Biology subjects

Dai, C.

Publications and source records attributed to Dai, C..

At least 19 recordsLinked to original sources

bioRxiv2026

Efficient recovery of transgene-free edited tobacco using the transgene self-elimination system via de novo regeneration

Elimination of integrated editing constructs remains a major challenge for generating transgene-free progeny in plant genome editing based on Agrobacterium-mediated regeneration. Here, we adapted the Transgene Killer CRISPR (TKC) concept to tobacco and established the transgene self-elimination system, in which CRISPR/Cas9 is coupled with developmentally regulated BARNASE cassettes. Three pU3TR-derived vectors carrying DLL-, OsRP3- or PHAS-driven Barnase regenerated normally after leaf-disc transformation. The DLL construct pEB138 showed the highest T editing frequency, with 56 of 69 independent T plants (81.16%) carrying mutations at one or both NtDFR target loci. Under a stringent lineage-level screen in which up to 48 T progeny were tested per T line, 36 of 43 pEB138 lineages (83.72%) yielded uniformly marker-negative progeny for the three assayed T-DNA markers, compared with 0 of 7 lineages transformed with the parental pU3TR vector. The platform also supported recovery of dfr mutants with altered flower pigmentation and NtA622-edited lines with reduced nicotine accumulation. Moreover, introduction of DLL-iBarnase into the independent pDC45 editing backbone did not impair shoot regeneration, and representative NtBBL progeny showed markedly reduced nicotine content together with loss of the selectable cassette. These results demonstrate that BARNASE-assisted transgene elimination can be integrated with Agrobacterium-mediated de novo regeneration in a dicot, enabling one-step recovery of transgene-free edited tobacco progeny.

molecular biology↗

bioRxiv2026

A step beyond: age and sex-dependent changes in multidimensional locomotor and gait patterns of voluntary motion of C57BL/6 mice

Age-related decreases in physical activity drive frailty and reduced intrinsic capacity, promoting adverse health outcomes such as falls. Experimental tools for quantifying these metrics in preclinical models have lagged behind those used clinically; single-endpoint activity assessments (e.g., rotarod performance, treadmill activity) of rodents often require invasive handling and/or forced performance cues that may increase stress and variability, especially in aged animals. Here, we evaluated whether automated analysis of spontaneous movement using machine-learning-based software (Palmreader) could identify age-related changes in locomotor activity, gait, paw loading, or posture in male and female C57BL/6 mice across young, adult, and aged timepoints (4.5-, 7-, and 22 months of age). Parameters were quantified using the BlackBox One system during a 7-minute open-field recording. Principal component analysis demonstrated age-related separation of movement phenotypes, with individual parameters demonstrating increased hindlimb duty factor and longer stride lengths with age. Age-related changes in forelimb-to-hindlimb duty-factor imbalance, paw contact, and postural organization were also observed, and several gait alterations with age showed different patterns between males and females. Conventional measures of physical activity (e.g., distance traveled, walking time), in contrast, showed few age-related differences, particularly in females. These findings suggest that aging more consistently alters movement patterns rather than movement magnitude, and that gait and postural features may represent important age-related functional changes not captured by activity measurements alone. Automated assessment of spontaneous movement may therefore represent a useful complement to traditional physical activity testing, with the added benefit of minimal animal handling and short recording times.

animal behavior and cognition↗

bioRxiv2026

Conditional Generation And Inpainting Of Non-coding RNA Sequences With Masked Discrete Diffusion

Designing functional non-coding RNA (ncRNA) is fundamental to synthetic biology and RNA therapeutics, yet generative modelling for ncRNA has received far less attention than protein design. We present RNA-MDLM, a framework that extends Masked Discrete Language Models (MDLM) to the conditional generation and inpainting of ncRNA. We make two additions: first, conditioning on RNA-type representations from a pretrained RNA language model, and second, a modified classifier-free guidance scheme (Mod-CFG) that interpolates among conditional, unconditional, and random-sequence probabilities for better control. We also introduce REPAINT GAMES, a benchmark of seven structured masking tasks to probe a model's performance on sequence patterns, structural motifs, and base-pairing per RNA type. Our model is trained on 4.6 million ncRNA sequences spanning six evaluable classes. It produces sequences whose composition and folding statistics closely match natural RNAs. Through extensive ablation studies, we find that the embedding-conditioned model achieves the best balance of structural fidelity, biological novelty, and inpainting accuracy, and its class label steers generation far more strongly than a plain label baseline. We further show that a model trained on a smaller, class-balanced subset can appear more realistic mainly by copying abundant natural sequences rather than learning their rules. We also benchmark against a masked-diffusion model and a family-specific VAE on ribozyme families, and find that a type-conditioned model like ours and a per-family model are solving different tasks, which must be accounted for in a fair comparison. We will release the code and the trained models.

biophysics↗

bioRxiv2026

UTR-Diffusion: Conditional Diffusion Modeling for Multi-objective and Constrained UTR Design

Motivation: The 5-prime untranslated region (UTR) and the start-codon-proximal region of the coding sequence (CDS) jointly influence translation efficiency and local RNA secondary-structure stability, while synonymous codon choices throughout the CDS shape codon adaptation. Because the encoded protein is often predetermined, practical mRNA design must coordinate these quantitative objectives while preserving specified nucleotide sequences and amino-acid identities. Existing generative approaches typically address continuous-valued targeting, explicit sequence constraints, and codon-usage control separately rather than integrating all three within a single model. Results: We present UTR-Diffusion, a diffusion-based framework for 5-prime UTR and 5-prime UTR-CDS junction design. UTR-Diffusion conditions generation on continuous-valued MRL and MFE targets and supports nucleotide-level constraints, amino-acid-level constraints with synonymous-codon flexibility, and codon-adaptiveness control that modulates the sequence-level codon adaptation index (CAI). Systematic evaluations across dense MRL-MFE target grids showed that generated distributions shifted consistently with both targets, retained substantial diversity, and strictly preserved specified nucleotide sequences and amino-acid identities. Codon-adaptiveness control yielded distinct, monotonically ordered CAI levels that closely followed the specified adaptiveness targets. In comparative benchmarks, UTR-Diffusion outperformed representative existing methods in high-MRL optimization and precise MRL targeting for 5-prime UTR design, and achieved higher MRL, less-negative junction MFE, and higher CAI than peptide-preserving baselines in 50-nt 5-prime UTR-CDS junction design.

bioinformatics↗

bioRxiv2026

onsite: An Integrated Framework for Phosphosite Localization and False Localization Rate Estimation

With the rapid development of mass spectrometry-based proteomics, the volume of phosphoproteomic data has increased substantially. However, accurate localization of phosphorylation sites and standardized statistical validation remain critical analytical bottlenecks. To address the lack of standardized cross-algorithm evaluation, we introduce onsite, a unified and open-source Python framework. onsite integrates an alanine-decoy strategy to estimate the false localization rate (FLR) across three algorithms: AScore, PhosphoRS, and pyLucXor. This modular architecture efficiently processes large-scale datasets and enables global FLR calculation. Benchmarking on the standard synthetic phosphopeptide dataset PXD000138 highlighted distinct inter-algorithmic variations. Using the same 5% global FLR threshold, pyLucXor localized the most target sites (28,353). It also reached a high accuracy (91.22%) against the known ground truth, resulting in the largest number of correctly localized sites (25,865). Reanalysis of the highly fractionated, large-scale PXD012255 dataset further demonstrated that native integration of onsite into the quantms pipeline enables scalable processing and provides a standardized framework for FLR control in large-scale phosphoproteomics. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/737157v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@e4c85dorg.highwire.dtl.DTLVardef@1e8464org.highwire.dtl.DTLVardef@185cea1org.highwire.dtl.DTLVardef@1c0d1bc_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

False discovery rate control for trustworthy AI-based de novo peptide sequencing

AI-based de novo peptide sequencing predicts peptide sequences from tandem mass spectra, enabling identification beyond predefined databases but leaving prediction reliability difficult to assess, particularly with respect to false discovery rate (FDR). In database search, FDR control is provided by target-decoy competition over a finite search space, whereas de novo predictions are generated in open sequence space and lack naturally matched sequence-level decoys. Here we introduce Counterpart Calibration Theory (CCT), a theory-guided framework that reframes de novo FDR control as a four-group score-ranking and threshold-selection problem over target-side predictions and matched counterpart-side comparators. Implemented in {pi}-NovoQC, CCT provides dual-level FDR control at the peptide-spectrum match and peptide levels. Across models, datasets, instruments and acquisition modes, {pi}-NovoQC achieves stable FDR control while preserving identification yield. In large-scale proteomic applications, {pi}-NovoQC recovers low-abundance in-database peptides missed by database search and provides de novo-supported protein-group and variant evidence.

bioinformatics↗

bioRxiv2026

Germline mitophagy selectively eliminates deleterious mitochondrial DNA across generations

Faithful transmission of genetic information through the immortal germline is essential for organismal health and species survival, yet how mutant mitochondrial genomes (mtDNAs) are selectively eliminated across generations remains unclear. Here, we show that germline mitophagy functions as a mutation-responsive surveillance system that selectively eliminates mutant mtDNAs and shapes inheritance across generations. In C. elegans, mitochondria enriched for mutant mtDNAs are selectively removed in the maternal germline prior to oocyte fertilization via PINK1/Parkin-dependent and BNIP3-mediated mitophagy pathways activated by mtDNA defects. Germline mitophagy declines with age, resulting in offspring that inherit increased burdens of mutant mtDNAs. Conversely, enhancing mitophagy within germ cells restricts the transmission of deleterious genomes in a compounding manner, ultimately driving their complete elimination from the matrilineal lineage. Together, our findings demonstrate that germline mitophagy is a critical determinant of intergenerational mitochondrial genome inheritance, establishing its role in restricting the transmission of defective genetic information.

Cell Biology↗

bioRxiv2026

Fibulin-2 transduces a matrix-to-metabolism signal in kidney fibrosis

Fibrotic extracellular matrix (ECM) is not merely a structural scaffold but an instructive signaling interface that shapes epithelial cell state. However, the molecular cues by which matrix remodeling controls tubular metabolism during kidney fibrosis remain poorly defined. Here, we identify Fibulin-2 (FBLN2) as a fibroblast-derived matrix cue that transduces fibrotic ECM remodeling into tubular mitochondrial metabolic reprogramming. Using fibroblast-selective deletion of Smoothened (Smo) across distinct fibroblast subpopulations, we found that loss of fibroblast Smo preserved kidney function and attenuated fibrosis in mouse models of chronic kidney injury. Multi-omics profiling revealed coordinated remodeling of the fibrotic matrisome, highlighted by suppression of FBLN2, an ECM glycoprotein genetically linked to kidney function in humans. Mechanistically, FBLN2 engaged EGFR in tubular epithelial cells and activated EGFR-AKT signaling in a non-canonical ligand-like manner. This signaling axis suppressed acetyl-CoA acetyltransferase 1 (ACAT1), a mitochondrial regulator of fatty acid oxidation and amino acid metabolism. Disruption of fibroblast Smo-FBLN2 signaling restored ACAT1-dependent oxidative metabolism and reduced tubular fibrotic activation. Spatial lipidomics revealed compartment-specific lipid remodeling associated with altered mitochondrial fatty acid metabolism, including acylcarnitine and phospholipid changes linked to reduced fibrotic injury. Together, these findings define a Fibulin-2-EGFR-ACAT1 matrix-to-metabolism signaling axis that couples fibrotic ECM remodeling to tubular mitochondrial metabolism during kidney fibrosis.

cell biology↗

bioRxiv2026

π-MSNet: A billion-scale, AI-ready living proteomics data portal

Artificial intelligence (AI) is reshaping proteomics workflows, delivering remarkable gains in both peptide identification sensitivity and quantitative performance. However, the potential of deep learning models in proteomics has not been fully exploited due to the scarcity of large-scale, high-quality and consistently labeled datasets. Here, we present {pi}-MSNet, a billion-scale, AI-ready living mass spectrometry (MS) data portal. Using a uniform identification and quality control workflow, it comprises over 1.66 billion MS/MS spectra, 501 million peptide-spectrum matches (PSMs), and 9 million precursors from 36,356 LC-MS/MS runs across ten instrument types and 55 diverse species. Through community collaboration, the data are shared via international, interactive, and living web resources. Enabled by the built-in MSNetLoader Python API for seamless and scalable data access--with native support for PyTorch and TensorFlow--{pi}-MSNet provides an AI-ready data framework for efficient training and systematic benchmarking of multiple models across three representative tasks (e.g., MS/MS spectrum prediction, retention time prediction, and de novo peptide sequencing). In particular, by retraining multiple models on {pi}-MSNet, we achieved consistent performance improvements over their original versions. These improved models were subsequently integrated into the {pi}-MSNet agent to enable interactive, deployment-free use. Through SDRF (Sample and Data Relationship Format) metadata, an open-source cloud analysis workflow, and a community-driven interactive data portal that supports continuous data submission, {pi}-MSNet serves as a living, AI-ready resource for reproducible benchmarking, robust model training, and accelerated AI innovation in proteomics.

bioinformatics↗

bioRxiv2026

c-MYC is an aggregation-prone, amyloidogenic protein

The transcription factor c-MYC is a central regulator of diverse biological processes and an oncogenic driver. Paradoxically, c-MYC also exhibits intrinsic tumor-suppressive activity through apoptosis, a phenomenon known as the "c-MYC paradox". Here, we identify c-MYC as an intrinsically amyloidogenic protein. Under proteotoxic stress, c-MYC becomes detergent-insoluble aggregates. Notably, even in the absence of stress, c-MYC assembles into soluble amyloid-like oligomers in human cancer tissues and Alzheimers disease brains. In vitro, recombinant c-MYC proteins spontaneously form amyloid oligomers and protofibrils. In contrast, its obligate dimerization partner MAX is non-amyloidogenic and suppresses c-MYC amyloidogenesis. Mapping studies identify two short linear sequences within intrinsically disordered regions that confer amyloidogenicity. Importantly, the amyloid state of c-MYC induces apoptosis largely independently of transcription. Thus, these findings reveal a previously unrecognized amyloidogenic property of c-MYC, which contributes to its tumor-suppressing activity. Conceptually, c-MYC amyloidogenesis may serve as a built-in failsafe mechanism to restrain its oncogenic potential.

cell biology↗

bioRxiv2026

Inhalable Polymeric Nanoparticle Vaccine for Lysosome-targeting Co-delivery of Antigen and Adjuvant with Enhanced Immunoprotection

Conventional subunit vaccines suffer from premature clearance and poor synchronization of antigen and adjuvant, limiting coordinated immune activation. Here, we develop an amphipathic polymer (YAXA) with acid-labile imine bonds for acid-sensitive degradation and terminal NHS-activated esters for antigen conjugation. YAXA is co-assembled with hydrophobic TLR7 agonist 3M-052 and conjugated to protein antigen, forming nanoparticle vaccine YM3.7. Following aerosolized intratracheal inoculation into the lung, YM3.7 is efficiently internalized by antigen-presenting cell and trafficked into lysosome, where acidic conditions trigger its dissociation and co-release of antigen and adjuvant. This spatiotemporally synchronized delivery promotes robust immune activation, including cell maturation, germinal center formation, systemic and lung-resident B/T cell response, and IgG/sIgA production. In lethal pneumonia models induced by either Pseudomonas aeruginosa or Staphylococcus aureus, YM3.7 markedly improves survival over conventional antigen and adjuvant mixture. This study presents an inhalable nanoparticle platform that coordinately activates innate, humoral, mucosal, and cellular immunity for enhanced protection.

immunology↗

bioRxiv2026

Unrestrained fatty acid oxidation triggers heart failure in mice via cardiolipin loss and mitochondrial dysfunction

Cardiomyocytes primarily rely on fatty acid oxidation (FAO), which provides more than 70% of their energy. However, excessive FAO can disrupt cardiac metabolism by increasing oxygen demand and suppressing glucose utilization through the Randle cycle. Although inhibition of FAO has been investigated in heart failure, its overall therapeutic impact remains uncertain. To determine the consequences of enhanced FAO, we generated cardiomyocyte-specific ACC1 and ACC2 double-knockout (ACC dHKO) mice, which exhibit constitutively elevated FAO. ACC dHKO mice developed dilated cardiomyopathy and heart failure. Lipidomic analysis revealed marked depletion of cardiolipin caused by reduced linoleic acid, a direct consequence of excessive FAO. This cardiolipin deficiency impaired mitochondrial electron transport chain (ETC) activity, leading to mitochondrial dysfunction. Pharmacologic inhibition of FAO with etomoxir or oxfenicine restored cardiolipin levels, normalized ETC activity, and prevented cardiac dysfunction in ACC dHKO mice. These findings demonstrate that unrestrained FAO disrupts both lipid and energy homeostasis, culminating in heart failure in this model. Collectively, these results indicate that although FAO is essential for cardiac energy production, therapeutic strategies aimed at stimulating cardiac FAO may be detrimental rather than beneficial in heart failure.

cell biology↗

bioRxiv2026

quantms-rescoring enables deep proteome coverage across protein quantification, immunopeptidomics, and post-translational modifications experiments.

The growing volume of public proteomics datasets and the advent of novel machine learning (ML)-based methods create unprecedented opportunities for discovery through large-scale reanalysis. However, traditional desktop tools are increasingly insufficient for processing and integrating data at this scale. To address this challenge, we present a novel package, quantms-rescoring, that extends the cloud-native quantms workflow with a machine learning-based rescoring module. Unlike prior tools that rescore single-engine outputs, quantms-rescoring seamlessly integrates multiple search engines (SAGE, COMET, and MSGF+), performs automatic model selection, model fine-tuning, and scales reproducibly on cloud infrastructures. In quantms-rescoring, we rely on multiple fragment-ion intensity (AlphaPeptDeep and MS2PIP) and retention-time prediction (DeepLC) methods to improve results from multiple peptide database search engines. It features automatic model selection, fine-tuning, and retraining for MS/MS intensity and retention time prediction to select the best model for a given dataset. We applied the novel workflow to five representative datasets spanning DDA label-free quantification, TMT 10-plex isobaric labelling of tumor proteomics data, immunopeptidomics, phospho-proteomics, and unseen lysine malonylation experiments. We achieved a 16-22.8% increase in identified spectra, along with the quantification of 2191 additional phosphorylated peptides and 1337 phosphosites. In the tandem mass tag (TMT)-labeled clear cell renal cell carcinoma dataset, 76 novel differentially expressed multiple search engines identified proteins with quantms-rescoring. Additionally, novel 11,688 HLA-II potential binders were detected in the immunopeptidomics dataset by multiple search engines with quantms-rescoring. For unseen malonylation data, we reported more than 58.8% malonylation PSMs and 30.5% modification sites than COMET alone. Together, these results show that integrating multi-engine searches with machine learning-derived features can be combined in a scalable workflow that enhances identification, PTM localization, and quantification performance.

bioinformatics↗

bioRxiv2025

pmultiqc: An open-source, lightweight, and metadata-oriented QC reporting library for MS proteomics

The increasing scale and complexity of proteomics data demand robust, scalable, and interpretable quality control (QC) frameworks to ensure data reliability and reproducibility. Here, we present pmultiqc, an open-source Python package that standardizes and generates web-based QC reports across multiple proteomics data analysis platforms. Built on top of the widely adopted MultiQC framework, pmultiqc offers specialized modules tailored to mass spectrometry workflows, with full initial support for quantms, DIA-NN, MaxQuant/MaxDIA, and mzIdentML/mzML-based pipelines. The package computes a wide range of QC metrics, including raw intensity distributions, identification rates, retention time consistency, and missing value patterns, and presents them in interactive, publication-ready reports. By leveraging sample metadata in the SDRF format, pmultiqc enables metadata-aware QC and introduces, for the first time in proteomics, QC reports and metrics guided by standardized sample metadata. Its modular architecture allows easy extension to new workflows and formats. Alongside comprehensive documentation and examples for running pmultiqc locally or integrated into existing workflows, we offer a cloud-based service that enables users to generate QC reports from their own data or public PRIDE datasets.

bioinformatics↗

bioRxiv2025

Intratumoral microbiota-derived S1P sensitizes the combination therapy of Capecitabine and Nivolumab

Clinical responses of colorectal cancer (CRC) treatments vary considerably due to the heterogeneity of tumor micro-environment (TME), where intratumoral microbiota may reshape the unique inflammation imprints. However, its complex mechanistic underpinnings remain incompletely elucidated. Herein we sought to delineate the critical role of intratumoral microbiota in potentiating combination therapeutics against CRC. By comparing germ-free (GF) and specific pathogen-free (SPF) mouse models of 33 potential CRC treatments, we screened out the combination regimen of Capecitabine-Nivolumab significantly augmented by intratumoral microbiota in tumor regression and survival prolongation. The enrichment of Bacteroides fragilis induced by Capecitabine-Nivolumab was concomitant with elevated microbial sphingosine-1-phosphate (S1P), which further upregulated tumoral PD-L1 expression by enhancing histone deacetylation at the CD274 locus. Activation of microbial sphingosine kinase 2 (SphK2) ultimately led to an expansion of effector memory CD8+ T cells and reduction of exhausted T cell subsets within TME. To conclude, these findings advance our understanding of the intricate interplay among intratumoral microbiota, sphingolipid metabolites and immunochemotherapeutics. It highlights microbial sphingolipids as potential predictive biomarkers for strategies of targeting intratumoral microbiota in CRC management.

microbiology↗

bioRxiv2025

Genetic and functional analysis of unproductive splicing using LeafCutter2

Alternative splicing commonly generates unproductive mRNA transcripts that harbor premature termination codons1, leading to their degradation by nonsense-mediated decay (NMD). These events reduce overall protein expression levels of affected genes, potentially contributing to gene regulation and disease mechanisms. Here, we present LeafCutter2, which enables identification and quantification of unproductive splicing from short-read RNA-seq data. LeafCutter2 requires minimal gene annotations (start and stop codons) to annotate NMD-inducing splicing events, and identifies differential unproductive splicing between groups, providing insights into its contributions to differential gene expression. Moreover, LeafCutter2 enables mapping of unproductive splicing quantitative trait loci (u-sQTLs), which often colocalize with expression QTLs and GWAS loci. Applying LeafCutter2 to RNA-seq data across human and 6 non-human species, we uncovered a broad landscape of unproductive splicing, which varies widely across tissues. Strikingly, we observed a conserved developmental-stage-specific increase in unproductive splicing during testis maturation across all species. In Alzheimers disease (AD), we analyzed RNA-seq data from the AD Functional Genomics consortium FunGen-xQTL project and identified unproductive splicing events in 18 AD risk genes, including TSPAN14, PICALM, and CASS4, likely mediating genetic effects on disease risk. We performed an integrative analysis using gene expression QTLs, protein expression QTLs, and AD GWAS data, showing that unproductive splicing provides unique regulatory insights beyond traditional approaches. Thus, LeafCutter2 represents a powerful tool for understanding the functional impact of alternative splicing on gene expression and disease mechanisms.

genomics↗

bioRxiv2025

ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata

Intensity-based absolute quantification (iBAQ) is essential in proteomics as it allows for the assessment of a proteins absolute abundance in various samples or conditions. However, the computation of these values for increasingly large-scale and high-throughput experiments, such as those using DIA, TMT, or LFQ workflows, poses significant challenges in scalability and reproducibility. Here, we present ibaqpy (https://github.com/bigbio/ibaqpy), a Python package designed to compute iBAQ values efficiently for experiments of any scale. ibaqpy leverages the Sample and Data Relationship Format (SDRF) metadata standard to incorporate experimental metadata into the quantification workflow. This allows for automatic normalization and batch correction while accounting for key aspects of the experimental design, such as technical and biological replicates, fractionation strategies, and sample conditions. Designed for large-scale proteomics datasets, ibaqpy can also recompute iBAQ values for existing experiments when an SDRF is available. We showcased ibaqpys capabilities by reanalyzing 17 public proteomics datasets from ProteomeXchange, covering HeLa cell lines with 4,921 samples and 5,766 MS runs, quantifying a total of 11,014 proteins. In our reanalysis, ibaqpy is a key component in automating reproducible quantification, reducing manual effort and making quantitative proteomics more accessible while supporting FAIR principles for data reuse.

bioinformatics↗

bioRxiv2025

c-Rel drives pancreatic cancer metastasis through Fibronectin-Integrin signaling-induced isolation stress resistance and EMT activation

Pancreatic ductal adenocarcinoma remains one of the deadliest malignancies, with limited treatment options and a high recurrence rate. Recurrence happens often with metastasis, for which cancer cells must adapt to isolation stress to successfully colonize distant organs. While the fibronectin-integrin axis has been implicated in this adaptation, its regulatory mechanisms require further elaboration. Here, we identify c-Rel as an oncogenic driver in PDAC, promoting epithelial-to-mesenchymal transition (EMT) plasticity, extracellular matrix (ECM) remodeling, and resistance to isolation stress. Mechanistically, c-Rel directly regulates fibronectin (Fn1) and CD61 (itgb3) transcription, enhancing cellular plasticity and survival under anchorage-independent conditions. Fibronectin is not essential for EMT, but its absence significantly impairs metastatic colonization, highlighting a tumor-autonomous role for FN1 in isolation stress adaptation. These findings establish c-Rel as a key regulator of PDAC metastasis by controlling circulating tumor cell (CTC) niche and survival, suggesting that targeting the c-Rel-fibronectin-integrin axis could provide new therapeutic strategies to mitigate disease progression and recurrence.

cancer biology↗