Biology subjects

Kyoda, K.

Publications and source records attributed to Kyoda, K..

6 recordsLinked to original sources

bioRxiv2026

Immobilization-free chemotaxis analysis reveals the novel behavioral mode of leaving in Caenorhabditis elegans

Animals balance staying in a favorable environment with exploring new ones. In C. elegans chemotaxis, the process by which worms migrate toward an attractant has been extensively studied. However, what happens after they reach it remains largely unexplored, partly because conventional assays immobilize worms at the point of arrival. Here, we quantitatively analyzed chemotactic behavior upon reaching an attractive odor source using an immobilization-free chemotaxis assay. We observed that 62% animals left the isoamyl alcohol region after initially approaching it, a behavior we termed "leaving behavior." Quantitative analysis revealed that leaving behavior represents a distinct locomotor state compared with free-moving, high-concentration odor avoidance, and approach behavior. To test whether leaving behavior is related to olfactory adaptation, we analyzed mutants in adaptation-related genes. The proportion of leaving behavior was significantly increased in egl-4 loss-of-function mutants compared with wild-type animals, whereas arr-1 mutants showed no significant difference. These results suggest that egl-4 negatively regulates leaving behavior, suggesting a role for this kinase in stabilizing post-arrival behavioral states beyond its known function in olfactory adaptation. Our findings indicate that chemotaxis involves dynamic behavioral transitions even after reaching an attractant, consistent with an exploration-exploitation trade-off framework. Significance StatementBy eliminating immobilization from a conventional chemotaxis assay, this study reveals behaviors that are typically obscured after animals reach an attractant. In C. elegans, we identify a post-arrival behavioral mode, "leaving," in which animals move away from an attractive odorant (isoamyl alcohol) after initially approaching it. Leaving behavior is quantitatively distinct from free-moving behavior, high-concentration odor avoidance, and approaching behavior. We further show that the frequency of leaving behavior is increased in animals lacking the egl-4 gene. These findings extend chemotaxis analysis beyond the point of odor source arrival and suggest that the nervous system actively drives behavioral switching even after a goal is reached, broadening our understanding of how sensory circuits govern exploration-exploitation decisions.

animal behavior and cognition↗

bioRxiv2026

A Computational Framework for Extracting Mechanistic Hypotheses from Quantitative Data of Morphological Dynamics

Advances in live-cell imaging and image analysis have made it possible to quantitatively measure the spatiotemporal morphological dynamics of biological phenomena at scale. However, a general framework is still lacking for systematically extracting, from the resulting multivariate data, which relationships between phenotypic characters are mechanistically interpretable and which gene perturbations disrupt those relationships. Here, we propose a computational framework for extracting mechanistic hypotheses and candidate genes from quantitative data of morphological dynamics. First, we detect reproducible correlations between phenotypic characters in wild-type data and interpret them as mechanistic hypotheses in light of existing knowledge. Next, we perform outlier analysis on data obtained under gene perturbation and extract, as candidates, genes that selectively disrupt relationships between phenotypic characters maintained in the wild type. We further integrate the extracted relationships into a spatiotemporal network to provide an overview of how phenotypic characters are linked across the developmental process. As a proof of concept, we applied the framework to quantitative data on nuclear division dynamics during early embryogenesis in Caenorhabditis elegans and recovered relationships between phenotypic characters consistent with known mechanisms while prioritizing candidate genes. This framework provides a useful basis for efficiently generating testable mechanistic hypotheses from quantitative data of morphological dynamics. Author SummaryHow does a single cell give rise to a complex organism? Answering this question requires understanding not only which genes are active, but how the physical behavior of cells--their shapes, positions, and movements--is coordinated across time and space. Live-cell imaging now allows researchers to measure these morphological dynamics in quantitative detail, yet extracting biological meaning from the resulting large, high-dimensional datasets remains a challenge. Here we present a computational framework that addresses this challenge by treating correlations between quantitative morphological measurements as windows into the underlying biological machinery. Applied to the nematode Caenorhabditis elegans, a powerful model organism whose early development is exquisitely reproducible, our approach automatically identifies pairs of cellular measurements that reliably co-vary in normal embryos and interprets these relationships as reflecting shared biological mechanisms. When genes are inactivated one at a time and the resulting embryos deviate from the expected co-variation, those genes are flagged as candidates for the disrupted mechanism. In a systematic test using embryos in which 263 genes had been individually inactivated, the framework correctly prioritized genes with known roles in spindle positioning and cell polarity. By converting large-scale morphodynamic datasets into a network of testable mechanistic hypotheses, this framework offers a broadly applicable strategy for moving from quantitative phenotyping to mechanistic understanding across diverse biological systems.

Systems Biology↗

bioRxiv2026

Divergence of Cortical Force-Generating Mechanisms Underlies Differences in Spindle Behavior between C. elegans and C. inopinata

Microtubule-dependent pronuclear migration and mitotic spindle positioning are fundamental processes during the first embryonic division in many animals. In the one-cell embryo of Caenorhabditis elegans, these events are regulated by well-characterized pulling forces acting on astral microtubules, including cortical forces mediated by the G-GPR-LIN-5 dynein complex. Although the overall framework of these dynamics is conserved, recent studies have revealed substantial interspecies variation in their regulation. Here, we investigated nuclei and mitotic spindle behaviors in one-cell embryos of Caenorhabditis inopinata, the closest known relative of C. elegans, using live-cell imaging and functional perturbation. We found that C. inopinata embryos exhibit altered pronuclear migration, reduced anaphase spindle oscillations, and slower centrosome diffusion during telophase compared with C. elegans. These differences suggest weaker cortical pulling forces. Functional analyses using RNA interference showed that GPR retains its essential role in force generation, whereas the contribution of the microtubule depolymerizing kinesin KLP-7 is reduced in C. inopinata. Our results point to evolutionary changes in microtubule-regulated spindle dynamics, and provide insight into how conserved cellular processes can diversify through subtle changes in their underlying mechanisms.

cell biology↗

bioRxiv2022

PHi-C2: interpreting Hi-C data as the dynamic 3D genome state

Hi-C is a widely used assay for studying three-dimensional (3D) genome organization across the whole genome. Here, we present PHi-C2, a Python package supported by mathematical and biophysical polymer modeling, that converts an input Hi-C matrix data into the polymer models dynamics, structural conformations, and rheological features. The updated optimization algorithm to regenerate a highly similar Hi-C matrix provides a fast and accurate optimal solution compared to the previous version by eliminating a computational bottleneck in the iterative optimization process. Besides, we newly set up the availability on Google Colab workflow to run, easily change parameters and check the results in the notebook. Overall, PHi-C2 can be a valuable tool to mine the dynamic 3D genome state embedded in Hi-C data. Availability and ImplementationPHi-C2 as the phic Python package is freely available under the GPL license and can be installed from the Python package index. The source code is available from GitHub at https://github.com/soyashinkai/PHi-C2. Without preparing a Python environment, PHi-C2 can run on Google Colab (https://bit.ly/3rlptGI). Contactsoya.shinkai@riken.jp or sonami@riken.jp

bioinformatics↗

bioRxiv2020

Deep Collection of Quantitative Nuclear Division Dynamics Data in RNAi-treated Caenorhabditis elegans Embryos

Recent advances in bioimage informatics techniques have yielded quantitative data on multicellular dynamics from microscopy images of animal development. Several such data collections have been created for Caenorhabditis elegans embryos under various gene silencing conditions. However, because of the limited depth of the datasets, it is impractical to apply standard statistical methods to these collections. Here, we created a deep collection of quantitative data on nuclear division dynamics during the first three rounds of cell division in C. elegans embryos, in which 263 essential embryonic genes were silenced individually by RNA-mediated interference. The collection consists of datasets from 33 wild-type and 1142 RNAi-treated embryos, including five or more datasets for 189 genes. Application of a two-sample t-test identified 8660 reproducible RNAi-induced phenotypes for 421 phenotypic characters. Clustering analysis suggested 24 functional processes essential for early embryogenesis. Our collection is a rich resource for understanding animal development mechanisms. In BriefKyoda et al. used bioimage informatics techniques to create a deep collection of quantitative data on nuclear division dynamics in RNAi-treated C. elegans embryos for 263 essential embryonic genes. Statistical analysis identified 8660 reproducible RNAi phenotypes for 421 phenotypic characters. The collection is a rich resource for understanding animal development. HighlightsO_LIBioimage informatics quantified nuclear division dynamics in C. elegans embryos C_LIO_LIFrom RNAi-silenced embryos we collected 1142 data sets on 263 essential genes C_LIO_LIStatistical analysis identified 8660 reproducible RNAi phenotypes C_LIO_LIClustering analysis suggested 24 functional processes in C. elegans embryogenesis C_LI

developmental biology↗

bioRxiv2020

BD5: an open HDF5-based data format to represent quantitative biological dynamics data

BD5 is a new binary data format based on HDF5 (hierarchical data format version 5). It can be used for representing quantitative biological dynamics data obtained from bioimage informatics techniques and mechanobiological simulations. Biological Dynamics Markup Language (BDML) is an XML(Extensible Markup Language)-based open format that is also used to represent such data; however, it becomes difficult to access quantitative data in BDML files when the file size is large because parsing XML- based files requires large computational resources to first read the whole file sequentially into computer memory. BD5 enables fast random (i.e., direct) access to quantitative data on disk without parsing the entire file. Therefore, it allows practical reuse of data for understanding biological mechanisms underlying the dynamics.

bioinformatics↗