Biology subjects

Li, Z.

Publications and source records attributed to Li, Z..

At least 307 records · Page 17Linked to original sources

bioRxiv2025

Overcoming EGFR resistance by monovalent and bident inhibitors targeting Cys775

Covalent targeting of EGFR cysteine 797 by osimertinib is one of the most successful breakthroughs in targeted therapy, fundamentally transforming the treatment landscape for non-small cell lung cancer (NSCLC) patients. However, resistance driven by mutation of C797 remains a major clinical challenge. Developing novel covalent strategies beyond C797 targeting presents a compelling opportunity for next-generation EGFR inhibitors. We first demonstrated that cysteine 775, located deep within the ATP-binding pocket, is accessible by a rationally designed covalent molecule ZNL-3, which as the first-in-class covalent cysteine 775 inhibitor exhibited strong efficacy in osimertinib-resistant mouse models. To further enhance resilience to resistance-causing mutations, we developed a dual-warhead, bident compound--YNW-1--which covalently targets both cysteine 775 and 797 simultaneously. YNW-1 is the first intramolecular lock to exhibit balanced reactive efficiency on both cysteines, rendering single-site mutations ineffective to confer resistance. The discovery of ZNL-3 and YNW-1 represents significant advancements in EGFR-targeted drug development, and further optimization toward clinical translation is a worthwhile strategy. SIGNIFICANCE: This study establishes the therapeutic potential of an EGFR covalent inhibitor through unprecedented targeting of cysteine 775 and provides the first demonstration that dual cysteine engagement offers superior efficacy over conventional covalent inhibitors by delaying resistance.

cancer biology↗

bioRxiv2025

Viral transcriptional regulators extensively rewire host pathways through diverse mechanisms

Viral transcriptional regulators (vTRs) reprogram host gene regulatory networks to promote replication, persistence, and immune evasion. Despite the identification of hundreds of vTRs in human viruses, how they rewire host pathways remains unclear. Here, we systematically profiled 95 vTRs from diverse human viruses across multiple functional assays. vTRs perturb immune, cell proliferation/death, and signaling pathways through various mechanisms; some bind DNA directly, others cooperate or antagonize human transcription factors (hTFs), and some remodel chromatin. vTRs can act as activators or repressors and recruit similar but not identical repertoires of proteins as hTFs. These findings reveal vTRs as versatile transcriptional modulators that converge on conserved host "pressure points" while diversifying across pathways to promote viral replication and persistence. Notably, many vTR dysregulate genes within autoimmune, neurological, and cardiovascular risk loci, revealing mechanistic links to disease. Together, we provide a comprehensive resource for understanding and targeting viral control of human transcription.

genomics↗

bioRxiv2025

DTCFinder: a bench-to-bits toolkit for label-free, whole-spectrum analysis of disseminated and circulating tumor cells in liquid biopsies

Disseminated (DTCs) and circulating tumor cells (CTCs) are rare but pivotal in understanding cancer metastasis and advancing liquid biopsy-based diagnostics. Traditional detection approaches, hindered by their dependency on epithelial markers or morphological features, impair the accurate identification of varied DTC/CTC phenotypes within liquid biopsies. We describe DTCFinder, a bench-to-bits, label-free method integrating high-throughput single-cell RNA sequencing and machine learning, enabling unbiased detection, in-depth molecular characterization, and precise tissue-of-origin tracing of DTCs/CTCs across various types of liquid biopsies, even with ultra-scarce DTC/CTC presence in peripheral blood and low sequencing depth. DTCFinder offers a robust and versatile toolkit for liquid biopsy-based cancer diagnostics, enhancing our understanding of cancer metastasis.

cancer biology↗

bioRxiv2025

The Genomic Architecture of Human DNA Replication Origins

The exact sites of DNA replication origins in human and other metazoans remain elusive. Examining human whole-genome sequencing data of 2,616 specimens, we observed conjoining reads at 2,025,756 non-random genomic positions, likely arising from nascent DNA and thus defining replication origins. These origins exhibited a 16 bp motif and periodic occurrence at the intervals of 10.5 bp and 200 bp. Genome-wide replication activity is related to the expression of DNA replication-related genes. Across the genome, DNA replication initiation is more active in early replicating regions, correlated with transcription activity in cis, enriched for de novo mutations and trait-associated polymorphisms. Our high-resolution mapping of human DNA replication origins points to molecular features that govern where and when replication begins in the genome.

cell biology↗

bioRxiv2025

IgG-Bridging-Seeded Synergistic Aggregation of SARS-CoV-2 Spikes Underlies Potent Neutralization by A Low-Affinity Antibody

Mechanistic studies of viral neutralization typically prioritize high-affinity antibodies, relegating low-affinity binders to the sidelines. We report P5-1C8, a Class 1 SARS-CoV-2 antibody that exemplifies this underexplored "low-affinity yet high-potency" phenotype, retaining strong neutralization of Omicron JN.1 despite markedly weakened trimer binding (KD = 225 nM; IC50 = 0.06 nM). Structural and biophysical analyses reveal that P5-1C8 engages WT and BA.1 spikes through canonical intra-spike bivalency, but with JN.1 it induces aggregation. Using virion-like nanoparticles displaying multiple spikes, we show that IgG remains bound with no detectable dissociation and triggers pronounced aggregation. Coarse-grained molecular dynamics delineate the stepwise pathway in which weak IgG-spike contacts seed aggregation via transient inter-spike bridging. Together, these findings establish the first mechanistic framework demonstrating how weak-binding antibodies can nonetheless achieve potent neutralization through higher-order aggregation, thereby expanding the conceptual landscape of antibody function and opening new directions for antibody evaluation and design. Graphical AbstractLow-affinity antibodies are frequently disregarded in discovery pipelines. This work reports P5-1C8, a Class 1 SARS-CoV-2 antibody with weak trimer binding (KD-to-IC50 > 3,700-fold) yet potent neutralization of Omicron JN.1. Structural, biophysical, functional and coarse-grained simulations collectively demonstrate that transient inter-spike IgG bridging seeds higher-order aggregation, which in turn drives neutralization and provides a mechanistic framework. O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/691454v1_ufig1.gif" ALT="Figure 1000"> View larger version (79K): org.highwire.dtl.DTLVardef@1d4f132org.highwire.dtl.DTLVardef@1274400org.highwire.dtl.DTLVardef@e186a6org.highwire.dtl.DTLVardef@4f1870_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

Ferrapoptosis: Discovery of a Hybrid Programmed Cell Death

Traditional taxonomies classify ferroptosis and apoptosis as distinct forms of regulated cell death. Here, we challenge this view by defining and validating a previously unrecognized neuronal death entity in traumatic brain injury (TBI), which we term "Ferrapoptosis". This hybrid mode of death is characterized by the coexistence of ferroptotic and apoptotic molecular and ultrastructural features within the same neuron-- ultrastructural alterations that cannot be fully characterized by any known cell death modalities. By integrating single-cell transcriptomics with multi-layered functional assays, we show that Ferrapoptosis dominates in severe injury and the acute phase, and is critically driven by mitochondrial oxidative stress. Over time, its predominance is gradually replaced by death modes in which either ferroptosis or apoptosis alone becomes the major pathway. Genome-wide CRISPR screening further identifies Smg7 as a key regulator that synchronously activates both death programs to drive the hybrid phenotype, whereas genetic or viral inhibition of Smg7 reduces Ferrapoptosis and promotes neurological recovery after TBI in mice. Our work systematically delineates a previously unrecognized form of cell death and its pathogenetic mechanisms, providing experimental evidence to refine cell death classification, and suggesting a conceptual strategy for treating complex diseases such as TBI by targeting shared regulatory nodes. HIGHLIGHTSO_LIDefined and validated a previously unrecognized cell death modality, termed "Ferrapoptosis". C_LIO_LIElucidated the ordered pattern of Ferrapoptosis across key stages of TBI pathogenesis. C_LIO_LIRevealed mitochondrial oxidative stress as the pivotal hub integrating ferroptosis and apoptosis. C_LIO_LIIdentify Smg7 as a core driver of Ferrapoptosis and a promising neuroprotective target. C_LI

cell biology↗

bioRxiv2025

A Stretchable Wearable Doppler Ultrasound Patch for Continuous Vascular Monitoring

Continuous blood flow monitoring is crucial for the diagnosing and preventing cardiovascular diseases, but existing techniques have several limitations that hinder widespread clinical use, such as invasiveness, operator dependency and lack of continuous measurement. In this paper, we present a wearable ultrasound patch designed for continuous blood flow monitoring. The patch integrates six ultrasonic transducers arranged symmetrically along the central axis. A baseplate with a specific angle (30 degrees) is used to form a 60-degree Doppler incident angle. We have simplified and improved the fabrication process of the serpentine electrodes, avoiding the cumbersome traditional steps such as transfer printing and spin-coating of Cu/PI. The fabricated wearable ultrasound patch exhibits a center frequency of 5.05 MHz, a -6 dB bandwidth of 42.58%, and an elastic stretchability of up to 30%. Based on pulsed-wave (PW) Doppler, it calculates the Doppler frequency shift using a slow-time sampling algorithm. In vitro studies showed an absolute percentage error of 4.1% to 12% for constant flow velocity measurements, with actual flow velocities varying between 20 and 100 cm/s. The mean absolute error (MAE) was 3.56 cm/s, and the mean absolute percentage error (MAPE) was 5.99%. In pulsatile flow experiments, the patch was also able to capture the changing peaks of the velocity waveform, demonstrating its feasibility for continuous blood flow monitoring.

bioengineering↗

bioRxiv2025

From deserts to forests: sequencing of the giant forest hog completes the genomic history of the African Suids

The giant forest hog (GFH, Hylochoerus meinertzhageni), the largest and most elusive wild pig, is found in a disjunct range across equatorial Africa, primarily inhabiting forests and woodlands. It is the only remaining African suid species without whole-genome data. The GFH is currently classified into three subspecies, differing in skull morphology and size. Here, we present the first whole-genome data of nine GFHs; one from Guinea and eight Uganda, representing the two geographically most distant subspecies. This genomic evidence allowed us to resolve the previously uncertain placement of the GFH among African suids as a sister group to the warthogs (Phacochoerus spp.). Also, we found a deep evolutionary split between the two GFH populations resulting in a level of genetic differentiation (FST > 0.75), which is similar to or higher than values observed among species of African wild pigs. The GFH likely diverged from warthogs around 4.6 Mya while the two study populations of GFH diverged at least 0.5 Mya and, subsequently, experienced very dissimilar demographic trajectories. The individual from Guinea had half of the heterozygosity level of the Uganda population, and lower value compared to all the African suids, probably as a result of the long term low Ne and isolation. Due to the close affinity of GFH with the African rainforest biotic zone, our findings have implications for understanding the history of habitat change in Africa. They also indicate a very deep and previously underappreciated evolutionary divergence within the GFH that may have implications for its conservation.

genomics↗

bioRxiv2025

Neuronal activity promotes repair by microglial reprogramming following demyelination

Microglia, the resident immune cells of the central nervous system (CNS), play a multifaceted role in neurological disorders. In multiple sclerosis (MS), a chronic demyelinating and neurodegenerative disease, microglia contribute to inflammation and tissue damage, but can also support repair by clearing myelin debris, limiting inflammation and promoting remyelination and neuroprotection. The timely transition from their pro-inflammatory to pro-regenerative states is essential for effective repair and, in chronic MS, persistent, defective microglial activation contributes to disease progression. Yet, the mechanisms underlying the microglial switch remain largely unknown. In this study, we demonstrate that neuronal activity can modulate microglial signature at the onset of remyelination in MS models, in a pattern-dependent manner. Transcriptomic analyses reveal a downregulation of pro-inflammatory, disease-associated microglial signatures alongside an upregulation of genes associated with oxidative phosphorylation and lipid metabolism, indicative of a shift toward pro-regenerative states following physiological activity enhancement. This activity-dependent reprogramming also extends to infiltrating monocytes and macrophages, collectively fostering a microenvironment favoring repair.

neuroscience↗

bioRxiv2025

The cloacal outgrowth orchestrates co-development of the bladder and umbilical arteries

The urinary bladder is an innovation of eutherian mammals to manage liquid wastes. However, bladder development and its embryonic function remain poorly understood. Here, we report that in both humans and mice the bladder emerges from the cloaca as the rostral outgrowth, which is flanked by the umbilical arteries. Arrest of the outgrowth, as seen in Shh-null mouse embryos, causes bladder agenesis and unexpected severe defect of the umbilical arteries, suggesting that the outgrowth epithelial signal Shh coordinates development of both the bladder and umbilical arteries. Wnt2 signaling molecule is restricted to the rostral outgrowth mesenchyme; and its expression is dramatically down-regulated in Shh-null mutants. Furthermore, Wnt2-null mutants develop small bladder and single umbilical artery defects. Findings here uncover an unexpected co-development mechanism of the bladder and umbilical arteries orchestrated by the cloacal outgrowth signals. The co-development paradigm offers a conceptual framework to understand evolution and development of the bladder. Significance StatementThe urinary bladder is found in the eutherian mammals but largely absent from other vertebrates. Innovation of the bladder has been viewed as a necessity to manage liquid metabolic wastes on dry land. In this study, we report that the bladder emerges from the rostral outgrowth of the cloaca. We also report an unexpected developmental link between the bladder and umbilical arteries and, demonstrate that their co-development is orchestrated by the outgrowth epithelial signal Shh and mesenchymal signal Wnt2. Findings here highlight a possibility that, in addition to managing metabolic wastes, normal bladder development is required for growth of mammalian embryos by supporting development of the umbilical arteries.

developmental biology↗

bioRxiv2025

Accelerated and High-Accuracy Variant Calling on Oxford Nanopore Technologies Sequencing Data with the Sentieon DNAscope LongRead and Hybrid Pipelines

Oxford Nanopore Technologies (ONT) has emerged as a clinically meaningful long-read sequencing platform, enabled by recent improvements in read accuracy and throughput. However, variant calling on ONT data remains challenging due to platform-specific error profiles, particularly indels in homopolymer regions. Here, we introduce description and comprehensive benchmarks of the Sentieon DNAscope LongRead and DNAscope Hybrid pipelines on ONT whole-genome datasets, demonstrating high accuracy and substantially reduced computational requirements across SNP, Indel, and Structural Variant detection. Benchmarked against multiple truth sets including GIAB v4.2.1, CMRG, and T2T-Q100, DNAscope LongRead reduced SNP errors by [~]50% relative to Clair3 across five reference samples and showed consistently higher accuracy in all non-long-homopolymer stratifications. Integration of ONT and Illumina short reads via DNAscope Hybrid further improved variant detection: with low depth ONT + Illumina datasets, the pipeline achieved F1 scores of 0.9992 (SNPs) and 0.9979 (Indels), outperforming alternative pipelines. In challenging benchmarks such as T2T-Q100 and CMRG genes, DS-Hybrid reduced SNP and Indel errors significantly, compared to next-best methods. For structural variants, DNAscope pipelines outperformed alternative (e.g. Sniffles2), highlighting the advantages of haplotype-resolved SV detection method. Despite its accuracy, the Sentieon software suite remains computationally efficient: complete ONT long read FASTQ-to-VCF analysis completed within [~]190 minutes on a 120-vCPU Azure instance, at costs less than $5 USD. Overall, the DNAscope LongRead and Hybrid pipelines deliver fast, accurate, and scalable germline variant calling for ONT datasets, providing a practical solution for comprehensive whole-genome analysis in both research and clinical genomics.

bioinformatics↗

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

Sequence-aware Prediction of Point Mutation-induced Effects on Protein-Protein Binding Affinity using Deep Learning

Amino acid mutations may lead to significant changes in the binding affinity of protein complexes, thereby causing a series of cellular dysfunctions. Therefore, accurate prediction of protein-protein binding affinity changes ({Delta}{Delta}G) induced by amino acid mutations is of great importance for understanding protein-protein interactions (PPIs). In this study, we propose SAMAffinity, a protein sequence-aware deep learning architecture for predicting changes in protein-protein binding affinity caused by amino acid mutations. SAMAffinity predicts mutation-induced {Delta}{Delta}G by integrating multi-source sequence features, leveraging a Mutation-Site Identification (MSI) module to highlight local semantic shifts and a Binding-Interface Awareness (BIA) module to capture interaction changes. Benchmark evaluations on public datasets show that under the mutation-level data splitting strategy, SAMAffinity outperforms the state-of-the-art sequence-based method AttABseq by 33.3%, 72.3%, 31.8%, and 30.5% on S1131, S4169, S645, and M1101 datasets, respectively. Moreover, under the complex-level data splitting strategy, SAMAffinity surpasses the structure-based method MpbPPI by 22.9%, 22.7%, 5.0%, and 11.4% on the corresponding datasets. Beyond predictive accuracy, the strong consistency between the models predicted distribution and natural amino-acid mutation tendencies indicates that SAMAffinity effectively captures the underlying mutational landscape shaped by intrinsic biochemical and evolutionary factors. Based on this capability, SAMAffinity demonstrated strong generalization in a study of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases, suggesting its potential for optimizing therapeutic antibody design.

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

Mechanistic insights into Phage SSB-activated bacterial Retron-Eco8 immunity

The Retron-Eco8 system, comprising a reverse transcriptase (RT), a non-coding RNA (ncRNA), and an OLD-family nuclease effector, protects bacteria from phage infection via abortive infection upon sensing a phage single-stranded DNA-binding protein (SSB). However, the molecular basis of this immunity remained unclear. Here, we report cryo-electron microscopy (cryo-EM) structures of Retron-Eco8 in inactive and activated states, revealing mechanisms of phage-triggered activation and effector function. Retron-Eco8 assembles into a tetrameric complex in which each protomer contains an RT, msrRNA-msdDNA duplex, and effector in an autoinhibited conformation. Upon phage infection, phage SSB binds msdDNA, relieving autoinhibition and activating the nuclease effector to degrade both phage and host DNA, triggering cell death to block phage propagation. Host SSB fails to activate the system, while DNA binding and oligomerization of phage SSB are essential for this activation, highlighting its specificity. These findings elucidate the molecular mechanism of Retron-Eco8-mediated immunity, facilitating retron-based biotechnological applications.

microbiology↗

bioRxiv2025

Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes

Decoding cellular systems requires integrating diverse omics data, yet most models are trained from scratch on a single modality, restricting generalization. Here we present CellHermes, a biological language model that leverages pretrained large language models (LLMs) to integrate multimodal forms of omics data, such as transcriptomic profile and PPI network, through natural language for better omics understanding. By reformulating these datasets into question-answer pairs, CellHermes emulates multiple self-supervised learning paradigms within a single, universal, language-based framework with LORA fine-tuning on existing natural language-LLM, achieving comparable or even better performance compared to current single-cell foundation models. Within this framework, CellHermes functions as an encoder, a predictor and an explainer for supporting a range of downstream tasks. For encoder, CellHermes encodes biological entities with embeddings that enable accurate network discovery and cross-dataset generalization; For predictor, CellHermes unifies heterogeneous downstream tasks by translating them into natural language question-answers pairs, allowing a single model to perform multi-task prediction by instruction fine-tuning; For explainer, CellHermes explains molecular mechanisms by combining attention analysis with text-based reasoning, leveraging the interpretability and reasoning capabilities of LLMs. We evaluated the ability of CellHermes as an encoder on representing genes and cells using 5 gene-level downstream tasks and 5 diverse single-cell datasets across different tissues compared with other single cell foundation models. We also evaluated CellHermes as a predictor on BioUniBench, a new benchmark of 10 tasks across 7 databases for LLMs, including perturbation response, cell fitness estimation, gene-disease association, and cell type identification. All benchmarks suggest CellHermes can unify multiple biological tasks within one model without compromising performance. By applying CellHermes as an explainer for a melanoma patients dataset, it can uncover potential key genes on tumor-reactive T cells. Together, CellHermes establishes natural language as a unifying medium for omics, offering a foundation that, in the future, potentially enables a more integrated and interpretable research loop of biological representation, prediction, and interpretability.

bioinformatics↗

bioRxiv2025

An EEG-based Multi-Scale Hybrid Attention and Squeeze Network for objective taste assessment

Taste perception is central to flavor experiences. Electroencephalography (EEG) signals carry rich information about taste perception. Because these neural data are independent of verbal reports and bypassing conscious filtering, EEG holds promise as a neural-sensing platform for objective taste representation. However, taste-evoked EEG signals exhibit complex spatiotemporal and spectral dynamics that require advanced computational approaches to decode effectively. This study developed an EEG-based Multi-Scale Hybrid Attention and Squeeze Network (MHASNet) for taste evaluation. EEG signals evoked by distinct taste stimuli were recorded, a dedicated taste-EEG dataset was compiled, and a novel deep learning architecture, MHASNet, was designed to classify these signals. MHASNet synergistically integrates multi-scale convolutions to capture temporal dynamics across different time scales, dual-attention mechanisms to localize discriminative brain regions and electrode positions, and a squeeze-and-excitation module to optimize frequency-band contributions-collectively enabling precise extraction of taste-specific neural signatures. Results showed that the proposed model achieved superior performance across five taste categories (sweet, sour, salty, bitter, and tasteless), with 94.33% accuracy, 91.37% F1-score, 91.89% precision, and 92.07% recall. These results surpass benchmark models while maintaining millisecond-level inference latency suitable for real-time applications. By complementing subjective evaluations and instrumental analyses, the model offers an objective, neurophysiology-based solution for taste evaluation.

neuroscience↗

bioRxiv2025

Fusobacterium nucleatum outer membrane vesicles activate the TNFR1/NF-κB signaling pathway to promote OSCC proliferation

Fusobacterium nucleatum (F. nucleatum, Fn) has emerged as a significant bacterium linked to cancer. However, the precise mechanisms by which Fn drives the progression of oral squamous cell carcinoma (OSCC) remain obscure. Outer membrane vesicles (OMVs), small vesicular structures secreted by Gram-negative bacteria, are known to contribute to their pathogenicity. This study investigated the role of Fn OMVs in oral cancer progression and the underlying mechanisms. MethodsMetagenomic sequencing was conducted on both oral cancer and control tissues. OMVs were isolated through ultracentrifugation. Cell proliferation assays on two cancer cell lines assessed the impact of Fn and its OMVs. Fn OMVs were administered to mice to evaluate their influence on tumorigenesis. RNA-seq was performed on OMV-treated cells to identify enriched genes, which were validated via qPCR and Western blot. Knockdown experiments were executed in vitro and in vivo to confirm the involvement of Fn OMVs in TNFR1-mediated NF-{kappa}B signaling. ResultsFn prevalence was markedly higher in oral cancer tissues compared to non-cancerous samples. In vitro, Fn and its OMVs significantly enhanced the growth and proliferation of oral cancer cell lines. Mice treated with Fn OMVs developed larger tumors than those receiving Fn alone. Furthermore, Fn OMVs notably induced TNFR1 expression and activated the NF-{kappa}B signaling pathway, as confirmed by RNA-seq, qPCR, and Western blot. TNFR1 knockdown significantly diminished the effects of Fn OMVs on cancer cell proliferation in vitro and tumor formation in vivo. ConclusionThis study compellingly demonstrates that Fn OMVs are pivotal in OSCC progression by directly activating TNFR1-mediated NF-{kappa}B signaling. These findings pave the way for future research into the prevention and treatment of oral cancer.

microbiology↗