Biology subjects

Tsujimoto, H.

Publications and source records attributed to Tsujimoto, H..

15 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

Genotype-first approach revealed unrecognized breed-specific genetic diseases in dogs

Numerous dogs are affected by genetic diseases because of their unique breeding situation. The identification of pathogenic variants in dogs not only has veterinary benefits but also leads to the development of new human disease models. However, fewer pathogenic variants have been identified in dogs than in humans, and standard genetic approaches, such as large-scale case/control studies and family analyses, often fail to identify novel causative variants in dogs. To address this, we applied a genotype-first approach by developing a multiplex polymerase chain reaction (PCR)-based targeted sequencing method for dogs, analyzing 203 genes--most of which are related to human disease--from 6,333 dogs collected from a veterinary hospital, and characterizing pathogenic variant carriers. In total, 120 pathogenic variants in 83 genes were identified. Frequent pathogenic variants were enriched in specific dog breeds. Dogs homozygous for a pathogenic CHEK2 variant, frequently identified in French Bulldogs (5.74%, homozygous), showed a five-fold increased risk of cancer (P = 3.45 x 10-2). A pathogenic NOD2 variant was detected in half of the Italian Greyhounds. However, their medical records did not document the typical clinical symptoms observed in humans with NOD2 deficiency. Some pathogenic variants were present in three or fewer dogs, and 11.0% of these dogs exhibited symptoms potentially associated with gene defects. These rare variants could become common within a short period due to the canine breeding system to induce breed-specific genetic diseases. These findings suggest that the genotype-first approach successfully identified unrecognized breed-specific genetic diseases. Further characterization of dogs with pathogenic variants enable to define clinical diagnosis, providing an important spontaneous model for human genetic diseases.

genomics↗

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

Photodynamic Therapy for Deep Organ Cancer by Implantable Wireless OLEDs

Metronomic photodynamic therapy (mPDT) is a method of continuously delivering low-intensity light to a cancer lesion. Since high light intensity is not required, the light device can be miniaturized and implanted in the body. Nevertheless, for clinical implementation, it is essential to develop a light source apparatus that can effectively illuminate the entire tumor and operate stably without damaging internal organs. To address this issue, an organic light-emitting diode (OLED) optical device that has an area large enough to cover the entire lesion and is powered by a wireless power supply has been developed. The device is thin and lightweight and it functioned stably in the abdominal cavity of rats for more than two weeks without causing foreign body reactions. By covering the tumor site of rat liver cancer with this device and continuously illuminating the tumor with appropriate administration of a photosensitizer, the entire tumor under the emitting surface was eliminated through apoptosis without leaving any residual tumor margins. This is the first report of a robust antitumor effect on parenchymal organ cancers by mPDT using a biocompatible, wirelessly powered OLED device, which represents a potential new deep organ cancer therapy.

bioengineering↗

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↗

bioRxiv2023

An 11-point time course midgut transcriptome across 72 h after blood feeding provides detailed temporal resolution of transcript expression in the arbovirus vector, Aedes aegypti

As the major vector for dengue, Zika, yellow fever, and chikungunya viruses, the mosquito Aedes aegypti is one of the most important insects in public health. These viruses are transmitted by bloodfeeding, which is also necessary for the reproduction of the mosquito. Thus, the midgut plays an essential role in mosquito physiology as the center for bloodmeal digestion and as an organ that serves as the first line of defense against viruses. Despite its importance, transcriptomic dynamics with fine temporal resolution across the entire digestion cycle have not yet been reported. To fill this gap, we conducted a transcriptomic analysis of Ae. aegypti female midgut across a 72-h bloodmeal digestion cycle for 11 time points with a particular focus on the first 24 h. PCA analysis confirmed that 72 h is indeed a complete digestion cycle. Cluster and GO enrichment analysis showed the orchestrated modulation of thousands of genes to accomplish the midguts role as the center for digestion as well as nutrient transport with a clear progression with sequential emphasis on transcription, translation, energy production, nutrient metabolism, transport, and finally autophagy by 24-36hr. We further determined that many serine proteases are robustly expressed as if to prepare for unexpected physiological challenges. This study provides a powerful resource for the analysis of genomic features that coordinate the rapid and complex transcriptional program induced by mosquito bloodfeeding.

genetics↗

bioRxiv2022

NF-κB Decoy ODN-Loaded Poly lactic-co-glycolic Acid Nanospheres Inhibit Alveolar Ridge Resorption

Residual ridge resorption combined with dimensional loss resulting from tooth extraction has a prolonged correlation with early excessive inflammation. Nuclear factor-kappa B (NF-{kappa}B) decoy oligodeoxynucleotide (ODN) is a member of a group of double-stranded DNA capable of downregulating the expression of downstream genes of the NF-{kappa}B pathway. The healing action of its embellished effect combined with poly(lactic-co-glycolic acid) (PLGA) nanospheres on tooth extraction socket still remains unknown. Hence, the aim of this study was to investigate the therapeutic effect of NF-{kappa}B decoy ODN-loaded PLGA nanospheres (PLGA-NfD) transfected into extraction sockets in Wistar/ST rats. Micro-computed tomography and trabecular bone analysis following treatment with PLGA-NfD demonstrated inhibition of vertical alveolar bone loss with increased bone volume, smoother trabecular bone surface, thicker trabecular bone, larger trabecular number and separation, and fewer bone porosities. Histomorphometric and reverse transcription-quantitative polymerase chain reaction analysis revealed reduced tartrate-resistant acid phosphatase-expressing osteoclasts, interleukin-1{beta}, tumor necrosis factor-, receptor activator of NF-{kappa}B ligand, turnover rate and increased transforming growth factor-{beta}1 immunopositive reactions and relative gene expressions. These data demonstrate that local delivery of PLGA-NfD could be used as a substantial suppressor of inflammation during the healing process in a tooth extraction socket, with the potential of accelerated new bone formation.

immunology↗

bioRxiv2022

Function and evolution of aquaporin IsAQP1 in the Lyme disease vector Ixodes scapularis

Ticks are important vectors of pathogenic viruses, bacteria, and protozoans to humans, wildlife, and domestic animals. Due to their life cycle, ticks face significant challenges related to water homeostasis. When blood-feeding they must excrete water and ions, but when off-host (for stretches lasting several months) they must conserve water to avoid desiccation. Aquaporins (AQPs), a family of membrane-bound water channels, are key players in osmoregulation in many animals but remain poorly characterized in ticks. Here, we bioinformatically identified AQP-like genes from the deer tick Ixodes scapularis and used phylogenetic approaches to map the evolution of the aquaporin gene family in arthropods. We find that most arachnid AQP-like sequences (including those of I. scapularis) form a monophyletic group clustered within aquaglycerolporins (GLPs) from bacteria to vertebrates. This gene family is absent from insects, revealing divergent evolutionary paths for AQPs in different hematophagous arthropods. We next sequenced the full-length cDNA of I. scapularis aquaporin 1 (IsAQP1) and expressed it heterologously in Xenopus oocytes to functionally characterize its permeability to water and solutes. We additionally examined IsAQP1 expression across different life stages and adult female tissues. We found IsAQP1 to be an efficient water channel with salivary gland-specific expression, implying its function may be closely tied to osmoregulation during blood-feeding. Its functional properties were unique: unlike most GLPs, IsAQP1 has low glycerol permeability, and unlike most AQPs, it is insensitive to mercury. Together, our results suggest IsAQP1 plays an important role in tick water balance during blood feeding and may hold promise as a target for novel vector control efforts. Graphical TOC/Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY eTOCIsAQP1, an aquaporin found in the black legged tick (Ixodes scapularis), is a functional water channel with low permeability to glycerol. In a phylogenetic analysis of 176 sequences from 48 arachnid species, most tick and other arachnid AQP-like genes formed a monophyletic clade nested within aquaglyceroporins (GLPs). IsAQP1 shows salivary-gland specific expression in adult females--with highest expression in unfed females--suggesting a role in water balance during blood feeding.

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↗

bioRxiv2019

Highly reliable, targeted photothermal cancer therapy combined with thermal dosimetry using indocyanine green lactosome

Indocyanine green (ICG) is a near-infrared light-absorbing substance. Thus, when a tumor in which ICG has accumulated is irradiated with a near-infrared (NIR) laser, only the tumor can be heated by a photothermal reaction. We developed ICG lactosome, a novel drug delivery system (DDS) composed of polymeric micelles and ICG that shows selective accumulation in tumor based on an enhanced permeability and retention (EPR) effect. We showed that ICG lactosome accumulated in a tumor by using an intradermal tumor mouse model of a murine colon cancer cell line (Colon26) transfected with Nano lantern luminescent protein (NLC26). Two days after the administration of ICG lactosome, the tumor was irradiated with an 808-nm diode-laser while monitoring tumor temperature. The results showed that the treated tumors were cured when the peak of tumor temperature during NIR irradiation reached 43{degrees}C or higher. To verify these results, photothermal therapy (PTT) using ICG lactosome was carried out using a newly developed system that can control the temperature at the NIR irradiation site at a constant level. All of the tumors that had been kept at 43{degrees}C during irradiation were cured, while 2 of 5 tumors that had been kept at 42{degrees}C were not cured, and none of tumors that had been kept at a temperature below 41{degrees}C were cured. ICG lactosome-assisted PTT combined with thermal dosimetry is a highly reliable method for cancer treatment and may afford further clinical opportunities for PTT.

bioengineering↗

bioRxiv2018

Comprehensive analysis of miRNA and protein profiles within exosomes derived from canine lymphoid tumour cell lines

Exosomes are small extracellular vesicles released from almost all cell types, which play roles in cell-cell communication. Recent studies have suggested that microenvironmental crosstalk mediated by exosomes is an important factor in the escape of tumour cells from the anti-tumour immune system in human haematopoietic malignancies. Here, we conducted comprehensive analysis of the miRNA and protein profiles within the exosomes released from four canine lymphoid tumour cell lines as a model of human lymphoid tumours. The results showed that the miRNAs and proteins abundantly contained in exosomes were similar among the four cell lines. However, the profiles of miRNA within exosomes differed among the cell lines and reflected the expression pattern of miRNAs of the parent cells. In the comparison of the amounts of miRNAs and proteins among the cell lines, those of three miRNAs (miR-151, miR-8908a-3p, and miR-486) and CD82 protein differed between exosomes derived from vincristine-sensitive and resistant cell lines. Further investigations are needed to elucidate the biological functions of the exosomal contents in the microenvironmental crosstalk of lymphoid tumours.

cancer biology↗