Biology subjects

Yu, Q.

Publications and source records attributed to Yu, Q..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Chromosomal Topological Domain Formation Modulates Transcription and the Coupling of Neighboring Genes in Escherichia coli

Chromosomal topology and transcription are tightly coupled, yet the quantitative impact of topological constraints on transcription, supercoiling, and the potential coupling between neighboring genes in vivo remains unclear. In this work, we constructed synthetic chromosomal domains in Escherichia coli that contained two genes inside a topology-controllable domain and a third gene outside. Using three-color single-molecule fluorescence in situ hybridization (smFISH), we measured transcription output from the three genes in individual cells under conditions in which gene orientation, domain formation state, and global chromosomal supercoiling density were varied. We found that topological domain formation repressed transcription, diminished gene orientation-dependent differences in transcription, and modulated the supercoiling sensitivity of genes located both within and near the domain. Relaxing global negative supercoiling through gyrase inhibition broadly repressed transcription; increasing global negative supercoiling level through topoisomerase I inhibition repressed highly expressed genes, while activating lowly expressed ones. Besides single-gene effects, we also observed an intrinsic coupling between neighboring genes with a non-monotonic dependence on the underlying supercoiling state, which shifted with domain topology and gene syntax. Our results establish chromosome topology as a major regulator of both transcription levels and the coupling between adjacent genes.

biophysics↗

bioRxiv2025

Common-specific edge-centric connectome across Four Episodes in Bipolar Disorder

BackgroundBipolar disorder (BD) is a heterogeneous psychiatric illness marked by dynamic mood states, including manic (BipM), depressive (BipD), mixed (mBD), and euthymic/remitted (rBD) episodes. These clinical fluctuations are accompanied by widespread functional disruptions in the brain. However, the shared and individual-specific neural mechanisms across distinct episodes of BD remain poorly understood. MethodsWe analyzed resting-state fMRI data from 190 participants (BD patients in four episodes and healthy controls) using edge-centric functional connectomes (eFC), which capture time-resolved co-fluctuations between brain regions. A common-orthogonal-basis extraction (COBE) algorithm was applied to decompose individual eFC matrices into shared and individual-specific subspaces. We characterized the spatial topology, genetic relevance, and circuit-level correlates of the shared component. Dynamic properties (entropy, identifiability) and symptom prediction models were assessed using entropy metrics, intraclass correlation coefficients, and support vector regression. ResultsThe shared eFC pattern was stable across participants, aligned with the sensory-association gradient (p < 0.0001), and exhibited significant heritability and test-retest reliability (p < 0.05). Entropy of individual loadings increased with illness duration and was significantly elevated in BD, particularly in mBD. Microcircuit modeling revealed that this shared pattern was inversely related to external input strength (r = - 0.34, pspin< 0.05), indicating intrinsic network dominance. mBD was associated with globally elevated eFC entropy and markedly reduced fingerprint stability. Symptom severity (HDRS, YMRS, HAMA) was significantly predicted from individual network topographies across BD phases, highlighting clinically meaningful dynamic signatures. ConclusionOur findings demonstrate that BD episodes are underpinned by a conserved functional scaffold and distinct individual-specific neural fingerprints. Edge-centric dynamics--especially those derived from individual-specific decompositions--offer robust biomarkers for mood state characterization and symptom severity, and may facilitate future personalized interventions in BD.

neuroscience↗

bioRxiv2025

Development of a High-Throughput Screening Method for Anti-NNV Drugs

Nervous necrosis virus (NNV) is an infectious pathogen, characterized by rapid infection and high mortality, commonly encountered in the aquaculture industry. Therefore, there is an urgent need to develop effective antiviral drugs against NNV, which is crucial for targeted treatment strategies, pathogen control, and loss reduction. Screening of effective antiviral active compounds, namely drug precursors, is key to developing highly efficient drugs against NNV. Reverse transcription-quantitative real-time PCR (RT-qPCR) is widely applied to screen and evaluate various substances to prevent and control virus infection. However, RT-qPCR is a cumbersome procedure and not suitable for high-throughput rapid screening. The aptamer TNA1c, which specifically binds to NNV-infected cells, was used to construct a target-driven activatable aptamer probe (TAA). Then, the TAA probe was applied to establish a high-throughput screening (TAA-HTS) method for efficient evaluation of substances against NNV infection. TAA-HTS technology achieved rapid, sensitive, and specific screening of bioactive substances with significant anti-NNV effects. As compared to commonly used analytical methods, such as RT-qPCR, TAA-HTS has the advantages of easy operation and high sensitivity and specificity. The findings of this study provide data support and a theoretical basis for the development of effective antivirus preparations. IMPORTANCEIn this study, a target-driven activatable aptamer probe was employed to establish a high-throughput screening method for anti-NNV drugs. This method enables rapid, sensitive, and specific screening of anti-NNV compounds while reducing background interference. Although RT-qPCR is currently the most widely used technique for antiviral drug screening and validation, the screening approach developed in this study significantly shortens screening time while achieving results consistent with those of RT-qPCR. This demonstrates that the screening method presented here has the potential to become a universal strategy for future drug screening against viral diseases. Moreover, this study not only provides a novel technical solution for NNV control but also demonstrates the potential of aptamer-based probes in high-throughput antiviral screening, serving as a model for combating viral pathogens in aquaculture and other fields.

pathology↗

bioRxiv2025

Ampk alpha2 T172 Activation Dictates Exercise Performance and Energy Transduction in Skeletal Muscle

AMPK (5'-AMP-activated protein kinase) is an energetic sensor for metabolic regulation and integration. Here, we employed CRISPR/Cas9 to generate non-activatable Ampk knock-in (KI) mice with mutation of threonine 172 phosphorylation site to alanine, circumventing the limitations of previous genetic interventions that disrupt the protein stoichiometry. KI mice of Ampk2, but not Ampk1, demonstrated phenotypic changes with increased fat-to-lean mass, impaired endurance exercise capacity, and diminished mitochondrial maximal respiration and conductance in skeletal muscle. Integrated temporal multi-omic analysis (proteomics/phosphoproteomics/metabolomics) in skeletal muscle at rest and during exercise establishes a pleiotropic yet imperative role of Ampk2 T172 activation for glycolytic and oxidative metabolism, mitochondrial respiration, and contractile function. Importantly, there is a significant overlap of skeletal muscle proteomic changes in Ampk2 T172A KI mice with that of type 2 diabetic patients. Our findings suggest that Ampk2 T172 activation is critical for exercise performance and energy transduction in skeletal muscle and may serve as a therapeutic target for type 2 diabetes.

molecular biology↗

bioRxiv2025

Next-Generation Multiplexed Targeted Proteomics Quantifies Post-Translational Modifications, Compound-Protein Interactions, and Disease Biomarkers with High Throughput

The GoDig platform enables sensitive, multiplexed targeted pathway proteomics without manual scheduling or synthetic standards. Here we present GoDig 2.0, which increases sample multiplexing to 35-fold, improves time efficiency and reduces scan delays for higher success rates, and allows flexible spectral and elution library generation from different mass spectrometry data types. GoDig 2.0 measures 2.4x more targets than GoDig 1.0, quantifying >99% of 800 peptides in a single run. We compiled a library of 23,989 human phosphorylation sites from a phosphoproteomic dataset and used it to profile kinase signaling differences across cell lines. In human brain tissue, we established a hyperphosphorylated tau assay including pTau127, revealing potential biomarkers for Alzheimers disease. We also quantified diglycyl-lysine peptides to assess polyubiquitin branching. Finally, we built a library of 20,946 reactive cysteines and profiled covalent compound-protein interactions spanning diverse pathways. GoDig 2.0 enables high-throughput analyses of site-specific protein modifications across many biological contexts.

biochemistry↗

bioRxiv2025

wspA mutation mediated Pseudomonas aeruginosa rugose small colony variant and its hyper-biofilm

Pseudomonas aeruginosa is a major multidrug-resistant pathogen whose biofilm formation complicates treatment. P. aeruginosa rugose small colony variants (RSCVs), characterized by enhanced biofilm production and resistance, pose significant clinical challenges. However, the formation mechanisms remain unclear in clinical strains. Paired clinical P. aeruginosa isolates AR8023-1 (wild-type) and AR8023-2 (RSCV) were collected from urine samples of a neurosurgery inpatient chronologically. The genome comparison was performed using Breseq, and plasmid complementation was performed to construct complemented strain (AR8023-2RifR::wspAAR8023-1). Then, all the aforementioned strains were examined RSCV phenotype, biofilm formation, and motility, and gene expression differences induced by the wspA mutation were analyzed via transcriptome sequencing, followed by quantification of c-di-GMP. In addition, we assessed antimicrobial susceptibility of the strains under both planktonic and biofilm conditions. The results showed that both AR8023-1 and AR8023-2 were identified as ST3420. AR8023-1 exhibited typical smooth morphology, while AR8023-2 displayed a RSCV phenotype. A single SNP difference was identified between the two strains, characterized by a deletion of glutamine at position 289 (CAG triplet deletion) in the wspA gene of strain AR8023-2. The complement isolates restored wild-type morphology, motility, and biofilm formation. Transcriptomics revealed a significant upregulation of c-di-GMP metabolic genes in RSCVs (P < 0.001), indicating that the WspA{Delta}289Q mutation activates diguanylate cyclase (DGC) activity, thereby elevating c-di-GMP synthesis. Intracellular c-di-GMP levels were significantly higher in RSCVs than those in WT and complement isolates, respectively (P < 0.001). Consequently, biofilm susceptibility testing demonstrated the {beta}-lactams MBIC of RSCVs was [≥]8-512-fold higher than those of WT and complement isolates. This study identifies a clinical WspA{Delta}289Q mutation that elevates c-di-GMP, driving RSCV formation and biofilm-mediated resistance. IMPORTANCEP. aeruginosa is a leading multidrug-resistant pathogen whose biofilm formation capability greatly complicates treatment outcomes. This study identifies a novel clinical mutation in the wspA gene (WspA{Delta}289Q) that drives the emergence of rugose small colony variants (RSCVs), a phenotype linked to heightened biofilm production and antimicrobial resistance. We demonstrate that this mutation upregulates cyclic di-GMP synthesis, leading to increased biofilm formation and markedly reduced susceptibility to {beta}-lactam antibiotics under biofilm conditions. These results provide important insights into the genetic basis of RSCV development in clinical settings and highlight the role of c-di-GMP signaling in biofilm-associated resistance. Our findings underscore the need to target c-di-GMP pathways as a potential strategy for combating persistent P. aeruginosa infections.

microbiology↗

bioRxiv2025

Hyperbolic Wavelet Interaction Network (HyWinNet): Multi-Scale Geometric Learning for Interpretable Protein-Protein Interaction Prediction

Understanding the multi-scale organization of protein-protein interactions (PPIs) is fundamental to deciphering cellular signaling, allosteric regulation, and disease mechanisms, yet existing computational approaches fail to simultaneously resolve atomic-scale binding interfaces and pathway-level coordination. We present HyWinNet, a geometric deep learning framework that unifies Lorentzian hyperbolic graph neural networks with spectral graph wavelet transforms to intrinsically model the hierarchical architecture of biological interactions. The core innovation lies in three synergistic components: (1) Hyperbolic embeddings that preserve evolutionary-conserved topological relationships through negatively curved space projections, capturing scale-free properties of protein networks; (2) Multi-scale wavelet decomposition operating at biologically grounded resolutions to jointly analyze local residue contacts and global pathway dynamics; (3) Contrastive learning with stochastic feature dropout that mimics natural binding interface plasticity. Benchmarked against state-of-the-art methods, HyWinNet demonstrates superior performance in recovering known interactions while predicting previously unannotated functional sites validated through independent structural and biochemical studies. By bridging hyperbolic geometry with graph signal processing, this work establishes a new paradigm for analyzing multi-scale biological networks, offering both interpretable computational predictions and testable hypotheses for experimental validation. The frameworks ability to map hierarchical relationships from atomic details to system-level modules provides a transformative tool for drug discovery and mechanistic studies of complex diseases.

bioinformatics↗

bioRxiv2025

Endothelial LRRC8C associates with LRRC8A and LRRC8B to regulate vascular reactivity and blood pressure

Vascular tone is impacted by the endotheliums ability to detect mechanical and chemical stimulation. Leucine-Rich Repeat-Containing protein 8A, (LRRC8A), was previously identified as a required component of the mechanoresponsive endothelial LRRC8 complex regulating AKT-endothelial nitric oxide synthase (eNOS) signaling and vascular function. While LRRC8A is broadly expressed, LRRC8B, C, D and E have tissue-restricted expression. Here, we identified 2 single nucleotide polymorphisms (SNPs) in LRRC8C highly associated with elevated diastolic and systolic blood pressure in human genetic studies, implicating LRRC8C as a regulator of vascular function. While LRRC8A/B/C/D/E are expressed in endothelium, co-immunoprecipitation experiments from lung endothelium using Lrrc8a-3xFlag knock-in mice, Lrrc8c-HA knock-in mice and endothelium-specific Lrrc8a-3xFlag overexpression mice demonstrate the endothelial LRRC8 complex to be composed largely of LRRC8A/B/C heteromers. Lrrc8a/b/c knock-out studies in mice and knock-down studies in human umbilical vein endothelial cells show co-dependent expression of LRRC8A/B/C proteins, but not LRRC8D. Functionally, LRRC8A and LRRC8C depletion reduces endothelial volume regulatory anion channel (VRAC) currents, inhibits AKT-eNOS signaling, increases myogenic tone, impairs eNOS dependent vasodilation, and exacerbates angiotensin-induced hypertension. These data identify LRRC8A, LRRC8B and LRRC8C as components of the endothelial LRRC8 complex and reveal LRRC8C as having a non-redundant role in regulating endothelial AKT-eNOS, vascular relaxation and susceptibility to hypertension.

cell biology↗

bioRxiv2025

Human hippocampal ripples prioritise model-based learning

Humans often learn optimally, inferring the value of options they have never directly experienced by leveraging internal models of the world, a process known as model-based learning. Yet how the brain decides which unexperienced options to update first remains unclear. Here we recorded intracranial EEG from 34 epilepsy patients performing a reinforcement-learning task that required using task structure to infer the value of unvisited (non-local) paths. After each reward, brief hippocampal "ripple" events signalled which indirect experience was most valuable, thereby encoding that paths priority. Longer ripples carried the strongest priority signals, allowing the brain to update high-value, unvisited options first and thereby optimise learning. Ripple events also coincided with selective cortical reactivation of these high-priority paths, consistent with prioritised replay. Crucially, when hippocampal ripples were precisely synchronised with activity in the lateral frontopolar cortex (LFPC), individuals showed greater model-based learning: stronger ripple-LFPC coupling predicted more effective use of task structure and more accurate learning of non-local values. Our findings establish hippocampal ripple-centred prefrontal coordination as a fundamental mechanism that prioritises valuable experiences for model-based learning, explaining how the human brain learns so efficiently.

neuroscience↗

bioRxiv2025

Baseline expression of c-Myc defines the tissue specificity of oncogenic K-Ras

KRAS is among the most frequently mutated oncogenes in cancer. Yet, mutations in KRAS are common only in tumors originating from a subset of tissues. It is critical to understand the molecular mechanisms underlying this oncogene tissue specificity. Utilizing genetically engineered mouse models carrying a conditional oncogenic allele of Kras, we expressed activated K-Ras in adult tissues to investigate its specificity. We discovered that the ability of K-RasG12D to influence the fitness of cells in a given tissue is not determined by its canonical signaling through MAPK. Instead, low baseline expression of c-Myc renders tissues non-permissive to oncogenic K-Ras, a context that can be reversed in the liver by ectopically expressing c-Myc. This functions independently of the proliferative index of the tissue or the induction of cell cycle arrest or apoptosis. Our findings reveal the importance of the basal state of the tissue-inherent signaling network for determining oncogene specificity.

cancer biology↗

bioRxiv2025

AF3Score: A Score-Only Adaptation of AlphaFold3 for Biomolecular Structure Evaluation

Scoring biomolecular complexes remains central to structural modeling efforts. Recent studies suggest that AlphaFold (AF) - a revolutionary deep learning model for biomolecular structure prediction - has implicitly learned an approximate biophysical energy function. While many researchers highly rely on AF-derived scores for structure evaluation, existing AlphaFold2-based implementations require iterative refinement of the input structure, leading to biased scoring. To address this limitation, we adapted AlphaFold3 into a score-only model, AF3Score, by directly feeding input coordinates into the confidence head while bypassing the diffusion-based structure module. AF3Score demonstrates robust performance in structural quality assessment across diverse systems including monomeric proteins, protein-protein complexes, de novo designed binders, and fold-switching proteins. In benchmarking designed binder screening, AF3Score outperformed state-of-the-art methods for 8 out of 10 targets. Moreover, combining AF3Score with AlphaFold2-derived methods significantly improved the enrichment of experimentally validated binders, increasing the success rate from 15.2% to 31.6%. Additionally, AF3Score effectively identified stable conformations in fold-switching proteins, whereas AlphaFold predominantly predicted only the dominant fold. These findings highlight the broad applicability of AF3Score, from high-throughput screening in de novo binder design to filtering docking-generated poses and molecular dynamics (MD) trajectories.

bioinformatics↗

bioRxiv2025

Modeling host-microbe interactions in immunocompetent engineered human gut tissues

The intestinal mucosal barrier contains microbial organisms within the lumen while preserving the ability to absorb nutrients. Dietary, microbial, and other exposures shaped human barrier evolution and continue to impact disease susceptibility. Here, we established engineered barrier models of the human small intestine and colon composed of a multilineage epithelium, mucus layer, accessible microbial compartment and autologous tissue-resident immune cells. The epithelium has crypt- and villus-like topological domains, with stem cells differentiating into absorptive and secretory lineages with region-specific identities. Secreted mucins accumulate apically, forming a dense mucus layer separating the epithelium from colonizing commensal and pathogenic bacteria. Intestinal memory T cells integrate into and interact with the epithelium. We use the engineered intestinal tissues to identify an epithelial gene regulatory network underlying response to Salmonella Typhimurium infection, and uncover epithelial-immune-pathogen crosstalk coordinating cytokine release and epithelial damage. Overall, this work allows for the modular integration of epithelial, microbial, and immune compartments providing a versatile system for studying human intestinal physiology and pathologies.

bioengineering↗

bioRxiv2025

Oncogene Silencing via ecDNA Micronucleation

Extrachromosomal DNA (ecDNA) contributes to cancer genome instability by enabling high-copy oncogene amplification, intratumoural heterogeneity and rapid genetic change. Micronuclei (MN) are frequently observed in chromosomally unstable cancers, yet their origins and relevance in ecDNA-driven tumours remain incompletely understood. Here, we investigate the relationship between ecDNA segregation errors and MN formation. We find that ecDNA frequently localizes to MN and represents a prominent source of MN content in ecDNA-positive cancer cells. Mitotic clustering of oncogene-bearing ecDNAs is associated with asymmetric inheritance and mis-segregation into MN. Transfer of oncogenes from ecDNA into MN is accompanied by reduced transcriptional output. Single-MN sequencing shows that individual MN are enriched for ecDNA to a degree that exceeds expectations from stochastic mis-segregation and that in MN oncogenes originating from multiple distinct ecDNAs coalesce. Using live-cell imaging, we observe that cells inheriting ecDNA-positive MN show limited proliferative capacity and an increased likelihood of cell death. In neuroblastoma patients with MYCN-amplified ecDNA, higher frequencies of ecDNA-positive MN at diagnosis are associated with improved event-free and overall survival. Together, these findings link ecDNA mis-segregation to MN formation and reduced cellular fitness, suggesting that ecDNA-positive MN may reflect a state of impaired oncogenic ecDNA function with potential relevance for clinical outcome.

cancer biology↗

bioRxiv2025

Domain-general neural effects of associative learning and expectations on pain and hedonic taste perception

Predictive cues significantly influence perception through associative learning. However, it is unknown whether circuits are conserved across domains. We investigated how associative learning influences perceived intensity and valence of pain and hedonic taste and whether the mechanisms that support expectancy-based modulation vary as a function of aversiveness and modality. Sixty participants were randomly assigned to receive either painful heat, unpleasant liquid saline, or pleasant liquid sucrose during fMRI scanning. Following conditioning, cues that were initially associated with low or high intensity outcomes were intermittently followed by stimuli calibrated to elicit medium intensity ratings. Learned cues modulated expectations and subjective outcomes similarly across domains. Consistent with this, the orbitofrontal cortex exhibited domain-general anticipatory activation. The left anterior insula mediated domain-general cue effects on subjective intensity, whereas the thalamus mediated cue effects on subjective valence. Notably, these two regions were nearly identical to those previously implicated in mediating cue effects on pain (nearly identical to those previously implicated in cue effects on mediating pain (Atlas et al., 2010). Pain specificity was evident when we measured variations in stimulus intensity, whether we used univariate or multivariate approaches, but there was minimal evidence of specificity by modality or aversiveness when we examined cue effects on medium trials. These findings suggest that shared neural circuits mediate the effects of learned expectations on perception, linking pain with other areas of affective processing and perception across domains.

neuroscience↗

bioRxiv2025

Active Learning for Protein Structure Prediction

Accurate protein structure prediction is challenging especially for families and types that are not well-represented in current training data. Active learning selects candidates for labeling with the aim of most rapidly improving model performance. In a general, many labeling strategies have been proposed. However, with protein structure prediction, most of these strategies dont apply or are difficult due to the high-dimensional, variable-dimensional regression target and the inherent complexity of the models involved. We applied a novel active learning strategy, DEW-DROP, to protein structure prediction on two different protein datasets: VHH-only antibodies (Nanobodies), and Mycobacterial proteins. We introduce a domain-specific fine-tuned Equifold model for VHH structures and apply DEWDROP to generate ensembles of predictions using Monte Carlo dropout. Using the statistics of these we select batches with high information content for labeling. We show that DEWDROP (1) improves model training efficiency through batch optimization outper-forming baselines, and (2) selects data with relevant high information content.

molecular biology↗

bioRxiv2025

AI-Guided Precision in Antibody Humanization: Structural Modeling to Minimize Immunogenicity and Preserve Efficacy

Antibody humanization is an essential part of converting animal-derived antibodies into clinical candidates however conventional CDR grafting techniques often faces challenges, especially when essential antigen-binding sites extend beyond CDR regions. Additionally, the CDR grafting process tends to retain undesired non-human elements within the CDR regions. In this study, we overcame these challenges by using our proprietary AI-predicted antigen-antibody complex algorithm to effectively humanize a murine antibody. During our AI-powered humanization process, in contrast to traditional CDR grafting techniques, only the crucial paratopes identified through our AI-predicted complex structure were precision grafted onto a select human germline. This paratope grafting technique significantly reduced the antibodies immunogenicity risk while maintaining bioactivity. Moreover, all AI-guided deep humanized antibody variants demonstrated favorable developability, including thermal stability, poly-reactivity, and hydrophobicity. These results not only advance antibody humanization techniques but also demonstrate the power of AI in expediting antibody engineering and derisking clinical research.

bioengineering↗

bioRxiv2025

Zinc Finger Repressors mediate widespread PRNP lowering in the nonhuman primate brain and profoundly extend survival in prion disease mice

Prion disease is a rapidly progressing and invariably fatal neurodegenerative disorder with no approved treatment. The disease is caused by the self-templated misfolding of the prion protein (PrP) into toxic species, ultimately leading to neurodegeneration and death. We evaluated a novel epigenetic regulation approach using Zinc Finger Repressors (ZFRs) to ablate PrP expression at the transcriptional level. When delivered using adeno-associated virus (AAV), ZFRs potently and specifically reduced prion mRNA expression by >95% in vitro and to near undetectable levels within single neurons in vivo. In wildtype mice, ZFRs stably lowered neuronal PrP expression throughout the central nervous system for at least 17 months. In mice inoculated with misfolded PrP, AAV-ZFRs given at either early or late disease stages profoundly extended lifespan, significantly reduced PrP in the brain, and improved an array of molecular, histological, biomarker, and behavioral readouts. Finally, we delivered a ZFR targeting the human prion gene (PRNP) to cynomolgus monkeys using a novel blood-brain-barrier penetrant AAV capsid. Extensive bulk and single-cell assessments revealed widespread ZFR expression and PRNP repression in all 35 brain regions assessed, providing the first demonstration of epigenetic regulation across the nonhuman primate neuraxis following a single intravenous (IV) dose. These results highlight the potential of a one-time IV administered ZFR treatment for prion disease and other neurological disorders.

neuroscience↗

bioRxiv2025

Selective depletion of cancer cells with extrachromosomal DNA via lentiviral infection

Dear Editor Extrachromosomal DNA (ecDNA), a major focal oncogene amplification mode found across cancer, has recently regained attention as an emerging cancer hallmark1,2, with a pervasive presence across cancers3,4. With technical advancements such as high-coverage sequencing and live-cell genome imaging, we can now investigate ecDNAs behaviors and functions 5-7. However, we still lack an understanding of how to eliminate ecDNA. We observed depletion of cells containing ecDNA during lentiviral but not transposon-based transduction while we sought to investigate the mechanism of ecDNA behavior. This discovery may provide critical information on utilizing a lentiviral system in emerging ecDNA research. Additionally, this observation suggests specific sensitivities for cells with ecDNA.

cancer biology↗