Biology subjects

Ito, T.

Publications and source records attributed to Ito, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Genomic and phenotypic consequences of long-term isolation and small population size in Yakushima macaques

The relative roles of genetic drift, inbreeding, and natural selection in island evolution have been fundamental themes in evolutionary biology for nearly a century. However, empirical studies have yet to reach a consensus, and research integrating both genotypes and phenotypes in primates remains limited. The present study aims to estimate the demographic history of an island primate population, the Yakushima macaque (Macaca fuscata yakui), and evaluate the genomic and morphological consequences of its isolation. To this end, we conducted population genetic analyses based on whole-genome sequencing and geometric morphometric analyses of cranial shape. Our results indicate that M. f. yakui has been isolated with an effective population size of several thousand individuals for over 100,000 years. Although we detected signatures of purifying selection, deleterious alleles have not been sufficiently purged. The degree of fluctuating asymmetry in males was slightly, yet significantly, higher in M. f. yakui than in mainland populations, whereas this trend was not observed in females. Marginal evidence of directional selection was found for cranial size, but not for shape. Overall, our findings suggest that due to long-term isolation, genetic drift and inbreeding have exerted a substantial influence on shaping genotypic and phenotypic variation in this island population.

evolutionary biology↗

bioRxiv2026

Characterization of a maintainable human myeloid model of VEXAS syndrome with enhanced TNF-induced cell death and DAMP release

Background: VEXAS syndrome is an adult-onset autoinflammatory disorder caused by somatic UBA1 mutations. UBA1-mutated myeloid cells have been reported to exhibit increased susceptibility to inflammatory cell death; however, the mechanisms underlying this phenotype and the extracellular consequences of enhanced cell death remain incompletely understood. Methods: Disease-associated UBA1 M41 mutations were introduced into U937 cells by CRISPR/Cas9-mediated genome editing. The resulting cells were characterized by genomic, molecular, and functional analyses, with particular focus on UBA1-associated cellular phenotypes, cell death signaling, and extracellular release of damage-associated molecular patterns (DAMPs). Results: Genome editing yielded UBA1-mutated cells that could be maintained under standard culture conditions while recapitulating multiple molecular and cellular features associated with VEXAS syndrome. Long-read sequencing revealed the intended UBA1 M41 mutation on one allele and a CRISPR/Cas9-induced on-target genomic deletion on the other. The mutant cells exhibited reduced UBA1b and increased UBA1c expression, prominent cytoplasmic vacuolization, reduced proliferative capacity, and increased basal cell death. They also showed enhanced susceptibility to TNF-induced cell death under conditions favoring either apoptosis or necroptosis. Mechanistic analyses demonstrated enhanced apoptotic and necroptotic signaling, together with increased basal abundance of RIPK1, RIPK3, and MLKL. Pharmacological inhibition of RIPK1, RIPK3, or MLKL attenuated membrane permeabilization under caspase-inhibited conditions. Enhanced cell death was accompanied by increased extracellular release of ATP, HMGB1, and S100A8/A9. Conclusions: We characterized a maintainable UBA1-mutated human myeloid model that recapitulates multiple molecular and cellular features associated with VEXAS syndrome. Using this model, we showed that UBA1 dysfunction is associated with heightened susceptibility to TNF-dependent apoptotic and necroptotic cell death and increased extracellular release of multiple DAMPs. These findings provide insight into the cellular consequences of UBA1 dysfunction and establish a tractable experimental platform for further mechanistic studies of VEXAS syndrome.

immunology↗

bioRxiv2026

Evaluating Lightweight and Full Fine-Tuning Strategies Against Classical Machine Learning for Protein Function Prediction

Motivation Protein language models (PLMs) have emerged as powerful tools for sequence-based prediction of protein function, yet systematic benchmarks comparing frozen embeddings, fine-tuning strategies like Low-Rank Adaptation (LoRA) and classical machine learning (ML) remain limited. We benchmarked four ML strategies: ML using amino acid descriptors (SL-AAFeat), ML using frozen embeddings from 20 PLMs across various pooling strategies (SL-Embed), full model fine-tuning (FT-Full) and LoRA-based fine-tuning (FT-LoRA). Performance was evaluated on the in-house VHH phage display dataset (VHH) for binding affinity prediction and the TAPE fluorescence dataset (FLS and FLS10) for mutational effect prediction. Results Model performance depended strongly on the dataset and adaptation strategy. Max pooling consistently improved embedding-based models, while amino acid descriptors remained competitive under specific datasets and resource constraints. Fine-tuning generally provided the highest predictive performance, but the advantage is not universal. Hyperparameter optimization significantly enhanced FT-LoRA, enabling it to outperform FT-Full on the VHH dataset with less than 10% model parameter adaptation. In contrast, FT-Full achieved the best performance on FLS and FLS10. Several medium-sized PLMs performed comparably to larger models, highlighting favorable performance-efficiency trade-offs. Overall, this paper presents a thorough review of PLM utilization strategies and practical recommendations for selecting suitable strategies based on dataset characteristics and available computational resources. Availability The source code used in this manuscript is available in a Zenodo repository at https://doi.org/10.5281/zenodo.21466255.

bioinformatics↗

bioRxiv2026

Complementary frontoparietal and corticothalamic contributions to relational reasoning

Complex reasoning requires frontal, parietal, and thalamic systems to manage increasing relational demands, yet the underlying circuit mechanisms remain unclear. Here, we combined EEG with biologically grounded corticothalamic neural field modelling while participants solved relational problems of graded complexity. Successful reasoning was associated with dissociable frontoparietal dynamics. Frontal regions showed increased theta-band power, whereas parietal regions showed reduced alpha- and beta-band power. Neural field modelling linked these dynamics to complementary complexity-dependent circuit adaptations. Parietal regions showed modulation of intracortical and corticothalamic gains, intrathalamic inhibition, prolonged loop delays, and faster synaptic filtering, whereas frontal regions primarily adjusted intracortical gains in a manner consistent with maintaining local excitatory-inhibitory balance and supporting longer temporal integration windows. Together, these empirical and model-derived insights demonstrate that problem-solving under increasing reasoning demand relies not simply on greater frontoparietal engagement, but on additional, region-specific reconfigurations of cortical and corticothalamic circuits.

neuroscience↗

bioRxiv2026

MECP2-mutant marmosets exhibit primate-specific phenotypes of Rett syndrome

We used genome editing to generate MECP2-knockout marmosets and establish a new primate model for Rett syndrome (RTT), a neurodevelopmental disorder. MRI analysis of this marmoset model revealed diminished cortico-cortical connections, particularly those originating in the prefrontal cortex. Detailed histological analysis revealed reduced dendritic arborization and synaptic density, particularly in the upper layers of the cerebral cortex. Cortical layer II/III excitatory neurons appeared to be immature based on single-nucleus RNA sequencing and spatial transcriptomics, while PVALB-positive interneurons exhibited upregulation of neuronal maturation-related transcripts. Moreover, the neurodevelopmental disease gene GABAB receptor 2 (GABBR2) emerged as a conserved downstream target of MECP2, illuminating a cellular cascade that could be targeted in preclinical and therapeutic drug development. Many of the gene expression changes observed in the marmoset model are common in RTT patients, thereby highlighting molecular links from cell type-specific phenotypes in the marmoset model to the RTT clinical presentation.

neuroscience↗

bioRxiv2026

Development of a genetically encoded supersulfide-dependent translocation reporter

Supersulfides are emerging sulfur-containing signaling molecules involved in redox regulation, mitochondrial function, and protein S-sulfhydration. However, their dynamic behavior in living mammalian systems remains poorly understood because existing analytical methods require destructive sample preparation or lack sufficient intracellular applicability. Here, we developed a genetically encoded supersulfide-dependent translocation reporter (SuTR) for mammalian cells and in vivo imaging. Although the previously reported probe psGFP failed to respond to supersulfides in mammalian cells, fusion of psGFP with the sulfide-responsive transcriptional repressor (SqrR) generated SuTR, a novel reporter that exhibited supersulfide-dependent translocation from the nucleus to the cytoplasm. Na2S2 and Na2S induced dose-dependent cytosolic translocation of SuTR, whereas Na2S showed no effect. Fluorescence recovery after photobleaching (FRAP) analysis revealed accelerated fluorescence recovery shortly after supersulfide stimulation, and overexpression of the endogenous supersulfide-producing enzyme Cysteinyl-tRNA Synthetase 2 (CARS2) similarly altered reporter dynamics. Mutational analyses demonstrated that reporter responsiveness depends on the DNA-binding activity of SqrR. Furthermore, SuTR successfully detected supersulfide induction in mouse liver in vivo following Na2S administration. These findings establish SuTR as a genetically encoded reporter for monitoring supersulfide dynamics in mammalian cells and tissues. HighlightsO_LIWe developed SuTR, a genetically encoded supersulfide-dependent translocation reporter. C_LIO_LISupersulfides induce nuclear-to-cytoplasmic translocation of SuTR C_LIO_LIFRAP enables rapid detection of endogenous and exogenous supersulfide responses C_LIO_LISuTR activity depends on the DNA-binding function of SqrR C_LIO_LISuTR enables visualization of supersulfide dynamics in mouse liver in vivo C_LI

Molecular Biology↗

bioRxiv2026

Exploring diverse routes to high-affinity-antibody variable domains through deep-sequencing-informed machine learning

The integration of in vitro selection, deep sequencing, and machine learning (ML) has recently been developed as a powerful strategy for discovering functional antibodies. However, how training data composition and ML search space design influence the identification of high-affinity variants remains unclear. Here, we aimed to optimize ML-integrated directed evolution for functional antibody discovery by selecting training data from deep sequencing analysis. By performing phage display selection using camelid heavy-chain antibodies (VHHs), we demonstrated that early-round data, retaining more binding-negative variants, can be superior for training models to identify high-performance VHHs. We also investigated a lead-independent ML search space design by focusing on conserved residues in final rounds, successfully identifying variants with higher affinities than those from lead-based maturation (KD = 7.9 nM). These findings demonstrate that training data selection and search space design are critical for successful ML-guided antibody engineering and provide diverse pathways for discovering high-affinity VHH variants.

bioengineering↗

bioRxiv2026

Training Strategy Optimization to Mitigate Shortcut Learning in Pan-Cancer Drug Response Prediction

BackgroundPrediction of in vivo drug response is a central challenge in precision medicine, but the scarcity of labeled clinical data still necessitates the use of large-scale cancer cell line resources for model training. Domain adaptation methods, which aim to transfer knowledge learned from a source domain (cell lines) to a target domain (patients) by aligning feature distributions across domains, are a promising approach to bridge the gap between in vitro models and in vivo patients. However, we observed that these methods can exhibit a significant discrepancy between pan-cancer evaluation metrics and cancer type-specific prediction accuracy. This performance gap warrants a detailed investigation into their underlying predictive characteristics. ResultsWe discovered that cancer-type-specific class imbalances in training data can lead domain adaptation models to engage in shortcut learning, where they primarily discriminate between cancer types rather than capturing the actual biological determinants of drug sensitivity. To address this, we propose a strategy of combining two approaches: (1) excluding cancer types causing imbalance from the training data, and (2) adjusting class balance through oversampling and class weighting while retaining cancer types causing the imbalance. Among all configurations tested in conjunction with the CODE-AE (Context-aware Deconfounding AutoEncoder) framework, the combination of moderate oversampling (30% non-responder ratio) with class weighting achieved the best performance, significantly improving prediction accuracy in 5 out of 11 external patient cohorts from TCGA and GEO. ConclusionsOur findings demonstrate that appropriate class imbalance correction--rather than wholesale exclusion of imbalanced cancer subtypes--enables effective utilization of biologically relevant information shared across cancer types for drug response prediction. This study highlights the critical importance of jointly optimizing training data composition and class balance adjustment strategies in developing robust pan-cancer drug response prediction models for precision medicine applications. HighlightsO_LIIdentified a critical discrepancy in current domain adaptation models for drug response prediction: high pan-cancer accuracy often masks poor performance within specific cancer types. C_LIO_LIRevealed the root cause as "shortcut learning," where models tend to distinguish between cancer tissue types (hematological vs. solid) rather than learning individual drug sensitivity. C_LIO_LIDiscovered severe class imbalance in training data, with hematological cell lines being disproportionately drug-responsive across multiple chemotherapeutics. C_LIO_LIProposed an architecture-agnostic fix using the CODE-AE framework: moderate oversampling (30% minority ratio) combined with class weighting. C_LIO_LIDemonstrated significant improvements in 5 of 11 external patient cohorts, showing that correcting class bias is more effective than simply excluding problematic data. C_LI

bioinformatics↗

bioRxiv2026

Functional Analysis of cha Genes Identifies ChaC as a Glutathione-Degrading Enzyme Rather Than a Sodium Transport Regulator

Glutathione (GSH) is a tripeptide that plays essential roles in redox regulation and stress responses across organisms. In Escherichia coli, the GSH-specific {gamma}-glutamyl cyclotransferase (ChaC) has been characterized biochemically, yet its physiological role remains unclear. Moreover, ChaC has been annotated as a regulator of the Na/H antiporter ChaA based on its genomic association, although experimental evidence supporting this function is limited. In this study, we investigated whether chaC and its co-transcribed gene, chaB, are involved in sodium transport or GSH metabolism. Gene expression analyses revealed that chaA, chaB, and chaC are upregulated under salt stress. Functional analyses using deletion mutants showed that loss of chaA reduced salt tolerance, whereas deletion of chaB enhanced tolerance and decreased intracellular sodium levels. In contrast, deletion of chaC had no significant effect on salt tolerance or sodium accumulation. Overexpression of cha genes further indicated that chaA, but not chaB or chaC, contributed to salt tolerance. Importantly, overexpression of chaC significantly reduced intracellular GSH levels, whereas chaB overexpression had no effect. These results indicate that ChaC primarily functions in GSH degradation rather than in cation transport, and that ChaB does not participate in GSH metabolism. Our findings clarify the distinct physiological roles of ChaC and ChaB and provide new insight into bacterial physiology regarding GSH metabolism and ion transport in E. coli.

biochemistry↗

bioRxiv2026

Antioxidant capacity of Catechinopyranocyanidins derived from adzuki beans

Catechinopyranocyanidins (Cpcs) which consist of diastereomers A and B are pigments derived from adzuki beans and are compounds in which the catechin and cyanidin skeletons are condensed to a pyrano ring. While catechins and anthocyanidins possess high antioxidant capacity, the physiological functions of Cpcs remains unclear. In this study, the antioxidant capacity of Cpcs was evaluated by in vitro antioxidant assays and by assessing their cytoprotective activity against oxidative stress in normal human dermal fibroblasts (NHDFs). Antioxidant capacity based on the hydrogen atom transfer (HAT) mechanism, as assessed by the ORAC assay revealed that Cpcs exhibit 14.1 mol TE/mol (Trolox equivalent antioxidant capacity: TEAC). Meanwhile, capacity based on the single electron transfer (SET) mechanism, as assessed by the DPPH, ABTS and CUPRAC assays revealed, they exhibit 2.1-3.6 mol TE/mol. Since TEAC value of Cpcs demonstrated by the HAT based mechanism higher than its SET based oxidative capacity suggesting that the antioxidant capacity of Cpcs is driven by the HAT mechanism. In cell culture experiments, Cpcs ameliorate cell toxicity in rotenone-induced injury model, suggesting to cytoprotective activity against mitochondrial dysfunction-dependent apoptosis. These results reveal novel physiological functions of Cpcs which may serve as a design guideline for elucidating in vivo dynamics based on antioxidant mechanisms.

biochemistry↗

bioRxiv2026

High extracellular osmolarity promotes yeast thermotolerance through osmotic modulation and glycerol-dependent adaptation.

High-temperature stress is a major constraint on yeast growth and fermentation, and has traditionally been interpreted primarily in terms of intracellular molecular damage such as protein denaturation and aggregation. Despite this extensive focus on intracellular mechanisms, how physical factors within the extracellular environment influence yeast thermotolerance remains poorly understood. Here we demonstrate that increases in extracellular osmolarity markedly attenuate growth inhibition under high-temperature conditions in yeast. This protective effect was consistently observed in multiple laboratory and industrial strains of Saccharomyces cerevisiae, as well as in ascomycetous and basidiomycetous yeasts, indicating that osmotic pressure-dependent thermotolerance is a broadly conserved phenomenon. We also found that extracellular osmolarity dynamically increases during growth and then decreases in a diauxic shift-like pattern after growth arrest. At high temperature, the secretion of glucose-derived metabolites decreased, but that of other solutes increased, suggesting that heat stress alters the composition of extracellular solutes contributing to osmolarity. In addition, intracellular glycerol levels increased at high temperature, and this increase was further enhanced under high-osmolarity conditions. Notably, expression of a constitutively active Hog1 mutant exhibited raised intracellular glycerol levels, enhanced nuclear localization of Hog1, and improved growth under high-temperature conditions. Collectively, these findings support a model in which extracellular osmolarity is modulated to avoid excessive intracellular osmolarity under high-temperature conditions, while the intracellular accumulation of glycerol contributes to yeast adaptation at high-temperature. Our results highlight extracellular-intracellular osmotic coordination as an additional physiological layer of high-temperature stress adaptation in yeast.

microbiology↗

bioRxiv2026

Dose-dependent modeling of combinatorial drug responses stratifies patient survival and reveals therapeutic vulnerabilities in precision oncology

A substantial proportion of cancer patients fail to benefit from their prescribed combination regimens, yet identifying superior alternatives from the vast pharmacological space prior to treatment failure remains an unsolved clinical challenge. Existing computational approaches either rely on multi-omics profiles unavailable in standard oncological practice or reduce drug efficacy to scalar metrics that discard the dose-dependent resolution essential for therapeutic optimization. Here, we present XACT, a hierarchical deep learning framework that reconstructs full dose-dependent drug responses for both monotherapy and drug combinations using only clinically accessible transcriptomic profiles. By leveraging an asymmetric X-Linear Attention mechanism that models second-order interactions between molecular drug substructures and intracellular signaling pathway activities, XACT captures concentration-dependent pharmacodynamics with state-of-the-art accuracy and generalizability to unseen transcriptomic landscapes. When applied to the TCGA pan-cancer cohort, XACT-derived resistance scores were significantly associated with clinical treatment outcomes and stratified overall survival as the strongest independent prognostic factor after multivariate adjustment for tumor stage and cancer type. Systematic virtual screening revealed therapeutic vulnerabilities and nominated alternative regimens for treatment-refractory sarcoma and pancreatic adenocarcinoma. These results establish XACT as a scalable, interpretable, and clinically translatable framework that advances precision oncology from computational prediction toward data-driven therapeutic prescription.

cancer biology↗

bioRxiv2026

Lipid A counteracts doxorubicin-induced systemic dysfunction by boosting mitochondrial activity

Vaccine adjuvants are critical for enhancing immune responses and sustaining antibody production. Although their safety profiles are well established, assessments have largely focused on metabolic and excretory organs such as the liver and kidneys, with limited attention to the heart. Here, we systematically evaluated the cardiac effects of five representative adjuvants in mice: alum, MF59, AS03, Sigma Adjuvant Systems, and lipid A. None of the adjuvants impaired baseline cardiac contractile function. Notably, lipid A uniquely enhanced mitochondrial respiratory capacity in rat and human induced pluripotent stem cell-derived cardiomyocytes and promoted mitochondrial membrane hyperpolarization. We next examined its therapeutic potential in a doxorubicin (Dox)-induced heart failure model characterized by mitochondrial dysfunction. Co-administration of lipid A with influenza hemagglutinin (HA) antigen significantly ameliorated cardiac dysfunction. In parallel, lipid A prevented the Dox-induced decline in anti-HA antibody titers, an effect associated with preservation of splenic B cell populations. Collectively, these findings reveal a previously unappreciated cytoprotective dimension of lipid A, demonstrating that it not only potentiates immune responses but also counteracts chemotherapy-induced functional decline by enhancing mitochondrial activity.

pharmacology and toxicology↗

bioRxiv2026

Selective approach behavior toward context-dependent ultrasonic vocalizations in male mice

Ultrasonic vocalizations (USVs) are widely used in rodent social communication, yet the functional significance of male-male vocal interactions in mice remains unclear. Here, we investigated whether USVs produced during specific social behaviors influence the behavior of conspecifics. Using playback experiments, we compared responses to vocalizations recorded during chasing and being chased in male-male interactions. We found that USVs emitted by chased intruders consistently elicited approach behavior in receiver mice, whereas those emitted by chasing individuals did not. Acoustic analyses revealed that these vocalizations differed in syllable composition, with intruder calls containing a higher proportion of upward frequency-modulated syllables and exhibiting higher mean frequencies. In addition, the temporal organization of syllables appeared to contribute to the behavioral response. Together, these results suggest that male mice respond selectively to certain USV patterns associated with specific social contexts, indicating that acoustic features and temporal structure may jointly influence social approach behavior in mice. HighlightsO_LIBehavioral context (chased vs. chasing) shapes the composition of USV syllable types C_LIO_LIMale mice selectively approach USVs from chased intruders, but not chasing residents C_LIO_LIThe approach response exhibits high temporal synchrony across individual receivers C_LIO_LITemporal organization of syllables modulates approach behavior based on acoustic features C_LI

animal behavior and cognition↗

bioRxiv2026

Autopsy-based longitudinal multi-organ high-dimensional profiling reveals lineage plasticity in TRK-inhibitor-resistant secretory breast carcinoma

Secretory breast carcinoma (SBC) is typically indolent, yet mechanisms underlying aggressiveness and therapeutic resistance to tropomyosin receptor kinase inhibitors (TRKi) remain unclear. Autopsy-based longitudinal multi-organ high-dimensional profiling of metastatic TRKi-resistant SBC demonstrated histopathological heterogeneity, including secretory and squamous components, arising from a shared clonal origin. Integrated genomic and transcriptomic analyses revealed hierarchical transcriptional rewiring consistent with a lineage-plastic state, suggesting a potential link to tumor aggressiveness and therapeutic resistance.

pathology↗

bioRxiv2026

Pan-Pharmacological Drug-Target Interaction Prediction with 3D-Informed Protein Encoding at Scale

Accurate prediction of drug-target binding affinity across multiple pharmacological endpoints remains challenging, as most deep learning methodologies focus on a single metric and face a trade-off between incorporating structural information and computational throughput. Here we present OmniBind, a multitask framework that resolves both constraints by encoding protein tertiary structures as discrete token sequences and integrating them with amino acid sequence features through a gated fusion mechanism. Trained on over two million compound-protein pairs from BindingDB, OmniBind simultaneously predicts four pharmacological endpoints in a single forward pass, providing a pan-pharmacological profile across multiple affinity endpoints for each compound-target pair. Across adversarial and temporal benchmarks, OmniBind consistently outperforms state-of-the-art architectures, demonstrating that structural encoding captures physicochemical interaction principles rather than ligand-protein co-occurrence patterns. Attention analysis reveals biologically meaningful binding site recognition, with the model selectively weighting the ABL1 gatekeeper residue T315 and responding to its drug-resistance mutation T315I. As a demonstration of practical utility, proteome-wide screening of 20,421 human proteins recovered 85.7% of known clinical targets of clozapine within the top 200 predictions, correctly resolving its pharmacological profile despite structural similarity to clomipramine. OmniBind provides an accurate, structurally informed, and interpretable platform for multi-endpoint drug-target interaction prediction, with applications to lead optimization, off-target safety assessment, and drug repositioning.

bioinformatics↗

bioRxiv2025

Whole-Tissue Distribution Analysis for Visualization of Nanoplastics in the Mouse Brain

Nanoplastics (NPs) are increasingly recognized as environmental contaminants and have been detected in diverse biological tissues; however, their biodistribution in complex organs remains poorly understood. Conventional section-based imaging restricts spatial context and volumetric analysis, making it challenging to map NP distribution in structurally complex organs such as the brain, even with fluorescent labeling. To address these limitations, we developed an integrated workflow combining tissue optical clearing (SeeDB2G) with light-sheet fluorescence microscopy (LSFM), enabling three-dimensional visualization of fluorescently labeled polystyrene (PS) NPs in neonatal mouse brains. At postnatal day 0 - a critical window of heightened vulnerability due to immature barrier systems and rapid neurodevelopment - pups were orally administered spherical PSNPs of 50 or 500 nm. Fluorescence stereoscopic imaging revealed pronounced organ-level accumulation of 50 nm PSNPs in the intestine, liver, kidney, and brain, compared with markedly lower signals from 500 nm PSNPs 24 h post-exposure. Optical clearing rendered the entire brain transparent while preserving fluorescence, allowing LSFM to accurately assess regional PSNP accumulation without sectioning. This workflow enabled whole-brain visualization of size-dependent NP uptake, with 50 nm PSNP detected throughout the brain and the highest accumulation observed in the thalamus and brainstem. Although the current implementation relies on fluorescent labeling and model NPs, this approach provides a scalable platform for whole-organ biodistribution analysis and lays the foundation for mechanistic studies of barrier permeability, developmental vulnerability, and organ-specific interactions.

pharmacology and toxicology↗

bioRxiv2025

Evolutionary stasis and homogeneous selection structure microbial communities in the deep subseafloor sedimentary biosphere

The subseafloor biosphere, one of the Earths largest and most stable microbial habitats, mainly consists of energy-limited sediments where microbial life persists over geological timescales. However, the mechanisms governing microbial community assembly and evolution under condition of extreme energy limitation remains unclear. Here, we analysed a 296 m sediment core off the Shimokita Peninsula using amplicon sequencing, metagenomics, and genome-resolved analyses from 31 depth intervals spanning [~]480 kyr. The microbial community composition was governed primarily by homogeneous selection, consistent with persistent environmental uniformity during burial. Genome-resolved analyses of 224 high-quality metagenome-assembled genomes revealed highy conserved gene repertoires and uniformly low ratios of non-synonymous-to-synonymous substitutions, suggesting strong purifying selection with minimal genomic changes over this timescale. Our results indicate that evolutionary stasis is a pervasive feature of microbial life in deep subseafloor sediments, and propose a conceptual framework linking long-term environmental stability to genomic preservation in the Earths most persistent biosphere.

ecology↗