Biology subjects

Yanagisawa, K.

Publications and source records attributed to Yanagisawa, K..

7 recordsLinked to original sources

bioRxiv2026

Genome-wide evidence for cryptic multi-generational introgression between invasive Bursaphelenchus xylophilus and native B. mucronatus

Hybridization between invasive and native species can generate novel genetic variation with ecological and evolutionary consequences, yet introgression often remains cryptic. We investigated natural hybridization between the invasive pinewood nematode Bursaphelenchus xylophilus, the causal agent of pine wilt disease, and native Bursaphelenchus mucronatus in Japan. Using multiplexed inter-simple sequence repeat genotyping by sequencing, we genotyped 342 field-collected nematodes and then conducted pedigree-calibrated analysis using HIest v2.0, based on 48 highly informative diagnostic single-nucleotide polymorphisms (SNPs), followed by ADMIXTURE, complementary NeighborNet and principal component analyses of a broader 2,756-SNP dataset. The results identified nine high-confidence hybrids (2.6%) across two consecutive years (2023-2024), including relatively balanced F1-like individuals and strongly parentally biased advanced backcross-like individuals, demonstrating recurrent, multigenerational introgression. Four hybrids (44.4%) were undetectable using a single-locus diagnostic marker, showing that advanced introgression can escape conventional surveillance. Chemotaxis assays detected no volatile-mediated pre-mating discrimination, whereas post-mating barriers were incomplete and asymmetric. Crosses with B. xylophilus as the maternal parent showed reduced hatchability, particularly with B. mucronatus mucronatus, while reciprocal crosses generally retained higher hatchability, although some showed reduced fecundity. These results provide a mechanistic basis for natural gene flow in a recent secondary contact zone. Because B. mucronatus kolymensis is widespread in Europe and produces viable hybrid offspring with B. xylophilus in both directions, pathogen establishment could create new opportunities for introgression. Our findings reveal cryptic, multigenerational introgression between an invasive forest pathogen and its native congener, highlighting the importance of genomic surveillance for forest disease management and invasion-risk assessment under climate change.

evolutionary biology↗

bioRxiv2025

COFFEE-PRESC: A fast pre-screening method using compound retrieval by pairwise positional relationship of representative fragments

Protein-ligand docking is one of the most widely used methods in structure-based virtual screening in the early stages of drug discovery. Its calculations require approximately one minute per compound, making exhaustive evaluation of ultra-large libraries containing billions of molecules computationally impractical. In this study, we propose COFFEE-PRESC (COmpound Filtering by Fragment pair-based Efficient Evaluation for PRE-SCreening), a fast, fragment-based pre-screening method. COFFEE-PRESC first docks fragments in a pre-constructed fragment set to the target protein and enumerates multiple favorable protein-fragment docking poses and then pairs them to consider pairwise positional relationship. The fragment set is composed of a small number of representative fragments that exhibit high similarity to many other fragments, enabling coverage of a large and diverse chemical space. Compounds that contain stuructures similar to fragment pairs are then retrieved through similarity-based searches. This retrieval methodology guarantees that the mutual positional relationship of the two matched fragments does not spatially collide. Finally, the retrieved compounds are evaluated using docking scores of the representative fragments and similarity values between the representative and individual fragments matched in the compound retrieval process. COFFEE-PRESC was 32-fold faster while higher accuracy than Spresso, a existing pre-screening tool, highlighting its potential for application to ultra-large compound library screening. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/692874v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@c9a5eborg.highwire.dtl.DTLVardef@abeddcorg.highwire.dtl.DTLVardef@18d0bfdorg.highwire.dtl.DTLVardef@10e1e33_HPS_FORMAT_FIGEXP M_FIG TOC Graphic C_FIG

bioinformatics↗

bioRxiv2025

Protocol for Membrane Permeability Prediction of Cyclic Peptides using Descriptors Obtained from Extended Ensemble Molecular Dynamics Simulations and Chemical Structures

Improving membrane permeability is crucial in cyclic peptide drug discovery. Although the approach based on molecular dynamics (MD) simulation is widely used, it is computationally expensive. Alternatively, machine learning can predict membrane permeability at negligible cost, but it requires a larger dataset. There are only 7991 experimental values of membrane permeability available at the newly developed database. Another challenge in predicting membrane permeability using machine learning arises from the unique stable conformation of each cyclic peptide, which is strongly related to membrane permeability but difficult to predict from chemical structure. Therefore, we developed a machine learning protocol using 3D descriptors obtained from MD simulations in addition to 2D descriptors obtained from chemical structure of cyclic peptides, respectively, to generate a universal model with a realistic computational cost. We targeted 252 peptides across four datasets and, to calculate their 3D descriptors, predicted their conformation outside the membrane, at the water/membrane interface, and in the membrane by MD simulations based on the replica exchange with solute tempering/replica exchange umbrella sampling method using 16 replicas. For machine learning, six different algorithms were used, ranging from simple methods such as ridge regression to more sophisticated methods such as XGBoost. The best prediction performance was obtained using XGBoost, with a Pearsons correlation coefficient R = 0.77 and root mean square error (RMSE) = 0.62. The important descriptors included those that describe the hydrophilicity and hydrophobicity of the peptide, conformational differences between inside and outside the membrane, and the degree of freedom of the peptide. We confirm the models ability to predict the membrane permeability of peptides that differ in chemical structure from the training data by predicting the external data consisting of 24 peptides and obtained R = 0.76, RMSE = 1.14. Furthermore, we extracted one of the four datasets of the training data, re-trained the model, and performed the prediction of the permeability coefficients of the extracted dataset. The results showed the models generic nature with R = 0.61 and RMSE = 0.74, when using position-specific (PS) 3D, and 2D descriptors. In such situations descriptors based on conformations obtained from MD are essential for the prediction.

biophysics↗

bioRxiv2024

Topological data analysis based on mixed-solvent molecular dynamics simulations enhance cryptic pocket detection

Some functional proteins undergo conformational changes to expose hidden binding sites when a binding molecule approaches their surface. Such binding sites are called cryptic sites and are important targets for drug discovery. However, it is still difficult to correctly predict cryptic sites. Therefore, we introduce a new method, CrypToth, for the precise identification of cryptic sites utilizing the persistent homology method. This method integrates topological data analysis and mixed-solvent molecular dynamics (MSMD) simulations. To identify hotspots corresponding to cryptic sites, we conducted MSMD simulations using six probes with different chemical properties: benzene, isopropanol, phenol, imidazole, acetonitrile, and ethylene glycol. Subsequently, we applied our topological data analysis method to rank hotspots based on the possibility of harboring cryptic sites. Evaluation of CrypToth using nine target proteins containing well-defined cryptic sites revealed its superior performance compared to recent machine-learning methods. As a result, in 7 out of 9 cases, hotspots associated with cryptic sites were ranked highest. CrypToth can explore hotspots on the protein surface favorable to ligand binding using MSMD simulations with six different probes and then identify hotspots corresponding to cryptic sites by assessing the proteins conformational variability using the topological data analysis. This synergistic approach facilitates the prediction of cryptic sites with high accuracy.

bioinformatics↗

bioRxiv2024

Comprehensive gene expression analysis of organoid-derived healthy human colonic epithelium and cancer cell line by stimulated with live probiotic bacteria

The large intestine has a dense milieu of indigenous bacteria, generating a complex ecosystem with crosstalk between individual bacteria and host cells. In vitro host cell modeling and bacterial interactions at the anaerobic interphase have elucidated the crosstalk molecular basis. Although classical cell lines derived from patients with colorectal cancer including Caco-2 cells are used, whether they adequately mimic normal colonic epithelial physiology is unclear. To address this, we performed transcriptome profiling of Caco-2 and Monolayer cells derived from healthy Human Colonic Organoid (MHCO) cultured hemi-anaerobically. Coculture with the anaerobic gut bacteria, Bifidobacterium longum subsp. longum differentiated the probiotic effects of test cells from those of physiologically normal intestinal and colorectal cancer cells. We cataloged non- or overlapping gene signatures where gene profiles of Caco-2 cells represented absorptive cells in the small intestinal epithelium, and MHCO cells showed complete colonic epithelium signature, including stem/progenitor, goblet, and enteroendocrine cells colonocytes. Characteristic gene expression changes related to lipid metabolism, inflammation, and cell-cell adhesion were observed in cocultured live Bifidobacterium longum and Caco-2 or MHCO cells. B. longum-stimulated MHCO cells exhibited barrier-enhancing characteristics, as demonstrated in clinical trials. Our data represent a valuable resource for understanding gut microbe and host cell communication.

ecology↗

bioRxiv2023

CycPeptMP: Enhancing Membrane Permeability Prediction of Cyclic Peptides with Multi-Level Molecular Features and Data Augmentation

Cyclic peptides are versatile therapeutic agents with many excellent properties, such as high binding affinity, minimal toxicity, and the potential to engage challenging protein targets. However, the pharmaceutical utilities of cyclic peptides are limited by their low membrane permeability--an essential indicator of oral bioavailability and intracellular targeting. Current machine learning-based models of cyclic peptide permeability show variable performance due to the limitations of experimental data. Furthermore, these methods use features derived from the whole molecule which are used to predict small molecules and ignore the unique structural properties of cyclic peptides. This study presents CycPeptMP: an accurate and efficient method for predicting the membrane permeability of cyclic peptides. We designed features for cyclic peptides at the atom-, monomer-, and peptide-levels, and seamlessly integrated these into a fusion model using state-of-the-art deep learning technology. Using the latest data, we applied various data augmentation techniques to enhance model training efficiency. The fusion model exhibited excellent prediction performance, with root mean squared error of 0.503 and correlation coefficient of 0.883. Ablation studies demonstrated that all feature levels were essential for predicting membrane permeability and confirmed the effectiveness of augmentation to improve prediction accuracy. A comparison with a molecular dynamics-based method showed that CycPeptMP accurately predicted the peptide permeability, which is otherwise difficult to predict using simulations.

bioinformatics↗

bioRxiv2022

The double-layered structure of amyloid-β assemblage on GM1-containing membranes catalytically promotes fibrillization

Alzheimers disease (AD) is associated with progressive accumulation of amyloid-{beta} (A{beta}) cross-{beta} fibrils in the brain. A{beta} species tightly associated with GM1 ganglioside, a glycosphingolipid abundant in neuronal membranes, promote amyloid fibril formation; therefore, they could be attractive clinical targets. However, the active conformational state of A{beta} in GM1-containing lipid membranes is still unknown. The present solid-state nuclear magnetic resonance study revealed a nonfibrillar A{beta} assemblage characterized by a double-layered antiparallel {beta}-structure specifically formed on GM1 ganglioside clusters. Our data show that this unique assemblage was not transformed into fibrils on GM1-containing membranes, but could promote conversion of monomeric A{beta} into fibrils, suggesting that a solvent-exposed hydrophobic layer provides a catalytic surface evoking A{beta} fibril formation. Our findings will offer structural clues for designing drugs targeting catalytically active A{beta} conformational species for the development of anti-AD therapeutics.

biophysics↗