Biology subjects

Hong, Y.

Publications and source records attributed to Hong, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

FPmotion an Automated Signal Processing and Statistical Analysis Tool for Fiber Photometry Data

Fiber photometry measures neural activity in vivo from genetically encoded indicators. Recordings generate large datasets that require extensive preprocessing and robust statistical methods for meaningful interpretation. However, existing analysis tools demand programming expertise, limiting accessibility. Here we describe FPmotion, a comprehensive, user-friendly software platform for batchwise processing, integration, and statistical analysis of fiber photometry data with or without accompanying behavioral information. FPmotion performs filtering, isosbestic regression, {Delta}F/F computation, detrending, and z-scoring. It also extracts detailed peak properties at whole-file, block-level, and individual-peak resolution. When behavioral data is provided, FPmotion automatically identifies behavioral bouts, computes bout-level statistics, performs peri-event signal extraction, and supports multi-group comparisons through ANOVA- and LMEM-based statistical frameworks. All analyses generate publication-ready figures and structured CSV outputs suitable for downstream workflows. FPmotion also introduces a dedicated alignment module for peri-event signal analysis. This module applies dynamic time warping followed by barycenter averaging to realign peri-event traces while preserving the behavioral time anchor, producing cleaner and more temporally coherent peri-event motifs. We demonstrate FPmotions capabilities using the dlight1.1 sensor to measure dopamine responses from ventral striatum in behaving mice before and after amphetamine treatment. Together, FPmotion offers a fully automated framework for FP data analysis that improves interpretability and accessibility while reducing analysis time substantially.

neuroscience↗

bioRxiv2025

Template-based RNA structure prediction advanced through a blind code competition

Automatically predicting RNA 3D structure from sequence remains an unsolved challenge in biology and biotechnology. Here, we describe a Kaggle code competition engaging over 1700 teams and 43 previously unreleased structures to tackle this challenge. The top three submitted algorithms achieved scores within statistical error of the winners of the recent CASP16 competition. Unexpectedly, the top Kaggle strategy involved a pipeline for discovering 3D templates, without the use of deep learning. We integrated this template-modeling pipeline and other Kaggle strategies to develop a single model RNAPro that retrospectively outperformed individual Kaggle models on the same test set. These results suggest a growing importance of template-based modeling in RNA structure prediction.

biophysics↗

bioRxiv2025

Recruitment of the outer-membrane lipoprotein DolP to thedivision site via cardiolipin-mediated diffusion-stateswitching

In diderm bacteria, the outer membrane (OM) must invaginate in step with septal peptidoglycan (PG) remodeling during cytokinesis. One OM lipoprotein, DolP, has been shown to localize at the cell division site and to facilitate the daughter cell separation. Yet how DolP is recruited to the division site remains unclear at the molecular level. Here, we show that DolP arrives concomitantly with the late divisome protein FtsN. Utilizing single-particle tracking Photoactivated Localization Microscopy (spt-PALM), we investigated the dynamics of individual DolP molecules in living Escherichia coli cells. Single-molecule analysis revealed two diffusion states: a diffusive state across the cell envelope and a previously underappreciated immobile state enriched at the septal and polar regions. Importantly, DolP does not comigrate with the processive FtsW-FtsI-FtsN complex. Mutations of the cardiolipin-binding surface caused loss of mid-cell enrichment and reduced the immobile fraction. Cardiolipins preference for high negative curvature enriches it at the division site, creating a trapping environment for DolP. Additionally, the active septal constriction but not the late divisome proteins recruit DolP. Together, these findings indicate that cardiolipin-mediated immobilization underlies DolPs septal enrichment.

microbiology↗

bioRxiv2025

Axially swept dithered light-sheet microscope to reveal cardiac morphology

Understanding cardiac microstructure and vascular networks in their entirety is critical for assessing cardiovascular development, disease progression, and therapeutic interventions. Light-sheet microscopy combined with tissue clearing enables high-resolution volumetric imaging of intact organs but faces limitations in trabeculated myocardium due to trade-offs among light-sheet thickness, effective range, and frame rate. We exploit temporal dynamics that govern illumination-detection interplay to maintain uniform resolution across specimens. Building on this, we implemented high-speed dithered light-sheet (DiLS) illumination, extending the confocal region by over 40% and enhancing the space-bandwidth product while preserving optical sectioning. Integration of DiLS with a sweeping approach establishes the axially swept dithered light-sheet (AS-DiLS), which enhances imaging throughput while preserving axial resolution and enables uniform illumination up to 12.5-millimeter range. AS-DiLS delivers near-isotropic resolution (~2.5 m) for investigating intricate ventricular trabeculae, vasculature, and extracellular matrix, providing a scalable platform for comprehensive cardiovascular morphology and topology assessment from embryos to adults. TeaserVolumetric imaging reveals microstructure and vascular networks in their entirety with near-isotropic resolution.

bioengineering↗

bioRxiv2025

Integrating Mutation and Stop Signals for Improved RNA Structure Analysis and Insight Discovery

BackgroundUnderstanding RNA structures is essential for exploring its diverse cellular roles. Chemical modification-based RNA structure probing remains a key approach to revealing RNA structures in complex in vivo conditions. Practically, chemical modifications generate both mutation and stop signals during reverse transcription within a single experiment. However, traditional analysis pipelines often rely on only one of the two signals without fully leveraging both. ResultsTo address this, we developed a machine learning-based approach, STONE, that integrates both signals from a single experiment. STONE improved RNA structure analysis across multiple independent validation regions, notably 28S rRNA, viral RNA and regulatory RNAs. In genome-wide datasets, especially single-cell data, STONE significantly increased nucleotide coverage per transcript and improved gene detection reliability. In genome-wide datasets, especially single-cell data, STONE substantially enhanced structural information coverage at both transcript and nucleotide levels. This maximized signal utilization, yielding RNA structures in single-cell data comparable to those from bulk datasets. Furthermore, STONE-derived structural scores allow direct identification of RNA-binding protein binding sites directly from a single probing experiment, with results on RNAs such as U1 snRNA, RNase P RNA, and XIST lncRNA closely matching established techniques like CLIP-seq and RNP-MaP. ConclusionsBy integrating mutation and stop signals from one single experiment, STONE advances RNA structure analysis accuracy, extends nucleotide coverage, and facilitates complex RNA-protein interaction studies, broadening the methods applications in RNA-based research.

bioinformatics↗

bioRxiv2025

Modtector: Ultra-Fast Modification Signal Mining on Mapped Sequencing Reads

SummaryCurrent tools for RNA epitranscriptomic modification and structural feature analysis are often fragmented, focusing on single signal types and struggling with processing efficiency, especially as sequencing data volumes increase and single-cell technologies advance. To address this challenge, we developed Modtector, an efficient and versatile tool for unified extraction of modification signals from aligned sequencing data. By integrating dual-signal recognition within a single framework, Modtector employs a "count-then-correct" approach to handle both mutation and stop signals, significantly reducing computational complexity. This results in a multi-fold performance improvement compared to existing tools, particularly when processing large-genome, high-coverage datasets--such as completing the analysis of HEK293 22G data in 5 minutes-- demonstrating its potential for large-scale data analysis. Availability and implementationThe manual is available at Readthedocs (https://modtector.readthedocs.io/), and source code is available at GitHub (https://github.com/TongZhou2017/modtector) and Crate.io (https://crates.io/crates/modtector). Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/681300v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1535c98org.highwire.dtl.DTLVardef@1e40f39org.highwire.dtl.DTLVardef@1a37476org.highwire.dtl.DTLVardef@3315c5_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Impacts of perinatal factors on white matter outcome at 8 to 10 years by diffusion tensor imaging

BackgroundWhile perinatal factors are known to influence brain development, their long-term impact on white matter microstructure remains incompletely understood. Previous studies using tract-based spatial statistics (TBSS) have shown limited associations between neonatal measures and later white matter development. MethodsWe investigated associations between perinatal factors (birth weight [BW], gestational age [GA], and head circumference [HC]) and white matter microstructure in 117 children aged 8-10 years from the UNC Early Brain Development Study cohort. Diffusion tensor imaging (DTI) data were analyzed using a fiber tract-based framework examining 54 major white matter tracts. Statistical analysis was performed using a functional analysis of fiber tract profiles. ResultsGA and BW showed widespread significant associations with white matter microstructure (38 and 36 out of 54 tracts, respectively), while HC showed limited associations (3 out of 54 tracts). Post-hoc univariate analysis revealed stronger associations with axial diffusivity (AD) compared to radial diffusivity (RD) or fractional anisotropy (FA). AD associations with BW, GA, and HC were found in 30, 31, and 8 tracts, respectively. ConclusionsUsing a fiber tract-based analysis approach, we demonstrated that GA and BW are strongly predictive of white matter organization at school age, while HC showed limited predictive power. The predominant associations with AD suggest these perinatal factors primarily influence axonal organization rather than myelination. These findings enhance our understanding of how early life factors impact long-term brain development.

neuroscience↗

bioRxiv2025

3D-printing-assisted, microfabricated devices reveal hierarchical and temporal mechanosensing in high-density fibroblast culture

Understanding how cells integrate mechanical forces across multiple directions, length scales, and timescales remains a fundamental challenge in mechanobiology. Deciphering how cells integrate this information is particularly important in the context of wound healing, where the timing and duration of the fibroblast-to-myofibroblast transition can determine healing outcomes. Here, we discovered that fibroblasts in engineered tissues respond to directional anisotropy in stress through a hierarchical temporal cascade, with individual cell elongation (24 hr) preceding collective alignment (40 hr), which then drives -smooth muscle actin expression and myofibroblast transition (96h). To enable this discovery, we developed a modified hydrogel-assisted stereolithographic elastomer (HASTE) prototyping platform to incorporate a detergent that improves wettability of template agar hydrogels by poly(dimethylsiloxane) elastomer. HASTE allowed rapid prototyping of intricate 3D micropost arrays that provides isotropic (8-post) versus anisotropic (4-post) boundary conditions. Fibroblasts sensed and responded to stress directionality before bulk tissue reorganization occurs. Computational modeling predicted steady-state activation patterns based on initial stress anisotropy rather than magnitude, and our experiments reveal that reaching this state requires sequential mechanosensitive processes operating across distinct timescales. This temporal hierarchy persists even when extensive cell-cell contacts might be expected to mask matrix-mediated mechanical signals. Our findings demonstrate that fibroblast mechanosensing involves adaptive responses encoded through progressive cell and tissue reorganization. Results provide insight into how nanoscale mechanosensing scales up to direct tissue-level organization, with implications for understanding wound healing, understanding fibrosis, and engineering functional tissue replacements.

bioengineering↗

bioRxiv2025

Therapeutic Potential of a Tectorigenin Derivative (TED) in Herpetic Stromal Keratitis: Antiviral and lmmunomodulatory Mechanisms

ObjectivesIn this study, the anti-HSV-1 effects of TED were investigated in vitro and in vivo, its therapeutic efficacy against herpetic stromal keratitis (HSK) was evaluated, and its mechanism of action was examined. MethodsWe assessed the cytotoxicity and anti-HSV-1 activity of TED in Vero cells using CCK-8 and plaque assays. The HSK model mice received TED (2.5, 5, or 15 mg/kg), and symptom progression was monitored. The therapeutic effects were evaluated through HE staining, corneal immunofluorescence, and HSV-1 gB quantification. Mechanistic studies were performed to examine the expression of TLR pathway genes, CD4+/CD8+ T-cell infiltration in the cornea and draining lymph nodes, and dendritic cell activation. ResultsTED showed potent anti-HSV-1 activity in vitro and ameliorated HSK in mice, improving body weight and reducing corneal pathology and the incidence of encephalitis. It decreased HSV-1 loads in the cornea, trigeminal ganglion, and brain while modulating cytokines (downregulating IFN-/{gamma}, TNF-, and IL-1{beta} and upregulating IL-10). TED suppressed the TLR pathway (TLR2/3/9, TRAF6, IRF3, and NF-{kappa}B) in the corneal epithelium and reduced CD4+/CD8+ T-cell and dendritic cell infiltration while increasing the number of CD4+ T cells in the lymph nodes. ConclusionsTED has significant anti-HSV-1 activity and effectively treats HSK by reducing viral loads, improving symptoms, and modulating immune responses through TLR pathway inhibition and immune cell regulation, suggesting its potential as an HSK therapeutic.

pharmacology and toxicology↗

bioRxiv2025

Ex vivo and in vivo CRISPR/Cas9 screenings identify the roles of protein N-glycosylation in regulating T-cell activation and functions

Cytotoxic CD8+ T-cells play central roles in tumor immunotherapy. Understanding mechanisms that regulate development, differentiation, and functions of cytotoxic CD8+ T-cells leads to development of better immunotherapies. By combining primary T-cell culture and a syngeneic mouse tumor model with both genome-wide and custom CRISPR/Cas9 screenings, we systematically identified genes and pathways that regulate PD-1 expression and functions of CD8+ T-cells. Among them, inactivation of a key enzyme in glycoconjugate biosynthesis, beta 1, 4-galatosyltransferase 1 (B4GALT1), leads to significantly enhanced T-cell receptor (TCR) activation and functions of CD8+ T-cell. Interestingly, suppression of B4GALT1 enhances functions of TCR-T-cells, but has no effect on chimeric antigen receptor T (CAR-T) cells. We systematically identified the substrates of B4GALT1 on CD8+ T-cell surface by affinity purification and mass spectrometry analysis, which include protein components in both TCR and its co-receptor complexes. The galactosylation of TCR and CD8 leads to reduced interaction between TCR and CD8 that is essential for TCR activation. Artificially tethering TCR and CD8 by a TCR-CD8 fusion protein could bypass the regulation of B4GALT1 in CD8+ T-cells. Finally, the expression levels of B4GALT1 normalized to tumor infiltrated CD8+ T-cells in tumor microenvironment are significant and negatively associated with prognosis of human patients. Our results reveal the important roles of protein N-glycosylation in regulating functions of CD8+ T-cells and prove that B4GALT1 is a potential target for tumor immunotherapy.

immunology↗

bioRxiv2025

UHRF1 restricts HCoV-229E infection through epigenetic silencing of the viral receptor APN

The emergence of SARS-CoV-2 has posed significant threats to global health, particularly for the older population. Similarly, common human coronaviruses, such as HCoV-229E, which typically cause mild cold-like symptoms, have also been linked to severe diseases, underscoring the need to understand virus-host interactions and identify host factors contributing to viral pathogenesis and disease progression. In this study, we performed a genome-wide CRISPR knockout screen using HCoV-229E and identified Ubiquitin-like with PHD and RING finger domain 1 (UHRF1) as a potent restriction factor. Mechanistically, UHRF1 suppressed HCoV-229E infection by downregulating the expression of its cell entry receptor, aminopeptidase N (APN), through promoter hypermethylation. Focused CRISPR activation screens of UHRF1-downregulated genes confirmed the critical role of APN in HCoV-229E infection and identified additional genes (e.g., SIGLEC1, PLAC8, and heparan sulfate biosynthesis genes) contributing to the restrictive functions of UHRF1. Transcriptomic and single-cell RNA sequencing analysis revealed that UHRF1 expression decreases with age, negatively correlating with increased APN expression. This age-related decline in UHRF1 was further validated in primary alveolar macrophages isolated from elderly individuals, which exhibited heightened susceptibility to HCoV-229E infection compared to those from younger individuals. Our findings highlight UHRF1 as a key age-related host defense factor against coronavirus infection and provide novel insights into the epigenetic regulation of viral entry receptors.

microbiology↗

bioRxiv2025

Rewiring amino acid metabolism to boost nonnatural polyketide production in Yarrowia lipolytica

Malonyl-CoA is a central precursor involved in the synthesis of various bio-based chemicals, including polyketides, fatty acids, and flavonoids. However, the production of these chemicals is often limited by the availability of malonyl-CoA. Based on retrosynthesis principles, we designed two thermodynamically favorable malonyl-CoA pathways using L-glutamate and L-aspartate as substrates. The novel pathways leverage oxidative deamination and decarboxylation reactions and efficiently channel metabolic flux toward malonyl-CoA, resulting in increased production of total polyketides beyond the capacity of the native acetyl-CoA carboxylase route using glucose as substrate. We also discovered a new-to-nature polyketide (4-hydroxy-6-hydroxyethyl-2-pyrone) derived from the side activity of the TAL pathway, reaching 6.4 g/L in Y. lipolytica. This work highlights the utility of the novel malonyl-CoA pathways in enhancing polyketide production, and the possibility of upcycling abundant amino acids or protein waste in the animal farming or meat industry to produce high-value nonnatural polyketides. HighlightsO_LITwo thermodynamically favorable malonyl-CoA pathways in Yarrowia lipolytica using L-glutamate and L-aspartate as substrates were engineered. C_LIO_LIThe novel oxygen-dependent malonyl-CoA pathways outperformed the canonical acetyl-CoA carboxylase pathway in polyketide production. C_LIO_LIA new-to-nature polyketide, 4-hydroxy-6-hydroxyethyl-2-pyrone (HHEP), was produced at a remarkably high titer of 6.4 g/L in shaking flask. C_LIO_LIThe engineered pathway improved the total production of malonyl-CoA-derived compounds (TAL, HHEP, and lipids). C_LI

synthetic biology↗

bioRxiv2025

Tracing colorectal malignancy transformation from cell to tissue scale

The transformation of normal intestinal epithelium into colorectal cancer (CRC) involves coordinated changes across molecular, cellular, and architectural scales; yet, how these layers integrate remains poorly resolved. Here, we survey colorectal tumorigenesis by combining whole-transcriptome spatial molecular imaging (WTx CosMx SMI) with single-nucleus RNA-sequencing (snPATHO-seq) and digital histopathology on colon samples containing reference mucosa, adenomas and carcinomas, as well as a metastatic lymph node. Leveraging (discrete) histological annotations and (continuous) data-driven trajectories, we quantify the dynamics of cellular density, heterogeneity, function and signaling along the reference-adenoma-carcinoma axis, which is concordant in its spatial and molecular definition. This combination of analytical approaches across different data views let us chart tissue transformation across dimensions (physical/transcriptional) and scales (cell/tissue). We resolve [~]3.5 million cells into 43 epithelial, immune, and stromal subpopulations that exhibit a bi-furcating tumor evolution: On the one hand, LGR5+ stem-like epithelial cells are enriched in highly homogeneous proliferative tumor cores. On the other hand, MMP7+ fetal-like states are restricted to immunosuppressive invasive fronts, rich in cancer-associated fibroblasts (CAFs) and tumor-associated macrophages. These subpopulations form concentric spatial layers that organize transformed regions, and their compound aligns with histological malignancy. We further define transition crypts - single colonic crypts with divided histological and transcriptional makeup - that arise from rare crypt fusion or abrupt transformation events. Finally, we trace MMP7+ fetal-like tumor cell states and concomitant myofibroblast-like FAP+ CAFs to lymphovascular invasion sites and matched lymph node metastases, thereby recapitulating invasive programs at single-cell resolution and across sites. In all, we present single cell- and WTx-resolved spatial data that are among the first of their kind. These open up spatial-centric, out-of-the-box analytical avenues to resolve the molecular, cellular and architectural dynamics that attend tissue transformation during CRC onset, progression and dissemination.

cancer biology↗

bioRxiv2025

A Task-Specific Transfer Learning Approach to Enhancing Small Molecule Retention Time Prediction with Limited Data

Liquid chromatography (LC) is an essential technique for separating and identifying compounds in complex mixtures across various scientific fields. In LC, retention time (RT) is a crucial property for identifying small molecules, and its prediction has been extensively researched over recent decades. The wide array of columns and experimental conditions necessary for effectively separating diverse compounds presents a challenge. Consequently, advanced deep learning for retention time prediction in real-world scenarios is often hampered by limited training data that spans these varied experimental setups. While transfer learning (TL) can leverage knowledge from upstream datasets, it may not always provide an optimal initial point for specific downstream tasks. We consider six challenging benchmark datasets from different LC systems and experimental conditions (100-300 compounds each) where TL from RT datasets under standard condition fails to achieve satisfactory accuracy (R2 [≥] 0.8), highlighting the need for more sophisticated TL strategies that can effectively adapt to the unique characteristics of target chromatographic systems under specific experimental conditions. We present a task-specific transfer learning (TSTL) strategy that pre-trains multiple models on distinct large-scale datasets, optimizing each for fine-tuned performance on the specific target task, then integrates them into a single model. Evaluated on five deep neural network architectures across these six datasets through 5-fold cross-validation, TSTL demonstrated significant performance improvements with the average R2 increasing from 0.587 to 0.825. Furthermore, TSTL consistently outperformed conventional TL across various sizes of training datasets, demonstrating superior data efficiency for RT prediction under various experimental conditions using limited training data. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/661631v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1330e45org.highwire.dtl.DTLVardef@ee1126org.highwire.dtl.DTLVardef@1a7f2fdorg.highwire.dtl.DTLVardef@141cb26_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Highly accurate protein structure prediction-based virtual docking pipeline accelerating the identification of anti-schistosomal compounds

Schistosomiasis is a major neglected tropical disease that lacks an effective vaccine and faces increasing challenges from praziquantel resistance, underscoring the urgent need for novel therapeutics. Target-based drug discovery (TBDD) is a powerful strategy for drug development. In this study, we utilized AlphaFold to predict the structures of target proteins from Schistosoma mansoni and Schistosoma japonicum, followed by virtual molecular screening to identify potential inhibitors. Among 202 potential therapeutic targets, we identified 37 proteins with high-accuracy structural predictions suitable for molecular docking with 14,600 compounds. This screening yielded 268 candidate compounds, which were further evaluated in vitro for activity against both adult and juvenile S. mansoni and S. japonicum. Seven compounds exhibited strong anti-schistosomal activity, with HY-B2171A (Carubicin hydrochloride, CH) emerging as the most potent. CH was predicted to target the splicing factor U2AF65, and knockdown of its coding gene Smp_019690 resulted in a phenotype similar to CH treatment. RNA sequencing revealed that both CH treatment and Smp_019690 RNAi disrupted splicing events in the parasites. Further studies demonstrated that CH impairs parasite viability by inhibiting U2AF65 function in mRNA splicing regulation. By integrating RNAi-based target identification with structure-based virtual screening, alongside in vitro phenotypic and molecular analyses of compound-treated schistosomes, our study provides a comprehensive framework for anti-schistosomal drug discovery and identifies promising candidates for further preclinical development. Author SummarySchistosomiasis is a debilitating disease caused by parasitic worms and affects over 230 million people worldwide. For decades, control of the disease has relied on a single drug, praziquantel, which is less effective against juvenile parasites and increasingly threatened by the risk of drug resistance. To accelerate the discovery of new treatments, we integrated advanced computational tools with molecular validation. Using AlphaFold-predicted structures of 37 key schistosome proteins, we performed large-scale virtual screening to identify promising drug candidates. Over 200 compounds were prioritized for phenotypic screening on two major human-infective schistosome species, leading to the discovery of seven with potent anti-parasitic activity. Among them, Carubicin hydrochloride showed strong efficacy by targeting a critical splicing factor, U2AF65. Inhibiting this protein--either chemically or through gene silencing-- disrupted parasite movement, suppressed cell proliferation, and altered splicing events. Our work highlights the power of integrating AI-based protein modeling with phenotypic and mechanistic validation to accelerate drug discovery for neglected tropical diseases like schistosomiasis.

pathology↗

bioRxiv2025

Advanced adaptive strategies in an ancestral body plan: insights from a 510-Ma-old leptomitid sponge

Sponges have thrived in diverse environmental conditions since the early Cambrian till today. However, little is known about how their adaptive capability and strategies have been shaped throughout evolutionary history. Here, we explore this question based on a new leptomitid sponge fossil from the Cambrian Stage 4. The family Leptomitidae was an abundant sponge group inhabiting Cambrian soft substrates but significantly declined thereafter. The new species exhibits a sophisticated set of morphological characteristics adaptive to a shallow siliciclastic environment, which are unprecedented among leptomitids. These include (1) a robust body wall woven by spirally twisted monaxonic spicules; (2) a thick stub-like root tuft for anchoring; (3) spicules radiating out from the sponge body to prevent clogging and sinking; and (4) the inferred capability to close the osculum against unfavourable stimuli. Nevertheless, the new fossil species maintains a leptomitid body plan which lacks modularity and morphological plasticity, the two common and critical attributes in extant sponges to enhance flexibility and resilience in changing environmental conditions. This juxtaposition of evolutionary innovation and structural conservatism offers a compelling case for further exploration of the evolutionary mechanisms that shaped early sponge lineages.

paleontology↗

bioRxiv2025

Adhesive implant interfaces prevent fibrosis by disrupting mechanobiological feedback

Fibrotic encapsulation around medical implants affects millions of patients annually. Current approaches targeting inflammation or implant material properties have failed clinically, but the mechanical origins of implant-induced fibrosis remain unexplored. Here, we demonstrate that directional imbalance of mechanical forces ("tension anisotropy") is the primary driver of fibroblast activation at implant-tissue interfaces, and that it can be eliminated through adhesive bonding strategies. Computational modeling reveals a mechanistic basis for successful adhesive anti-fibrotic interfaces: conventional sutured implants generate highly anisotropic stress fields between discrete suture anchor points that activate fibroblasts, while adhesive interfaces distribute forces isotropically, maintaining a mechanical environment that does not activate fibroblasts. In vivo experiments from the literature across multiple animal models confirm these predictions: as predicted, adhesive interfaces completely prevent fibrotic capsule formation for up to 12 weeks across diverse organs, while maintaining identical implant composition and geometry compared to sutured controls. Results establish tension anisotropy as a mechanical regulator of implant fibrosis and provide a mechanistic foundation explaining why adhesive interfaces succeed where all previous anti-fibrotic strategies have failed. By addressing the root mechanical cause of fibrosis, this mechanobiology-driven approach may enable a universal approach for preventing fibrosis across all categories of implantable medical devices. Significance statementMillions of patients suffer from medical device failure due to fibrotic encapsulation, in which a surgically implanted item such as pacemaker leads or a vascular graft loses function by becoming covered with scar tissue. Implants affixed to soft tissues by sutures are especially prone to this form of failure, but implants affixed with a recently invented adhesive are not. We present the discovery that directional imbalance of forces ("tension anisotropy") drives conversion of healing tissue into scar tissue. Conventional sutured implants create highly anisotropic stress fields between anchor points that activate fibroblasts, while adhesive interfaces distribute forces isotropically, attenuating scarring. This mechanistic insight explains why adhesive implant-tissue interfaces successfully prevent fibrotic capsule formation across multiple animal models and organ systems, where all previous anti-fibrotic approaches have failed. By addressing root mechanical causes of fibrotic remodeling, this discovery provides a pathway for clinical remediation of fibrotic encapsulation.

biophysics↗

bioRxiv2025

Integrin-Specific Signaling Drives ER Stress-Dependent Atherogenic Endothelial Activation

Atherogenic endothelial activation arises from both the local arterial microenvironment--characterized by altered extracellular matrix composition and disturbed blood flow--and soluble proinflammatory stimuli such as oxidized low-density lipoprotein (oxLDL). Fibronectin, a provisional extracellular matrix protein enriched at atheroprone sites, enhances endothelial activation and inflammation triggered by oxLDL and disturbed flow. Although endoplasmic reticulum (ER) stress contributes to vascular dysfunction, the role of matrix composition in regulating ER stress remains unknown. We show that oxLDL and disturbed flow induce ER stress selectively in endothelial cells adhered to fibronectin, whereas both stimuli fail to induce ER stress in cells on basement membrane proteins. This matrix-specific ER stress response requires integrin activation, as endothelial cells deficient for integrin activation (talin1 L325R mutation) fail to activate ER stress in response to disturbed flow and oxLDL and direct stimulation of integrin activation using CHAMP peptides is sufficient to trigger ER stress. Blunting endothelial expression of fibronectin-binding integrins (5, v) using siRNA prevents ER stress in response to atherogenic stimuli in vitro, whereas endothelial 5 and v deletion reduces ER stress at atheroprone sites in vivo. The mechanisms driving integrin-dependent ER stress remain unclear, since matrix composition does not affect protein translation, unfolded protein accumulation, or superoxide production, and scavenging superoxide (TEMPOL) does not reduce integrin-dependent ER stress. Inhibiting ER stress with TUDCA reduces proinflammatory and metabolic gene expression (bulk RNAseq) but does not prevent NF-{kappa}B activation, a classic proinflammatory transcription factor. Rather, TUDCA prevents activation of c-jun N-terminal kinase (JNK) and c-jun activation, and blocking JNK (SP600126) or c-Jun activity (TAM67) prevents proinflammatory gene expression following both stimuli. Together, these findings offer new insight into how the arterial microenvironment contributes to atherogenesis, with fibronectin-binding integrin signaling promotes ER stress in response to mechanical and metabolic stressors, thereby amplying proinflammatory endothelial activation through JNK-c-Jun signaling. HighlightsO_LIFibronectin promotes endothelial ER stress in response to oxLDL and disturbed flow via integrin 5{beta}1 and v{beta}3 signaling. C_LIO_LIMatrix-specific ER stress occurs independently of oxidative stress or misfolded protein accumulation, indicating a non-canonical UPR activation. C_LIO_LIER stress amplifies proinflammatory gene expression through JNK-c-Jun signaling, without activating NF-{kappa}B. C_LIO_LITargeting the integrin-ER stress-JNK axis may offer new therapeutic strategies for early atherosclerosis. C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/654582v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@ed7ca3org.highwire.dtl.DTLVardef@191c238org.highwire.dtl.DTLVardef@660f3dorg.highwire.dtl.DTLVardef@7287a4_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

pathology↗