Biology subjects

Masuya, H.

Publications and source records attributed to Masuya, H..

8 recordsLinked to original sources

bioRxiv2026

ELAplus: Fast and accurate analysis platform for energy landscape analysis facilitated by fine-tuning optimization algorithm.

1. Large-scale community-composition datasets, especially from microbiome studies, increasingly provide opportunities to identify major community compositional types (e.g., enterotypes in human microbiomes) and potential transitions depending on environmental factors. Energy landscape analysis based on maximum entropy models has emerged as a promising framework for characterizing such multi-stability in ecological communities. However, its application to diverse, high-dimensional compositional datasets remains limited by computational inefficiency, insufficient evaluation of predictability, and lack of systematic assessment of uncertainty. 2. Here, we present a computationally tractable inference framework for energy landscape analysis of multispecies communities, implemented in the R package ELAplus. We introduce a framework combining cross-validation-based selection of optimization settings, enabling accurate and computationally efficient model fitting across a wide range of simulated community datasets. In addition, we incorporate a bootstrap-based approach to quantify the reliability of inferred stable states, providing a systematic measure of uncertainty in landscape structures. 3. Simulation analyses demonstrate improved predictive performance and robustness compared to existing implementations. Applications to empirical datasets further illustrate how the framework can reveal stable states, basins of attraction, and potential tipping points under varying environmental conditions. The package also provides visualization tools, including disconnectivity graphs and energy surface plots, to facilitate intuitive interpretation of complex ecological landscapes. 4. Our framework enables robust and computationally efficient inference of ecological stability from compositional and environmental data, expanding the applicability of energy landscape approaches in diverse natural communities.

ecology↗

bioRxiv2026

Major microbiota transitions across liver disease progression are mediated by key taxa linking host environments and microbial communities

The gut microbiome plays a critical role in chronic liver disease, yet quantitative methods that integrate microbial community structure with clinical indices remain limited. Here, we applied an energy landscape analysis (ELA)-based framework to characterize transitions in gut microbiota along a clinical gradient of liver disease severity, as measured by the FibroScan-AST (FAST) score. The analysis revealed characteristic community states corresponding to low, intermediate, and high FAST scores, indicating that liver disease progression is associated with structured transitions in microbial community organization rather than simple changes in overall diversity. To further dissect community-level organization, we developed the community shaping index (CSI), which quantifies how strongly individual taxa are associated with specific community structures. Integrating CSI with species-environment association parameters identified key taxa with distinct ecological roles, including unclassified Subdoligranulum taxon, an unclassified Ruminococcus gnavus group taxon, and Streptococcus salivarius, that link host environmental conditions with community composition. Exploratory causal discovery suggested that Streptococcus salivarius and the unclassified Subdoligranulum may play distinct, potentially active roles in linking liver health with gut microbial organization, but with contrasting associations: S. salivarius was linked to disease-related liver deterioration and dysbiotic states, whereas the unclassified Subdoligranulum was associated with healthier liver function and coherent community organization. Overall, this ecologically grounded framework provides a unified and mechanistic foundation for analyzing microbiome structure and disease-associated transitions.

ecology↗

bioRxiv2025

Population genomic scan of endogenous retrovirus insertions revealed cryptic drivers of selective sweep in wild house mouse genomes

Endogenous retroviruses (ERVs) represent a major source of structural variation in mammalian genomes, yet their diversity in wild populations remains poorly understood. Here, we conduct a comprehensive genome-wide survey of non-reference ERV insertions in wild house mice (Mus musculus) to characterize their distribution and evolutionary dynamics. Using a newly developed bioinformatics pipeline, we detected and annotated over 100,000 non-reference ERV insertions from short-read sequencing data across 163 wild mouse genomes. Our analyses revealed marked differences in ERV insertion patterns among subspecies and populations, including variation in genomic localization and population-specific polymorphisms. These heterogeneous patterns suggest distinct evolutionary histories and host-retrovirus interactions across populations. For instance, we describe the distribution of the ERV-derived Fv4 locus, which shows subspecies-restricted occurrence and confers resistance to murine leukemia viruses (MLVs). Several lines of evidence showed that the spread of Fv4 insertions in Korean population has been driven by adaptive introgression from neighboring populations. Our study provides the first large-scale population genomic scan of ERV diversity in wild house mice. By cataloguing extensive polymorphism in non-reference ERV insertions, our results highlight the role of ERVs as dynamic genomic elements that contribute to structural variation and adaptive evolution. Article SummaryEndogenous retroviruses (ERVs) are viral sequences embedded in animal genomes that can create structural genetic variation. In this study, we conducted a genome-wide survey of non-reference ERV insertions in 163 wild house mice using short-read sequencing data and a newly developed computational pipeline. We identified more than 100,000 polymorphic ERV insertions and found substantial differences among subspecies and geographic populations. One example, the ERV-derived Fv4 locus, illustrates how ERV variation can influence the genetic pattern of polymorphisms in the species. These results demonstrate that ERVs are dynamic genomic elements that contribute to population divergence and adaptive evolution.

evolutionary biology↗

bioRxiv2023

Exploring Disease Model Mouse Using Knowledge Graphs: Combining Gene Expression, Orthology, and Disease Datasets

BackgroundThe RIKEN BRC develops and maintains the RIKEN BioResource MetaDatabase to help users explore appropriate target bioresources for their experiments and prepare precise and high-quality data infrastructures. The Swiss Institute of Bioinformatics develops two RDF datasets across multi species for the study of gene expression and orthology: Bgee and Orthologous MAtrix (OMA, an orthology database). MethodsThis study integrates the RIKEN BioResource knowledge graph with Resource Description Framework (RDF) datasets from Bgee, a gene expression database, the OMA, the DisGeNET, a human gene-disease association, Mouse Genome Informatics (MGI), UniProt, and four disease ontologies in the RIKEN BioResource MetaDatabase. Our aim is to explore which model organisms are most appropriate for specific medical science research applications, using SPARQL queries across the integrated datasets. More precisely, we attempt to explore disease-related genes, as well as anatomical parts where these genes are overexpressed and subsequently identify appropriate bioresource candidates available for specific disease research applications. ResultsWe illustrate the above through two use cases targeting either Alzheimers disease or melanoma. We identified two Alzheimers disease-related genes that were overexpressed in the prefrontal cortex (APP and APOE) and 21 RIKEN bioresources predicted to be relevant to Alzheimers disease research. Furthermore, executing a transitive search for the Uberon terms by using the Property Paths function, we identified two melanoma-related genes (HRAS and PTEN), and eight anatomical parts in which these genes were overexpressed, such as the "skin of limb" as an example. Finally, we compared the performance of the federated SPARQL query via the remote Bgee SPARQL endpoint with the performance of a centralized SPARQL query using the Bgee dataset as part of the RIKEN BioResource MetaDatabase. ConclusionsAs a result, we demonstrated that the performance of the federated approach degraded. We concluded that we improved the degradation of the query performance of the federated approach from the BioResource MetaDatabase to the SIB by refining the transferred data through the subquery and enhancing the server specifications, that is optimizing the triple store query evaluation.

bioinformatics↗

bioRxiv2023

Ontology for Cellular Senescence Mechanisms

Although cellular senescence is a key factor in organismal aging, with both positive and negative effects on individuals, its mechanisms remain largely unknown. Thus, integrating knowledge is essential to explain how cellular senescence manifests in tissue damage and age-related diseases. Here, we propose an ontological model that organizes knowledge of cellular senescence in a computer-readable form. We manually annotated and defined cellular senescence processes, molecules, anatomical structures, phenotypes, and other entities based on the Homeostasis Imbalance Process ontology. We described the mechanisms as causal relationships of processes and modeled a homeostatic imbalance between stress and stress response in cellular senescence for a unified framework. HoIP was assessed formally, and the relationships between cellular senescence and disease were inferred for higher-order knowledge processing. We visualized cellular senescence processes to support knowledge utilization. Our study provides a knowledge base to help elucidate mechanisms linking cellular and organismal aging.

cell biology↗

bioRxiv2022

Decomposing predictability to identify dominant causal drivers in complex ecosystems

Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time-series based causal inferences. Here we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded new but interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems. Significance StatementEffective use of ecosystem monitoring data to resolve global environmental issues is a major challenge of the 21st century ecology. A promising solution to address this challenge is a time-series-based causal analysis which can provide insight on the mechanical links between ecosystem components. In this work, a model-free framework named EcohNet is proposed. EcohNet utilizes ensemble predictions of echo state networks, which are known to be fast, accurate, and highly relevant for a variety of dynamical systems, and can robustly predict causal networks of ecosystem components. It also can provide an optimized forecasting of overall ecosystem components, and could be used to analyze complex and hybrid multivariate time series in many scientific areas, not limited to ecosystems.

ecology↗

bioRxiv2020

Integrated Collection of Stem Cell Bank data, a data portal for standardized stem cell information

The last decade has witnessed an extremely rapid increase in the number of newly established stem cell lines. However, due to the lack of a standardized format, data exchange among stem cell line resources has been challenging, and no system can search all stem cell lines across resources worldwide. To solve this problem, we have developed Integrated Collection of Stem Cell Bank data (ICSCB) (http://icscb.stemcellinformatics.org/), the largest database search portal for stem cell line information, based on the standardized data items and terms of the MIACARM framework. Currently, ICSCB can retrieve >16,000 cell lines from four major data resources in Europe, Japan, and the United States. ICSCB is automatically updated to provide the latest cell line information, and its integrative search helps users collect cell line information for over 1,000 diseases including many rare diseases worldwide, which has been a formidable task, thereby distinguishing itself from other database search portals.

bioinformatics↗

bioRxiv2019

Energy landscape analysis of ecological communities elucidates the phase space of community assembly dynamics

Compositional multistability is widely observed in multispecies ecological communities. Since differences in community composition often lead to differences in community function, understanding compositional multistability is essential to comprehend the role of biodiversity in maintaining ecosystems. In community assembly studies, it has long been recognized that the order and timing of species migration and extinction influence structure and function of communities. The study of multistability in ecology has focused on the change in dynamical stability across environmental gradients, and was developed mainly for low-dimensional systems. As a result, methodologies for studying the compositional stability of empirical multispecies communities are not well developed. Here, we show that models previously used in ecology can be analyzed from a new perspective - the energy landscape - to unveil compositional stability in observational data. To show that our method can be applicable to real-world ecological communities, we simulated assembly dynamics driven by population level processes, and show that results were mostly robust to different simulation assumptions. Our method reliably captured the change in the overall compositional stability of multispecies communities over environmental change, and indicated a small fraction of community compositions that may be channels for transitions between stable states. When applied to murine gut microbiota, our method showed the presence of two alternative states whose relationship changes with age, and suggested mechanisms by which aging affects the compositional stability of the murine gut microbiota. Our method provides a practical tool to study the compositional stability of communities in a changing world, and will facilitate empirical studies that integrate the concept of multistability from different fields.

ecology↗