Biology subjects

Matsui, Y.

Publications and source records attributed to Matsui, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

GlyEnzEA: reaction-informed enrichment of enzyme-centered glycan sets from glycomics data

Enzyme-centered interpretation of glycomic changes requires testing whether glycans associated with particular reactions change collectively. Motif analyses and reaction databases do not directly provide enzyme-specific sets of measured glycans, and structural ambiguity or overlapping reactions can give different enzymes identical sets. A framework is needed to construct these sets and define distinguishable test hypotheses. GlyEnzEA constructs sets by matching reaction rules to candidate glycan topologies, groups enzymes sharing identical sets, and tests each set once. In an Mgat3-knockout mouse glycome, 48 enzymes yielded 16 sets; only the MGAT3 set showed significant negative enrichment after correction (Storey q=0.033). Across seven HEK293 perturbations, target sets were significant in two conditions: MGAT1 and class-I mannosidase knockouts. GlyEnzEA supports enzyme-centered interpretation at the resolution of distinguishable enzymes or enzyme groups.

bioinformatics↗

bioRxiv2026

When can site-specific glycoform compositions resolve biosynthetic rates?

Mass spectrometry measures site-specific glycoform compositions, but when these measurements can resolve upstream biosynthetic rates remains unclear. We assess whether small changes in effective rates, each representing the processing capacity of a group of biosynthetic reactions, can be distinguished near specified baseline rates with secretion held fixed. Because proportions sum to one, m jointly normalised glycoform classes provide at most m-1 independent quantities. Distinguishing changes in k rates therefore requires at least k+1 classes, although this count alone does not ensure identifiability. In a canonical N-glycan network with eight rates, exhaustive evaluation of nine-class panels identified panels that could locally distinguish all eight rates. Among these panels, class choice changed the allowable measurement error by approximately 18-fold for the same target uncertainty in inferred rates. A separate simulation within the fixed network showed that estimating rate-change combinations with weaker composition responses reduced bias but amplified measurement noise. We applied the framework to plasma data from 40 controls and 70 patients across 14 inherited glycosylation-disorder groups. A prespecified composition score for the expected pathway disturbance ranked among the top two in 13 of 14 groups. Network responses linked observed composition shifts to biosynthetic reaction classes. Expanding site models from five to eight reaction classes improved control-composition fit but reduced the number of sites at which all modelled rates were locally distinguishable and increased noise sensitivity. Rate inference thus requires sufficient panel size, separable rate responses, adequate measurement precision and a model that fits the composition. These requirements guide measurement design and help determine whether compositions support pathway-level interpretation or individual-rate inference.

systems biology↗

bioRxiv2026

GlycoMeSH: linking glycan structures to biomedical context for systematic enrichment analysis

Glycan identification has advanced, but glycan structures remain difficult to translate into reproducible biomedical context because reusable glycan-level annotations are sparse. We present GlycoMeSH, a resource that links glycans to Medical Subject Headings (MeSH) through an inference model, a traceable association database and a glycan-set enrichment workflow. GlycoMeSH-BERT recovered ~60% of literature-derived associations at recall@30 and expanded open-vocabulary MeSH coverage beyond closed-label baselines, without higher per-prediction accuracy. At matched candidate counts, its predictions showed motif-level semantic agreement comparable to those baselines, independently of the training labels. GlycoMeSH-DB contains 789,627 associations between 26,954 glycans and 20,302 MeSH terms. GlycoMeSH-EA returned enriched MeSH terms for glycan sets from glycomics and glycoproteomics datasets. Each association represents a biomedical context rather than a validated mechanism, and retains its source PMID or prediction score for audit. GlycoMeSH supplies the missing, evidence-traceable annotation layer that makes glycan sets directly analyzable by enrichment across glycoscience datasets.

bioinformatics↗

bioRxiv2026

OmicsLake: Versioned, Agent-Aware Data Lineage for R/Bioconductor Workflows

OmicsLake is an R package for version-aware, dataset-level lineage in R/Bioconductor exploratory workflows. It associates stored versions with dependencies captured within tracked ref()-dplyr-save_as() pipelines or supplied explicitly, and can attach caller-supplied human or software-agent identifiers. Controlled tests recovered all ground-truth edges within the supported path and enabled explicit recovery of an overwritten tagged intermediate. OmicsLake 0.99.3 is MIT-licensed software available at https://github.com/matsui-lab/OmicsLake (tag v0.99.3); source, tests, and evaluation files are archived at https://doi.org/10.5281/zenodo.21391428. Supplementary Material accompanies this preprint.

bioinformatics↗

bioRxiv2026

Real-time mass defect-driven prediction of glycopeptide precursors enables enrichment-free serum glycoproteomics

Glycopeptide enrichment remains a cornerstone in glycoproteomics, but bias and reproducibility issues continue to hinder biological insight and clinical translation. Employing curated glycoproteomics datasets and machine learning, we trained a glycopeptide classifier to recognize N-glycopeptide precursors through mass defect signatures. Integration of the classifier into a data-dependent acquisition framework facilitated real-time prediction of N-glycopeptides from human serum and revealed sex differences in murine plasma fucosylation opening avenues for enrichment-free glycoproteomics.

bioinformatics↗

bioRxiv2026

Secreted ORF8 reprograms macrophages to enhance SARS-CoV-2 infection of lung epithelial cells

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) primarily targets the respiratory epithelium, yet severe disease features diffuse lung injury and hyperinflammatory syndromes driven by dysregulated immune activation. Emerging evidence indicates that resident and infiltrating immune cells in the lung can encounter the virus early in infection and, under specific conditions, become infected (1, 2). This process amplifies local inflammation and facilitates viral propagation in the lower airways and distal lung regions (3, 4), where angiotensin converting enzyme 2 (ACE2) expression is limited (5, 6). However, how epithelial and immune cell compartments interact to produce the hallmark pulmonary pathology of SARS-CoV-2 infection remains unresolved. Here we show that secreted ORF8, a SARS-CoV-2 accessory protein, drives inflammatory lung pathology by increasing macrophage permissiveness to infection, triggering pyroptosis, and amplifying viral replication in alveolar epithelial cells. Co-culture of macrophages with human alveolar type II (AT2) cells overrides ORF8s previously reported inhibition of AT2 infection (7, 8), restoring productive viral replication. In vivo, IL-17RA blockade counteracts ORF8 activity, lowering viral burden and attenuating pulmonary inflammation and fibrosis. These findings reveal a paracrine role for ORF8 in reprogramming macrophages, thereby establishing a feedforward proviral circuit that accelerates lung pathology in COVID-19 and are clinically relevant given the recurrent emergence of SARS-CoV-2 variants with either intact or deleted ORF8 since the beginning of the pandemic.

microbiology↗

bioRxiv2026

Glycan Reachability Analysis: A Bottleneck-Aware Frameworkfor Inferring Tissue-Specic Glycan Biosynthetic Potential fromTranscriptomics

Glycan biosynthesis requires the coordinated expression of glycosyltransferases, modifying enzymes, and nucleotide-sugar synthesis and transport machinery. Existing computational tools predict glycan structures from gene expression using binary thresholds, losing quantitative information about relative biosynthetic capacity across tissues. Here we present glycan biosynthetic reachability analysis, which integrates expression-based Z-scores across curated pathway steps using AND/OR logic and minimum aggregation to produce continuous, tissue-comparable scores and an explicit expression-limiting step. Applied to 17,382 GTEx v8 RNA-seq samples across 54 human tissue types, reachability resolved quantitative differences hidden by presence/absence calls; for example, pancreas was 96% binary-positive for the sLeX pathway but had low median reachability (Z = -1.86). Bottleneck stability was evaluated across all 19 multi-step metrics. In independent HEK293 knockout glycomics, the minimum score contained information beyond a within-knockout permutation null but did not outperform naive mean aggregation or binary topology. Nested leave-one-knockout-out selection favored a relaxed low quantile (q = 0.2) rather than validating the strict minimum. Within-GTEx associations between reachability and signaling-response transcripts are reported only as transcriptomic coherence because predictors and readouts share the same RNA-seq source. The mouse tissue-glycome comparison remained null. Reachability is therefore a hypothesis-generating rank of transcriptomic potential, not a measure of enzymatic activity or glycan abundance. Author SummarySugars attached to the surface of every human cell -- collectively called glycans -- control many biological processes from immune recognition to cancer signaling. Understanding which glycans each tissue can produce requires knowing which sugar-building enzymes are expressed. Current computational approaches often check whether each enzyme is detectable, ignoring quantitative differences in expression. We developed glycan reachability analysis, a method that treats glycan assembly like a production line whose transcriptomic score is set by the weakest expressed step. Using gene expression data from 54 human tissue types, we show that this bottleneck-aware score reveals differences invisible to binary methods; for example, pancreas has all sialyl Lewis X enzymes detectable but at uniformly low transcript levels. In an independent HEK293 knockout-glycomics benchmark, the minimum score contained non-random information but did not outperform naive mean aggregation or binary topology. Associations between reachability and signaling-response transcripts are interpreted only as same-transcriptome coherence and do not establish glycan-mediated signaling. Bottleneck identities were reproducible for most tissue-metric combinations but unstable in some cases, including bulk-brain GM3. Because the mouse tissue-glycome comparison remained null, reachability is presented as a hypothesis-generating rank of transcriptomic potential rather than a substitute for glycomics.

systems biology↗

bioRxiv2026

Multi-omics definition of the sex-specific glycoproteome of murine tissues

Sex-specific differences in the glycoproteome remain poorly defined despite growing evidence that protein glycosylation is a key determinant of sex biology. Here we present a tissue-resolved glycoproteome atlas of adult male and female C57BL/6J mice, integrating transcriptomics, proteomics and glycoproteomics with sialic acid speciation and lectin microarray profiling across 19 tissues. Quantitative analysis of >26,800 protein- and site-specific N-glycoforms from 1,512 glycoproteins revealed highly distinct tissue glycoproteomes shaped by coordinated regulation of protein abundance and glyco-enzyme expression. Multi-omics integration identified strong glycophenotype-enzyme relationships, including control of tissue sialylation by Cmas and Cmah, suggesting rate-limiting roles in glycosylation. Pronounced sex-linked glycophenotypes were observed in salivary gland, liver and kidney, driven by differences in fucosylation, sialylation and protein abundance, whereas the brain glycome was largely conserved between sexes. An interactive online database (https://igcore.cloud/mta/atlas-viewer/) provides a resource for exploring sex-biased glycosylation across mouse tissues.

cell biology↗

bioRxiv2025

GlycoTraitR: an R package for characterizing structural heterogeneity in N-linked glycoproteomics data

Glycoproteomics data are rapidly accumulating due to advances in mass spectrometry instrumentation and the development of specialized search engines (e.g., pGlyco3, Glyco-Decipher) that enable identification of N-linked glycopeptide spectral matches (GPSMs) together with glycan structures. These advances have greatly expanded the scale and depth of N-linked glycopeptides; however, the intrinsic structural heterogeneity of glycosylation remains challenging to interpret. No existing tool provides a unified trait-based framework for analyzing N-linked GPSM data at both the glycosylation-site and protein levels. We developed glycoTraitR, an R package for trait-based analysis of structural heterogeneity in N-linked glycoproteomics data. GlycoTraitR provides a unified workflow to import GPSMs from search engine outputs, extract biologically interpretable glycan structural traits, and perform comparative analyses of micro- and macro-heterogeneity across experimental conditions using statistical testing. ImplementationThe R package and the source code of glycoTraitR are freely available on github at https://github.com/matsui-lab/glycoTraitR. A more detailed introduction and quick start guide are avaible at https://matsui-lab.github.io/glycoTraitR/.

bioinformatics↗

bioRxiv2025

GlycanGT: A Foundation Model for Glycan Graphs with Pretrained Representation and Generative Learning

MotivationGlycans are highly diverse biological sequences, but their functional understanding has lagged behind that of proteins and nucleic acids. Many glycans remain incompletely characterized or ambiguously annotated, limiting computational analyses. Existing computational approaches are primarily graph-based, capturing local structural features but struggling to model global patterns and incomplete sequences. ResultsWe present GlycanGT, a foundation model for glycans built on a graph transformer architecture. Glycans were represented as graphs with monosaccharides as nodes and glycosidic bonds as edges, and the model was pretrained using a masked language modeling objective. GlycanGT demonstrated higher performance than existing methods across 8 benchmark classification tasks (e.g., 0.734 Macro-F1 in domain prediction and 0.844 AUPRC for immunogenicity classification), and its embeddings formed biologically meaningful clusters that recovered known N- and O-glycan categories. Moreover, GlycanGT accurately proposed candidates for ambiguous sequences, maintaining >80% top-5 accuracy for both monosaccharide and glycosidic bond predictions under high masking levels. Availability and implementationThe pretrained GlycanGT model weights and usage scripts are available on Hugging Face: https://huggingface.co/Akikitani295/GlycanGT. Additional scripts used for analyses in the paper are publicly available on GitHub: https://github.com/matsui-lab/GlycanGT. Contact: matsui.yusuke.d4@f.mail.nagoya-u.ac.jp

bioinformatics↗

bioRxiv2025

Discovery of AVI-6451, a Potent and Selective Inhibitor of the SARS-CoV-2 ADP-Ribosylhydrolase Mac1 with Oral Efficacy in vivo

The COVID-19 pandemic made plain the need for effective antivirals acting on novel antiviral targets, among which viral macrodomains have attracted considerable attention. We recently described AVI-4206 (1), a potent and selective inhibitor of the SARS-CoV-2 ADP-ribosylhydrolase Mac1 based on a 9H-pyrimido[4,5-b]indole core, the first Mac1 inhibitor to demonstrate antiviral efficacy in mouse models of SARS-CoV-2 infection, but requiring IP administration and frequent dosing. Herein we describe an extensive, structurally enabled medicinal chemistry effort to identify orally bioavailable Mac1 inhibitors by addressing permeability and efflux liabilities of 1 and many of its analogs. Multiple strategies were pursued to overcome these issues, including replacing a urea function to reduce hydrogen bond donor count. While heterocyclic urea mimetics could deliver analogs like AVI-6318 (3) with potencies and ADME profiles similar to 1, abrogation of the P-gp liability was finally achieved with entirely non-polar substituents in place of urea. Thus, AVI-6451 (4) is a potent Mac1 inhibitor lead with low intrinsic clearance, high oral bioavailability, and antiviral efficacy with once-daily oral administration in a mouse model of SARS-CoV-2 infection.

pharmacology and toxicology↗

bioRxiv2024

Predicting Alzheimer's Cognitive Resilience Score: A Comparative Study of Machine Learning Models Using RNA-seq Data

BackgroundCognitive resilience (CR) in Alzheimers disease (AD) refers to preserved cognitive function despite substantial AD pathology. Diverse biological processes have been implicated in CR, including synaptic maintenance, neuroimmune regulation, and metabolic homeostasis. However, how these mechanisms are organized into molecularly distinct CR subtypes and relate to clinical and neuroanatomical heterogeneity remains unclear. Here, we applied a machine learning framework to multi-cohort transcriptomic, proteomic, and neuroimaging data to investigate molecular subtypes of CR in AD. MethodsRNA-seq data from the Religious Orders Study and Memory and Aging Project (ROSMAP) cohort were used to train machine learning models classifying individuals with AD pathology as CR or non-CR based on residual-based resilience scores. Model development and performance estimation used nested cross-validation to minimize information leakage. Final ROSMAP-trained models were evaluated in the independent Mount Sinai Brain Bank (MSBB) cohort. Model-derived genes were used for biological interpretation and hierarchical clustering of CR individuals. The subtype structure was further evaluated in the Alzheimers Disease Neuroimaging Initiative (ADNI) cohort using cerebrospinal fluid proteomics, MRI-derived brain measures, and longitudinal MMSE data. ResultsMachine learning models showed modest but consistent predictive performance in ROSMAP, with out-of-fold AUROC values of 0.644-0.688. In the independent MSBB full cohort, AUROC values were 0.586-0.659, with improved discrimination in a top/bottom quartile analysis. Hierarchical clustering identified two major molecular subgroups among CR individuals in ROSMAP/MSBB RNA-seq data. A reduced 22-gene/protein signature showed a partial, cluster-like resemblance to this structure in ADNI cerebrospinal fluid proteomics. In ADNI, both projected CR subtypes showed preserved brain tissue-volume profiles and slower longitudinal MMSE decline compared with non-CR participants, whereas clear differences between CR subtypes were not observed. Differential CSF proteomic analysis suggested partially distinct molecular characteristics. ConclusionsThese findings suggest that CR in AD may encompass molecularly heterogeneous, subtype-like profiles that converge on broadly preserved brain structure and slower cognitive decline. Our results provide a candidate framework for stratifying resilience-associated molecular phenotypes in AD and warrant prospective and experimental validation. We also developed the Resilience Gene Analyzer, a web-based platform for visualizing gene-level contributions to CR prediction (https://igcore.cloud/GerOmics/REsilienceGeneAnalyzer/).

bioinformatics↗

bioRxiv2024

HuTAge: a Comprehensive Human Tissue- and Cell-specific Ageing Signature Atlas

SummaryAgeing is a complex process that involves interorgan and intercellular interactions. To obtain a clear understanding of ageing, cross-tissue single-cell data resources are required. However, a complete resource for humans is not available. To bridge this gap, we developed HuTAge, a comprehensive resource that integrates cross-tissue age-related information from The Genotype-Tissue Expression project with cross-tissue single-cell information from Tabula Sapiens to provide human tissue- and cell-specific ageing molecular information. Availability and ImplementationHuTAge is implemented within an R Shiny application and can be freely accessed at https://igcore.cloud/GerOmics/HuTAge/home. The source code is available at https://github.com/matsui-lab/HuTAge. Contacthimori.koichi.b5@f.mail.nagoya-u.ac.jp

bioinformatics↗

bioRxiv2024

A Computational Approach to Interpreting the Embedding Space of Dimension Reduction

Nonlinear dimension reduction methods are widely applied in studies analyzing gene and protein expression, by revealing patterns of discrete groups and continuous orders in high-dimensional data. However, the tools are limited to understanding the obtained embedding structures of biological mechanisms, hindering the full exploitation of data. Here, we propose a novel framework to interpret embedding systematically by identifying and mapping associated biological functions. The method performs statistical tests and visualizes significantly enriched functions essential for the organization of the embedding structure, by applying it to the embedding results of two datasets: the Genotype Tissue Expression dataset and a Caenorhabditis elegans embryogenesis dataset, one capturing distinct cluster structures and the other capturing continuous developmental trajectories. We identified the associated functions for interpreting the two embeddings and confirmed it as a useful explainable AI tool in exploratory data analysis by providing annotations to the embedding space.

bioinformatics↗

bioRxiv2024

GPNMB+ microglia moderate the amyloid beta-tau interaction in early Alzheimer's disease

BackgroundAlthough interactions between amyloid-beta and tau proteins have been implicated in Alzheimers disease (AD), the precise mechanisms by which these interactions contribute to disease progression are not yet fully understood. Moreover, despite the growing application of deep learning in various biomedical fields, its application in integrating networks to analyze disease mechanisms in AD research remains limited. In this study, we employed BIONIC, a deep learning-based network integration method, to integrate proteomics and protein-protein interaction data, with an aim to uncover factors that moderate the effects of the A{beta}-tau interaction on mild cognitive impairment (MCI) and early-stage AD. MethodsProteomic data from the ROSMAP cohort were integrated with protein-protein interaction (PPI) data using a Deep Learning-based model. Linear regression analysis was applied to histopathological and gene expression data, and mutual information was used to detect moderating factors. Statistical significance was determined using the Benjamini-Hochberg correction (p < 0.05). ResultsOur results suggested that astrocytes and GPNMB+ microglia moderate the A{beta}-tau interaction. Based on linear regression with histopathological and gene expression data, GFAP and IBA1 levels and GPNMB gene expression positively contributed to the interaction of tau with A{beta} in non-dementia cases, replicating the results of the network analysis. ConclusionsThese findings indicate that GPNMB+ microglia moderate the A{beta}-tau interaction in early AD and therefore are a novel therapeutic target. To facilitate further research, we have made the integrated network available as a visualization tool for the scientific community (URL: https://igcore.cloud/GerOmics/AlzPPMap).

systems biology↗

bioRxiv2024

Systematic identification of exercise-induced anti-aging processes involving intron retention

Exercise is one of the most promising anti-aging interventions for maintaining skeletal muscle health in older adults. Nine "Aging Hallmarks", proposed by Lopez-Otin, offer insights into the aging process; however, the link between these hallmarks and exercise is not fully elucidated. In this study, we conducted a systematic multi-omics analysis of skeletal muscles, focusing on aging and exercise, based on gene signatures for aging hallmarks. It is posited that mRNA splicing activity, linked to genomic instability, constitutes a fundamental hallmark of aging, and it exhibits divergent expression patterns in response to aging and exercise. Additionally, we analysed splicing events and discovered that intron retention (IR) is significantly impacted by aging, exhibiting contrasting changes to those induced by resistance training in the older cohort. The isoforms characterised by IR are notably enriched in mitochondrial functions. Conclusively, our results underscore the significance of splicing mechanisms as a novel aspect of aging hallmarks in skeletal muscles and propose a new mechanism by which exercise exerts its anti-aging effects on skeletal muscles through intron retention. Key points summaryO_LISkeletal muscle aging involves significant structural and functional changes, including loss of muscle mass, decline in strength, and mitochondrial dysfunction, all influenced by genomic instability. C_LIO_LIExercise has been identified as a key intervention that counters genomic instability and modulates mRNA splicing patterns, particularly through the regulation of Intron Retention, to mitigate aging effects in skeletal muscle. C_LIO_LIWe reveal the novel role of IR, especially in principal isoforms, where it is linked to critical cellular processes like mitochondrial function, suggesting a targeted pathway through which exercise exerts its anti-aging effects. C_LIO_LIThe findings provide new insights into the molecular mechanisms underlying the beneficial effects of exercise on aging skeletal muscle. C_LIO_LIThis study lays the groundwork for future research on exercise-induced modulation of mRNA splicing as a therapeutic strategy for aging and potentially age-related diseases, pointing towards a significant shift in how we approach aging intervention strategies. C_LI

systems biology↗

bioRxiv2023

Single-cell Glycogenomics Deciphers Links Between Altered Transcriptional Regulation and Aberrant Glycosylation in Alzheimer's Disease

Glycosylation is increasingly recognized as a potential therapeutic target in Alzheimers disease. In recent years, evidence of Alzheimers disease-specific glycoproteins has been established. However, the mechanisms underlying their dysregulation, including tissue- and cell-type specificity, are not fully understood. We aimed to explore the upstream regulators of aberrant glycosylation by integrating multiple data sources using a glycogenomics approach. We identified dysregulation of the glycosyltransferase PLOD3 in oligodendrocytes as an upstream regulator of cerebral vessels and found that it is involved in COL4A5 synthesis, which is strongly correlated with amyloid fiber formation. Furthermore, COL4A5 has been suggested to interact with astrocytes via extracellular matrix receptors as a ligand. This study suggests directions for new therapeutic strategies for Alzheimers disease targeting glycosyltransferases. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/573290v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@14d608forg.highwire.dtl.DTLVardef@fe8352org.highwire.dtl.DTLVardef@15f8f77org.highwire.dtl.DTLVardef@c3ef28_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Phosphorylation of 53BP1 by ATM enforce neurodevelopmental programs in cortical organoids

53BP1 is a well-established DNA damage repair factor recently shown to regulate gene expression and critically influence tumor suppression and neural development. For gene regulation, how 53BP1 is regulated remains unclear. Here, we showed that 53BP1-serine 25 phosphorylation by ATM is required for neural progenitor cell proliferation and neuronal differentiation in cortical organoids. 53BP1-serine 25 phosphorylation dynamics controls 53BP1 target genes for neuronal differentiation and function, cellular response to stress, and apoptosis. Beyond 53BP1, ATM is required for phosphorylation of factors in neuronal differentiation, cytoskeleton, p53 regulation, and ATM, BNDF, and WNT signaling pathways for cortical organoid differentiation. Overall, our data suggest that 53BP1 and ATM control key genetic programs required for human cortical development.

developmental biology↗