack an early-segregated germline, growth- and environment-dependent physiology could itself influence mutation accumulation and, ultimately, substitution rates. Here we examine this possibility in duckweeds (Araceae, Lemnoideae), whose closely related lineages differ severalfold in sequence divergence. Nonsynonymous and synonymous divergence scaled proportionally across all six lineages examined, indicating that this heterogeneity does not reflect differences in selective constraint and instead points to variation in mutation rate. Lineages without prominent anthocyanin accumulation occupied lower latitudes and, under high light, sustained higher relative growth rates and higher photosynthetic efficiency. Comparing the early-diverging, anthocyanin-accumulating Spirodela polyrhiza with the late-diverging, anthocyanin-free Wolffia australiana revealed contrasting acclimation strategies: S. polyrhiza preferentially induced photoprotective pathways, including anthocyanin biosynthesis and non-photochemical quenching, whereas W. australiana instead expanded mitochondrial oxidative phosphorylation together with the cytosolic translational machinery, and broadly remodeled primary metabolism. Duckweed lineages therefore differ in how they balance photoprotection against photochemical energy use and downstream metabolic capacity, a divergence that may be associated with their lineage-specific molecular evolutionary rates.

evolutionary biology↗

bioRxiv2026

LBD-type transcription factors suppress local and systemic nitrogen responses through distinct regulatory pathways

Nitrogen (N) is a major determinant of plant growth and productivity. Because soil N availability and internal N demand fluctuate, plants have evolved sophisticated mechanisms to coordinate N acquisition and utilization at the whole-plant level. However, how this coordination is achieved remains poorly understood. Here, we show that N-inducible LATERAL ORGAN BOUNDARIES DOMAIN transcription factors LBD37, LBD38, and LBD39 (LBDs) function as repressors of local N uptake and assimilation and systemic N-demand signaling in Arabidopsis. Triple mutants lacking these three LBDs displayed enhanced nitrate influx and increased accumulation of nitrate, amino acids, and total N. Transcriptome analysis identified an array of N-starvation- and nitrate-inducible genes derepressed in shoots and roots, including C-TERMINALLY ENCODED PEPTIDE (CEP) and CEP DOWNSTREAM (CEPD) genes, as well as genes involved in N uptake and assimilation. Grafting and genetic analyses revealed that LBDs gate the systemic N-demand signaling relay by repressing CEP and CEPD expression organ-autonomously. We also found that LBDs locally repress genes involved in N uptake and assimilation through a distinct regulatory mechanism. We propose that LBDs are key transcriptional repressors in a regulatory framework for optimizing N acquisition and utilization under fluctuating N conditions at the whole-plant level.

plant 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

Stable isotope-assisted computational mass spectrometry reveals root-specific alkaloids in Glycyrrhiza species

Licorice (Glycyrrhiza) is a medicinal plant widely used in approximately 70% of traditional Japanese Kampo formulations and is known to produce a wide array of specialized metabolites with diverse pharmacological properties. Although hundreds of metabolites have been reported, the overall chemical diversity of Glycyrrhiza remains poorly characterized. Here, using mass spectrometry data obtained from fully 13C-labeled leaves and roots of Glycyrrhiza uralensis and Glycyrrhiza glabra, we determined the carbon number, followed by molecular formula and substructure prediction in combination with MS/MS similarity-based molecular networking. After excluding redundant ions, including isotopic peaks, adducts, and in-source fragments, we extracted 3,060 unique metabolite features with assigned carbon numbers. Among these, substructure information was assigned to 1,015 features (33%) across the four plant tissues, revealing the tissue-specific metabolome profiles. Furthermore, we discovered five previously unreported alkaloids, homopipecolic acid-conjugated flavonoids, in the roots of G. uralensis and G. glabra, and Glycine max, another member of the Fabaceae family. Two of these structures were validated using nuclear magnetic resonance spectroscopy. We further proposed a biosynthetic route involving a spontaneous reaction between 1-piperideine and malonyl glycoside substrates and confirmed the formation of the conjugated product using authentic standards.

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

Multiomics-based assessment of the impact of airflow on diverse plant callus cultures

Plant cell culture has multiple applications in biotechnology and horticulture, from plant propagation to the production of high-value biomolecules. However, the interplay between cellular diversity and ambient conditions influences the metabolism of cultured tissues; understanding these factors in detail will inform efforts to optimize culture conditions. This study presents multiomics datasets from callus cultures of tobacco (Nicotiana tabacum), rice (Oryza sativa), and two bamboo species (Phyllostachys nigra and P. bambusoides). Over four weeks, calli were cultured under continuous moisture without airflow or gradually reduced ambient moisture with airflow. For each sample, gene expression was profiled with high-throughput RNA sequencing, 442 metabolites were measured using liquid chromatography (LC) with triple-quadrupole mass spectrometry (LC-QqQMS), and 31 phytohormones were quantified using ultra-performance LC coupled with a tandem quadrupole mass spectrometer equipped with an electrospray interface (UPLC-ESI-qMS/MS) and ultra-high-performance LC- orbitrap MS (UHPLC-Orbitrap MS). These datasets highlight the impact of airflow on callus cultures, revealing differences between and within species, and provide a comprehensive resource to explore the physiology of callus growth.

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

Intronic TNR-retained ISOPROPYLMALATE ISOMERASE LARGE SUBUNIT1 transcripts impair leaf development in Arabidopsis

Intronic trinucleotide repeat (TNR) is widely distributed in plant genomes. In Arabidopsis accession Bur-0, abnormally expanded TTC repeat in intron-3 of the ISOPROPYLMALATE ISOMERASE LARGE SUBUNIT1 (IIL1) gene causes growth defects called the irregularly impaired leaves (iil) phenotype, triggered by DNA methylation-mediated IIL1 gene silencing at elevated temperature. However, little is known about how the reduced expression of IIL1 causes the iil phenotype. We demonstrated that the iil phenotype was resulted from the relative increase of intron-3-retained IIL1 transcripts through the experiments where the iil phenotype was reproduced by introducing the IIL1 gene harboring 100 copies of TTC repeat into Col-0. The iil phenotype appeared when the total amount of the IIL1 transcripts was decreased by co-suppression and the percentage of intron-3-retained IIL1 transcripts was increased. The IIL1 gene encodes an isopropylmalate isomerase large subunit, forming heterodimers with small subunits (AtLeuD1, AtLeuD2, or AtLeuD3). In the myb28 myb29 mutant lacking AtLeuD1 and AtLeuD2, the iil phenotype was almost completely suppressed regardless of higher percentage of intron-3-retained IIL1 transcripts. The results indicated that the iil phenotype was associated with interaction with AtLeuDs, suggesting that intronic TNR-containing transcripts were translated into abnormal proteins and perturbed the metabolic pathway supporting the leaf development.

plant biology↗