Biology subjects

Liu, Y.

Publications and source records attributed to Liu, Y..

At least 505 records · Page 28Linked to original sources

bioRxiv2025

Emergence of hypervirulent Klebsiella pneumoniae ST111 with a novel virulence plasmid causing pyogenic liver abscess

Hypervirulent Klebsiella pneumoniae (hvKp)-associated pyogenic liver abscess is typically linked to K1/K2 capsule serotypes and canonical lineages such as ST23, ST65, and ST86. Cases outside these lineages are rare but pose significant public health concerns. We investigated an unusual liver abscess in an immunocompetent patient caused by hvKp sequence type 111 (ST111). Five isolates (KP1-KP5) were recovered, all belonging to the same clonal lineage. Genomic analysis revealed a 181 kb IncHI1B/IncFIB virulence plasmid encoding rmpADC and the salmochelin cluster (iroBCDN), but lacking aerobactin (iuc) and rmpA2 gene loci. Notably, this plasmid also carried the yersiniabactin locus (ybt), which is typically located on the chromosome. Comparative genomics indicated that similar ST111 strains have been reported only in China, suggesting clonal expansion of a previously unrecognized hvKp lineage. Functional assays demonstrated that plasmid curing abolished the hypermucoviscous phenotype, reduced capsule production, and completely attenuated virulence in murine models, confirming the plasmid as the major driver of hypervirulence. Interestingly, plasmid loss enhanced biofilm formation and relieved growth burden, highlighting fitness trade-offs associated with virulence. This study documents a rare case of hvKp ST111 liver abscess and identifies a novel plasmid conferring hypervirulence. Our findings expand the known diversity of hvKp and underscore that non-canonical lineages can acquire potent virulence determinants enabling severe disease. Continued clinical and genomic surveillance is essential to detect emerging hvKp clones before widespread dissemination.

microbiology↗

bioRxiv2025

Rapid and accurate quantification of viable Listeria monocytogenes with clonal specificity using microfluidic droplet digital PCR based technology

To overcome the technical bottlenecks in the precise quantification and molecular typing of viable foodborne pathogens, this study establishes a microfluidic droplet digital PCR (ddPCR) based method for rapid and accurate detection and quantification of viable Listeria monocytogenes with clonal specificity. In contrast to the time-consuming plate culture methods and unspecific rapid detection methods, the method in this study employs clonal complex (CC) specific primers and probe for strain-specificity and integrate the nucleic acid dye propidium monoazide (PMA) to effectively distinguish viable from dead bacteria. The rapid and precise quantification of viable bacteria is achieved through microdroplet counting. This method does not require DNA extraction, and the entire detection process takes only about 3 hours, with a quantitative detection limit of 3.3x102 CFU/mL, providing strong technical support for the risk monitoring of highly pathogenic specific types of L. monocytogenes.

microbiology↗

bioRxiv2025

Melody: Decoding the Sequence Determinants of Locus-Specific DNA Methylation Across Human Tissues

DNA methylation is a fundamental epigenetic modification that plays crucial roles in transcriptional regulation, cellular differentiation, and genome stability. However, how locus-specific DNA methylation is determined by intrinsic DNA sequence remains poorly understood. Here, we introduce Melody, a deep learning framework that predicts DNA methylation from 10-kb genomic sequences, enabling the integration of both local and long-range sequence signals. Across 39 human tissues, Melody accurately predicts methylation profiles and consistently outperforms existing state-of- the-art methods in whole-chromosome, hypomethylated-region, and cell-type-specific benchmarks. Melody also generalizes to methylation quantitative trait locus (meQTL) effect prediction and identifies regulatory sequence motifs associated with methylation variability. To extend prediction beyond profiled tissues, we further develop Melody-G, which incorporates single-cell RNA-seq foundation model embeddings to infer methylation states in previously unseen cell types directly from transcriptomic data. Together, Melody provides a unified framework for linking genomic sequence and cellular state to DNA methylation and offers new insights into the regulatory logic governing the human methylome.

bioinformatics↗

bioRxiv2025

Integrating physiology and transcriptomics reveals the mechanisms underlying the differences in cotton (Gossypium hirsutum L.) growth and development under different photoperiods

Photoperiod is a crucial environmental factor that regulates plant growth and flowering. While the photoperiodic flowering pathway has been extensively studied, the mechanisms underlying photoperiodic regulation of cotton (Gossypium hirsutum L.) growth and development remain unclear. In this study, we selected different genotypes of cotton varieties, XJ12 and XJ21-11, to investigate the effects of long-day (LD) and short-day (SD) conditions on cotton photosynthesis, dry matter accumulation, flowering time, and related gene expression, utilizing physiological index measurements, transcriptomics, and WGCNA analysis. The results indicated that LD treatment significantly enhanced photosynthetic parameters and dry matter weight in both cotton varieties, with the net photosynthetic rate (Pn) being 154.79% and 111.86% higher under LD compared to SD treatment. Additionally, the flowering time of the two varieties was advanced by 12 and 13 days, respectively, under LD treatment relative to SD treatment. LD conditions promoted gene expression in sucrose and starch metabolism, facilitating cotton growth. Furthermore, LD inhibited the expression of genes associated with flavonoid biosynthesis, thereby altering the secondary metabolism of cotton, and activated transcription factors through signaling pathways (including phytohormone signaling and MAPK signaling pathways) and biological clock rhythms, which induced earlier flowering in cotton. This study provides new insights into the molecular mechanism of the effect of photoperiod on cotton growth and development.

physiology↗

bioRxiv2025

Sphingosine-1-phosphate receptor modulators resensitize FLT3-ITD acute myeloid leukemia cells with NRAS mutations to FLT3 inhibitors

FLT3 inhibitor efficacy in AML with FLT3-ITD is short-lived, frequently due to new mutations, most commonly in NRAS. Sphingosine kinase 1 (SPHK1), which phosphorylates sphingosine to generate sphingosine-1-phosphate (S1P), is upregulated and localized to the plasma membrane in RAS-mutated cells. We studied S1P and FLT3 co-targeting to overcome FLT3 inhibitor resistance in NRAS-mutated FLT3-ITD AML cells. NRAS-mutated FLT3-ITD AML cell lines and patient blasts were treated with FLT3 inhibitors and/or S1P receptor (S1PR) modulators. FLT3 inhibitor sensitivity was assessed by immunoblotting, cytotoxicity and apoptosis assays. Co-treatment was also assessed in vivo in an orthotopic mouse model. Downstream RAS and SPHK1 effectors were measured by immunoblotting and qRT-PCR. The S1PR modulators fingolimod (FTY720) and mocravimod (KRP-203) resensitized FLT3-ITD-expressing MOLM-14 and MV4-11 human AML cells with G12D, G12S, Q61K or Q61H, but not G12C, and patient blasts with G13D or G13V NRAS mutations to FLT3 inhibitors. Moreover, FTY720 co-treatment resensitized G12D NRAS-mutated M14(R)701 cells to gilteritinib in vivo. Co-treatment inactivated ERK, transcriptionally downregulated SPHK1, and inactivated downstream AKT, p70S6K and BAD, with inactivation abrogated by constitutive SPHK1 expression. The clinically applicable S1PR modulators fingolimod and mocravimod resensitize NRAS-mutated FLT3-ITD AML cells to FLT3 inhibitors, supporting potential clinical efficacy of these combinations.

cancer biology↗

bioRxiv2025

Engineered Transdermal Peptide-Recombinant Type III Collagen Hydrogel with Biological Efficacy in Cell Proliferation and Wound Healing

Collagen, as a major structural protein in the extracellular matrix, plays a crucial role in tissue regeneration; however, traditional collagen sources often suffer from immunogenicity, poor stability, and batch-to-batch variability. In this study, a novel transdermal peptide-recombinant type III collagen hydrogel was developed, which has excellent biocompatibility, thermal stability, and skin repair effects. Transdermal peptide-recombinant type III collagen was expressed and purified by genetic engineering methods, and its potential for biomedical applications was further evaluated. The experimental results showed that the prepared collagen had a high purity (95%) and retained the unique secondary structure of collagen, showing good structural stability at different pH values. Through cell proliferation experiments and mouse wound healing experiments, we verified the superior effect of the collagen hydrogel in promoting wound healing and significantly accelerating wound healing at concentrations as low as 0.2 mg/mL. This achievement provides strong experimental support for its application in clinical skin wound repair.

biochemistry↗

bioRxiv2025

A stress-dependent TRIM28-ALKBH2 feedback loop modulates chemotherapy response

Chemoresistance to DNA-damaging agents, including platinum and alkylating compounds, limits treatment efficacy in non-small cell lung cancer (NSCLC) and is frequently associated with elevated DNA repair activity. Here, we identify a stress-responsive feedback loop between the E3 ligase TRIM28 and the demethylase ALKBH2. TRIM28 binds ALKBH2 and promotes its K48-linked polyubiquitination and proteasomal degradation, whereas ALKBH2 enhances TRIM28 transcription and protein levels, forming a feedback loop. Notably, alkylation stress induces a biphasic response: acute MMS exposure enhances TRIM28-ALKBH2 association and accelerates ALKBH2 degradation, whereas prolonged exposure promotes TRIM28 degradation, leading to ALKBH2 stabilization and transcriptional upregulation. Clinically, ALKBH2 is frequently elevated in lung adenocarcinoma and is associated with worse survival, whereas TRIM28 exhibits prognostic value specifically in chemotherapy-treated patients. Functionally, MMS-cisplatin co-treatment increased DNA damage and reduced clonogenic survival by counteracting ALKBH2-dependent alkylation tolerance, linking the TRIM28-ALKBH2 loop to chemoresistance in NSCLC.

biochemistry↗

bioRxiv2025

Rv3400 is a phosphoglucomutase required for trehalose metabolism in Mycobacterium tuberculosis

Mycobacterium tuberculosis is a global killer causing over a million deaths from tuberculosis (TB) every year. It is therefore a major burden on human health. To reduce the deadly impact of TB, we need a better understanding of the strategies used by M. tuberculosis to adapt its metabolism to survive and persist in the human host. This will help us design better strategies for TB treatment and control. Previous enzymological studies have reported the Mtb rv3400 gene as encoding a {beta}-phosphoglucomutase; however, its role in M. tuberculosis metabolism was not investigated. In the present study, we show that deletion of rv3400 leads to a 30-fold increase in {beta}-D-glucose 1-phosphate, confirming its primary function as a {beta}-phosphoglucomutase. Additionally, deletion of rv3400 leads to a growth defect when trehalose is the sole carbon source indicting that this enzyme is required for optimal use of trehalose, an essential disaccharide in mycobacteria. ImportanceTrehalose metabolism plays a corner stone in Mycobacterium tuberculosis physiology and virulence. Hence, a better understanding of the metabolism of this essential disaccharide is required to develop novel strategies to eradicate Tuberculosis. Here we report on the characterisation of the strain lacking a {beta}-phosphoglucomutase, encoded by the gene rv3400. We show that deletion of rv3400 leads to an increase of {beta}-D-glucose 1-phosphate and a growth defect when using trehalose as sole carbon source. Taken together, the data presented here provide evidence that Rv3400 is required for the catabolism of trehalose.

microbiology↗

bioRxiv2025

Scalable Human Cellular Models of Parkinson Disease Reveal A Druggable Link Between the Angiotensin Receptor 1 and Alpha-Synuclein Pathology

BackgroundParkinsons disease (PD) involves progressive loss of midbrain dopaminergic (mDA) neurons in the substantia nigra. No disease-modifying treatments exist, only symptomatic relief. Our lab reported an unbiased screen in larval zebrafish identifying renin-angiotensin-aldosterone system (RAAS) inhibitors, including clinically used AGTR1 inhibitors for hypertension, as potent neuroprotective agents. This study aims to investigate the effects of AGTR1 inhibition on human mDA neuron survival using inducible neurodegenerative 2D and 3D models for human mDA neuron degeneration. MethodsWe report a scalable high-content platform, using CRISPR-engineered human induced pluripotent stem cell (hiPSC)-derived mDA neurons expressing a tyrosine hydroxylase (TH) fluorescent reporter, allowing to track mDA neuron survival live "in a dish". We developed chemically inducible neurodegenerative 2D and 3D models for human mDA neuron degeneration, allowing to recapitulate PD pathology in human cells in vitro. ResultsOur model establishes scalable human cellular models of PD well-suited for therapeutic discovery. Using 2D and 3D mono and co-cultures, this study demonstrates that inhibition of AGTR1, via chemical or genetic means, protects against chemically induced mDA neuron degeneration. Transcriptomic analyses show AGTR1 inhibition lowers synuclein transcription, by reducing SNCA and SNCB gene expression. In 3D neuron-glia assembloids, AGTR1 inhibition protects against the accumulation of phosphorylated form of -synuclein (p129-Syn), key PD pathological marker. ConclusionsWe highlight AGTR1 as a key regulator of -synuclein transcription and aggregation in human mDA neurons, and AGTR1 inhibition as pro-survival in human iPSC-derived models. These findings position inhibition of AGTR1 as a promising therapeutic strategy for PD neuroprotection.

neuroscience↗

bioRxiv2025

A Syngeneic Immunocompetent Mouse Model of Gallbladder Cancer Reveals Tumorigenesis and Therapeutic Response

PurposeGallbladder cancer (GBC) is a rare but highly lethal malignancy with one of the poorest prognoses among cancers of the digestive system cancers. However, current experimental models of GBC face critical limitations, particularly their dependence on immunodeficient hosts, which precludes the investigation of the tumor microenvironment. We aimed to establish a novel syngeneic immunocompetent mouse model that recapitulates human GBC and enables the investigation of tumor-immune interactions. Experimental DesignWe engineered a murine cell line (mGBC1-ZH) from normal mouse gallbladder organoids expressing Kras and Trp53 (encoding mouse p53) mutations. Tumorigenic potential of mGBC1-ZH was evaluated by subcutaneous and orthotopic implantation. RNA-Seq and WES was used to demonstrate its characterization and similarity with human GBC. Immunohistochemistry, CCK8, and transwell assays wereused to investigate the role of CXCL5 in GBC. Therapeutic responses to standard first-line chemotherapeutic agents was evaluated in the syngeneic GBC model. ResultsThis model supports both subcutaneous and orthotopic tumor growth in immunocompetent C57BL/6J hosts while preserving hallmark features of human GBC, including biliary epithelial differentiation (CK7+/CK19+), aggressive histopathology, and an immunosuppressive "cold" tumor microenvironment. Genomic characterization revealed recurrent chromosomal instability and copy number alterations mirroring human GBC. Comprehensive transcriptomic profiling revealed profound gene expression changes during the model development, and resembled transcriptional features between mGBC1-ZH and human GBC cell lines and samples. CXCL5 was found to be upregulated in human GBC, and promote proliferation, metastasis, and neutrophils infiltration of GBC in the syngeneic mouse model. Functional validation of the syngeneic GBC model demonstrated therapeutic responsiveness to frontline chemotherapeutics gemcitabine and cisplatin with significant in vivo tumor regression. ConclusionsIn summary, we establised a novel syngeneic GBC mouse model, overcoming the limitations of traditional models by enabling studies in immunocompetent hosts. This model provides valuable insights into the molecular evolution from normal gallbladder cells to transformed cancer cells and establishes a robust platform for both mechanistic studies and therapeutic development.

cancer biology↗

bioRxiv2025

Key determinants of T cell epitope recognition revealed by TCR specificity profiles

Interactions between T-Cell Receptors (TCRs) and antigenic peptides presented on Major Histocompatibility Complex (MHC) molecules are central to the immune recognition of infected and malignant cells. The complexity of the TCR sequence space, the flexibility of the TCR-epitope interface and the lack of a standardized framework to visualize TCR specificity have hindered a comprehensive understanding of the principles governing TCR-epitope recognition across a broad range of epitopes. Here, we introduce a fully interpretable probabilistic framework, termed TCR specificity profiles (TSPs), which captures fundamental properties distinguishing epitope-specific from baseline TCR repertoires. We demonstrate that TSPs unravel key determinants of TCR-epitope recognition specificity. By identifying and analyzing TCRs recognizing dozens of epitope variants, we show that TSPs accurately predict cross-reactivity and reveal how TCR specificity evolves with epitope sequence, binding mode and MHC restriction. TSPs further enable interpretation of machine learning tools and reveal how AlphaFold3 can be used to decipher key determinants of TCR-epitope recognition specificity.

immunology↗

bioRxiv2025

Deep learning reveals a microprotein atlas in maize and uncovers novel regulators of seed amino acid metabolism

Microproteins represent a vast and functionally important class of genes that remain largely unexplored in plant genomes. Here, we developed DeepMp, a deep learning framework that integrated multi-omics evidence and built the most comprehensive plant microprotein atlas to date, identifying 18,338 high-confidence candidates in maize. The majority of these appear to have originated de novo from regions previously annotated as noncoding, and they show hallmarks of rapid, lineage-specific evolution and pronounced tissue specificity. These novel microproteins were found integrated into core regulatory networks, particularly in organ development and nutrient storage. Focusing on the maize kernel, population-scale analyses linked microprotein expression to natural variation in amino acid content. We functionally validated three grain-filling-specific candidates originating from noncoding regions by CRISPR-Cas9 knockouts, which confirmed their roles as precise modulators of arginine, aspartate, and methionine levels, without pleiotropic effects on kernel morphology. Our study provides a foundational resource and analytical framework, establishes microproteins as a new and functionally important coding layer in maize, and uncovers a previously untapped source of targets for crop improvement.

genomics↗

bioRxiv2025

ecDNA-driven oncogene super-expressors shape immunoevasive tumor microenvironment

ecDNA contributes to cancer genetic heterogeneity through random segregation during mitosis. Emerging evidence links ecDNA to immune evasion, but the mechanism remains elusive. Using genetically engineered mouse models of pancreatic ductal adenocarcinoma (PDAC), we show that Kras and Myc oncogenes are amplified either on ecDNAs or as homogeneously staining regions (HSRs) on chromosomes. ecDNA-driven tumors are more aggressive in immunocompetent mice. Single-cell transcriptomic and histological analyses reveal that ecDNA-driven tumors rapidly establish an immunoevasive tumor microenvironment (TME), marked by increased myofibroblastic cancer-associated fibroblasts (myCAFs) and reduced T cell infiltration. Mechanistically, ecDNA heterogeneity generates a subset of cancer cells with extremely high Kras expression, termed super-expressors, which secrete amphiregulin to promote myCAF expansion and suppress T cell infiltration. Clonally organized super-expressors establish an immunoevasive niche in the TME from patients with PDAC. Our findings demonstrate a causal role of ecDNA in TME remodeling, offering insights into cancer heterogeneity and immune evasion.

cancer biology↗

bioRxiv2025

SpaTranslator: A deep generative framework for universal spatial multi-omics cross-modality translation

Recent advances in spatial omics technologies have enabled the simultaneous analysis of multiple molecular patterns in tissue sections, offering unprecedented insights into cellular microenvironments. However, the high cost of measurements and the sparsity of data restrict the availability of paired spatial multi-omics datasets. Here, we present SpaTranslator, a deep generative framework that integrates graph neural networks with an adversarial variational generative model to fully capture spatial characteristics and enable effective cross-modality translation of spatial omics data, enabling simulation of paired spatial multi-omics data from single-omics measurements. Extensive experiments demonstrate that SpaTranslator consistently outperforms baseline methods in both clustering accuracy and biological coherence across various real-world scenarios, including spatial transcriptomics-epigenomics and spatial transcriptomics-proteomics translation tasks. Furthermore, SpaTranslator provides biologically meaningful insights through marker genes and proteins recovery, motif enrichment analysis, and gene regulation inference. Our work offers an effective and adaptable solution for spatial multi-omics cross-modality translation, supporting a broad range of biological and biomedical research.

bioinformatics↗

bioRxiv2025

Multimodal AI Decodes Extreme Environment Functional Dark Matter Beyond Homology

Functional annotation of proteins from extreme environments represents a major bottleneck for bioresource discovery, as a vast reservoir of functional dark matter defies existing homology-based methods. We demonstrate that environmental pressures impart conserved physicochemical energy signatures that co-determine protein function with sequence and structure. Here we developed ACCESS, a multimodal graph neural network employing hierarchical contrastive learning with a tailored label-sample co-embedding to fuse energy, sequence, and structural information and overcome homology scarcity. ACCESS surpasses state-of-the-art methods including BLASTp and CLEAN in annotating low-identity enzymes. Applied to extreme environmental metagenomics, we constructed a function map of extremophile enzymes to expand the biocatalyst library, pinpointed functionally critical residues to guide rational design, and enabled large-scale, function-based macro-evolutionary analyses. This paradigm transcends the limitations of homology, illuminating protein dark matter and accelerating the exploration of the biospheres functional diversity for applications in biotechnology and therapeutic development.

bioinformatics↗

bioRxiv2025

Rapid and Complete Inactivation of Enveloped Viruses by Electrochemical Disinfection: Unraveling the Contribution of Reactive Chlorine Species

Waterborne viruses pose significant threats to global water safety. Electrochemical (EC) disinfection is considered a promising next-generation technology to address this challenge. However, its efficacy against enveloped viruses remains inadequately under-stood. In this study, we employed bacteriophage Phi6 as a surrogate to systematically eval-uate the inactivation efficiency and damage mechanisms of enveloped viruses in a flow-through EC reactor. Experimental results indicated that Phi6 was more susceptible to EC disinfection than common bacterial surrogates. Comprehensive damage was observed across all major viral components, including structural proteins, the lipid envelope, and the RNA genome. The electrogenerated reactive chlorine species (RCS) were identified as the primary agent responsible for this inactivation, exhibiting higher reactivity than conven-tional free chlorine. Furthermore, radical chlorine species were also confirmed to be pro-duced during EC disinfection, contributing to rapid and extensive viral inactivation. This study demonstrates the feasibility of EC disinfection for inactivating enveloped viruses and provides deeper mechanistic insights into its highly efficient viral inactivation performance. SYNOPSISOur results elucidate the rapid inactivation and thorough damage of the enveloped virus surrogate Phi6 by electrogenerated reactive chlorine species during electrochemical disinfection. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/688411v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@7e8c29org.highwire.dtl.DTLVardef@23fcedorg.highwire.dtl.DTLVardef@b26312org.highwire.dtl.DTLVardef@1864ab1_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

Agentic Lab: An Agentic-physical AI system for cell and organoid experimentation and manufacturing

Reproducibility in biological research and manufacturing remains constrained by the complexity of multi-step protocols, fragmented data-analysis pipelines, and the intrinsic variability of experimental execution. Here, we present Agentic Lab, an agentic-physical AI platform that unifies large language model and vision language model (LLMs/VLMs)-driven reasoning with real-world laboratory operations. Agentic Lab uses multi-agent orchestration architecture, comprising of specialized subagents for knowledge retrieval, protocol design, multimodal data analysis, and training-free segmentation and representation learning for intrinsically explainable single-cell and organoid phenotyping. These agents operate under the orchestration of a virtual principal investigator MolAgent that is linked to an augmented reality (AR)-based physical AI interface, which can bridge digital reasoning with human physical execution. Agentic Lab perceives real-world experimental activities, provides context-aware instructions, identifying procedural errors in real time for humans to correct, and continuously evolves with its long-term memory database expanding through the accumulation of experimental data logs from human scientists. This interaction allows scientists and AI agents to collaborate and co-evolve dynamically, closing the loop between planning, action, and analysis in the traditional cell and organoid research lifecycle. We demonstrate Agentic Lab in organoid differentiation from human pluripotent stem cells, where it autonomously generates protocols, monitors culture procedures, and identifies subtle morphological heterogeneity linked to growth conditions. The system interprets these phenotypes, grounds them in literature, and proposes targeted instructions for improving differentiation efficiency. By combining multi-agent reasoning with physical laboratory awareness, Agentic Lab transforms experimentation and biomanufacturing from a static workflow into an adaptive, feedback-driven, bidirectional process that integrates agentic AI into the research lifecycle. This framework establishes a foundation for intelligent laboratories that integrate design, execution, and interpretation within a unified agentic-physical system.

bioengineering↗

bioRxiv2025

Decorin promotes nascent proteoglycan retention in cartilage matrix by strengthening collagen II-aggrecan integration

Cartilage extracellular matrix (ECM), a hydrated collagen II-aggrecan composite, undergoes dynamic turnover during both normal homeostasis and disease-associated remodeling. This study elucidates a crucial role for decorin in promoting the retention and stability of nascent aggrecan within this matrix. By applying bio-orthogonal click-labeling, we demonstrate that loss of decorin accelerates the release of nascent aggrecan under both physiological and inflammatory conditions, without affecting its preferred localization to the pericellular matrix. Conversely, supplementation with exogenous decorin mitigates inflammation-induced loss of nascent aggrecan, supporting its potential as a therapeutic target. At the molecular level, decorin exhibits strong binding affinity for aggrecan, and enhances aggrecan-aggrecan and aggrecan-collagen II interactions, reinforcing its direct role in integrating cartilage matrix constituents. Also, by binding to collagen II, decorin stiffens the collagen II fibril network, thereby strengthening the confinement effect that limits the diffusive loss of entrapped aggrecan. Notably, decorin does not alter chondrocyte transcriptomic profiles in vivo, emphasizing its primary role in maintaining matrix integrity through biophysical mechanisms rather than cell signaling. Together, these findings provide a mechanistic foundation for developing decorin-based biomaterials or gene therapies aimed at preserving or regenerating the cartilage matrix for improved outcomes in osteoarthritis.

biophysics↗