Biology subjects

Kang, B.

Publications and source records attributed to Kang, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Blood-brain barrier-traversing multivalent TGFβ trap for anti-cancer immunomodulation of gliomas

Clinical studies have revealed that glioblastoma (GBM) is refractory to immunotherapy, such as immune checkpoint inhibitors (ICIs), due to the immunosuppressive tumor microenvironment (TME) primarily intensified by intratumoral upregulation of transforming growth factor-{beta} (TGF{beta}). Primary GBM tumor bulks are surgically removed in clinic, but invasive tumor cells infiltrated into surrounding normal brain tissue cannot be eradicated by systemic therapy due to the integral blood-brain barrier (BBB), leading to near-inevitable tumor recurrence. To address these translational challenges, we have engineered a TGF{beta}-inhibiting protein nanocage comprising human heavy-chain ferritin protein and TGF{beta} receptor II ectodomain that enables BBB penetration and multivalent TGF{beta} entrapment, respectively. We demonstrate that the nanocage platform, denoted T{beta}R2-HFt, following systemic administration, traverses the BBB, and extensively modulates immunosuppressive TME, both stromal and immune components, thereby potentiating the therapeutic efficacy of a clinically used ICI in multiple mouse glioma models. Notably, a majority of the glioma-bearing animals that received surgical tumor resection, followed by treatment with T{beta}R2-HFt and the ICI, survived the tumor and resisted tumor rechallenge, providing a potential means to control GBM in a durable manner.

bioengineering↗

bioRxiv2026

P4HA1 sustains endocrine resistance in luminal breast cancer via regulation of cellular stress

The hormone receptor-positive luminal subtype accounts for approximately 70% of all diagnosed breast cancer cases, with endocrine regimens serving as the first-line therapy. However, 30% to 50% of luminal breast cancer patients acquire therapeutic resistance with five to ten years of treatment. Understanding the mechanisms of endocrine resistance and identifying new therapeutic targets remain urgent priorities. Here, we demonstrate that expression of the alpha subunit of prolyl 4-hydroxylase (P4HA1), a key collagen-modifying enzyme, is significantly upregulated in endocrine therapy-resistant luminal breast cancer cells compared to parental endocrine therapy-sensitive cells. Clinically, elevated P4HA1 expression correlates with shortened relapse-free survival in patients with luminal breast cancer. Genetically knocking down P4HA1 or pharmacologically inhibiting its activity successfully restores the sensitivity of endocrine-resistant luminal breast cancer cells to hormone therapy. Mechanistically, we reveal that combining P4HA1 inactivation with endocrine treatment dramatically enhances endoplasmic reticulum (ER) stress-related gene expression. Consistently, treating resistant cells with an exogenous ER stress inducer similarly enhances sensitivity to hormone therapy. Furthermore, we demonstrate that ER stress induced by P4HA1 inactivation is molecularly mediated by an accumulation of reactive oxygen species (ROS). Together, these findings uncover a novel, non-canonical role for P4HA1 as a critical mediator linking endocrine resistance to cellular stress adaptations, positioning P4HA1 as a promising therapeutic vulnerability in endocrine-resistant breast cancer. P4HA1 serves as a necessary role in maintenance resistant state, making it a viable target for combination therapies to overcome endocrine resistance.

cancer biology↗

bioRxiv2026

Extracts of Bacillus proteolyticus strain OSUB18 exhibit cytostatic activity

Although therapeutic advancements have dramatically improved outcomes for cancer patients in recent decades, advanced cancer remains a significant challenge. There is still an urgent need for new and more effective strategies that minimize toxicity and improve survival outcomes in cancer patients. Natural products from microorganisms are a rich and promising source of bioactive compounds with substantial therapeutic potential in cancer treatment. Recently, B. proteolyticus strain OSUB18, isolated from switchgrass, has been demonstrated to promote plant growth and regulate reactive oxygen species (ROS) production. Here, we investigated the anticancer potential of this plant-associated microbe and demonstrated that its crude extracts exhibited broad-spectrum cytostatic activity across diverse solid tumor cell lines. Importantly, OSUB18 extracts displayed limited cytotoxicity toward normal human mammary epithelial cells and mouse fibroblasts. This potentially selective cytostatic activity was retained within a water soluble, heat-labile, and protease-resistant fraction. The extracts primarily suppressed cell proliferation, accompanied by increased lipid peroxidation, with preliminary evidence suggesting the potential involvement of ferroptosis. These findings suggest that the bioactive component may be a selective cytostatic agent with multifaceted mechanisms of action.

cancer biology↗

bioRxiv2026

Neutrophil migration in the lung is altered by alveolar collapse and stretch

RationaleHeterogeneous alveolar collapse is prevalent in inflammatory lung conditions such as chronic obstructive pulmonary disease, acute respiratory distress syndrome, and pneumonia. Although neutrophil-released proteases contribute to the tissue remodeling that leads to alveolar collapse, how this altered mechanical environment in turn affects neutrophil migration remains largely unexplored. ObjectivesIn this study, we investigate how alveolar collapse and stretch influence neutrophil migration and identify the mechanical and biochemical factors that govern regional migration differences. MethodsWe developed a novel precision-cut lung slice platform that generates collapsed vs non-collapsed regions within the same slice. Neutrophils in both regions were longitudinally imaged for up to 5 hours to quantify motility behavior. Migration mechanisms were probed using migration-related inhibitors, collagenase, and cigarette smoke extract. A crystal ribcage system, which preserves intact alveolar shape and the air-liquid interface, was also used to assess the effects of ventilation on neutrophil migration. ResultsNeutrophil migration was faster in the collapsed region compared to not-collapsed regions. This regional difference was eliminated by Rho-associated protein kinase (ROCK) inhibition, which selectively increased migration speed in the non-collapsed region. The regional difference persisted with the addition of collagenase and cigarette smoke extract, both of which significantly increased the migration speed in both regions. In the crystal ribcage, the preserved air-liquid interface and ventilation together enhanced neutrophil migration compared with a collapsed lung. ConclusionsAlveolar collapse and stretch facilitate neutrophil migration, indicating the role of localized tissue remodeling in driving neutrophil activity and further disease progression.

bioengineering↗

bioRxiv2026

Phenotypic Analysis of GGDEF/EAL Domain Protein Function in Phytopathogenic Pantoea ananatis

Cyclic diguanosine monophosphate (c-di-GMP) is a ubiquitous bacterial second messenger that regulates diverse cellular processes, including colony morphology, motility, biofilm formation, and virulence. It is synthesized by diguanylate cyclases (DGCs) containing the GGDEF domain and degraded by phosphodiesterases (PDEs) containing the EAL domain. However, studies on the genetic and physiological characteristics of c-di-GMP metabolism in Pantoea ananatis are lacking. In this study, we identified 26 predicted c-di-GMP metabolism-related genes in the P. ananatis PA13 genome: 9 encode GGDEF-only domain proteins, 5 encode dual GGDEF/EAL domain proteins, and 12 encode EAL-only domain proteins. We constructed overexpression strains and mutants of 26 DGC- and PDE-encoding genes, and then assessed their Congo Red binding, mucoid and rugose phenotypes, pellicle formation, and swimming motility. We identified 14 of 26 DGC and PDE proteins that affect phenotype changes. Among the 26 DGC- and PDE-overexpressing strains, 13 exhibited the phenotypic changes described above, with some showing alterations in multiple phenotypes simultaneously. Notably, overexpression of dgcM induced changes across all phenotypes. Among the 26 DGC and PDE mutants, the pdeC mutant increased pellicle formation and Congo red binding, the pdeM mutant reduced the mucoid phenotype, and the pdeS mutant, which shows high similarity to ydiV, an anti-FlhD factor, increased swimming motility. Overexpression strains and mutants of 14 DGC and PDE proteins that exhibited phenotypic changes had higher intracellular c-di-GMP levels than the wild type. This study provides important insight into the role of the c-di-GMP network in the plant pathogen P. ananatis. IMPORTANCEPantoea ananatis is a versatile bacterium that causes significant diseases in various economically important plants. To survive and infect hosts, bacteria use a key signaling molecule called c-di-GMP to switch between swimming freely and forming protective communities known as biofilms. Despite its importance, the specific genes governing this signaling network in P. ananatis remained unknown. In this study, we systematically identified and characterized 26 genes responsible for regulating c-di-GMP levels in P. ananatis PA13. By analyzing mutants and overexpressing these genes, we pinpointed 14 critical factors that control essential behaviors such as motility, pellicle formation, and colony appearance. Notably, we discovered specific genes, such as dgcM and pdeS, that act as master regulators of these traits. This comprehensive functional map of the c-di-GMP network provides essential insights into how this pathogen adapts to its environment, offering potential targets to control plant infections.

microbiology↗

bioRxiv2026

MNS induces antiviral protection and suppresses inflammation

BackgroundIdentifying safe and broad-spectrum antiviral and anti-inflammatory agents remains an urgent need in infectious and inflammatory diseases. Here, we demonstrated that MNS (NSC170724), a small-molecule nitrovinyl benzodioxole, enhanced antiviral defense while limiting excessive inflammation. MethodsThe antiviral activity of MNS was evaluated in multiple cell lines and mouse infection models across DNA and RNA viruses. Virus-induced and LPS-induced inflammatory responses were assessed using RT-qPCR, ELISA and western blotting. Bulk RNA-seq and ATAC-seq were performed to define transcriptional and epigenetic mechanisms. ResultsMNS significantly suppressed viral infection in vitro and improved survival in four lethal viral infection models, accompanied by reduced viral loads and attenuated tissue injury. MNS also diminished virus-triggered and LPS-triggered inflammatory cytokine production in macrophages and multiple mouse organs, and protected mice from LPS-induced endotoxic lethality. Multi-omics profiling showed that MNS broadly repressed LPS-induced inflammatory transcriptional programs and reversed chromatin accessibility gains across promoters and transcription start sites. Joint analysis of RNA-seq and ATAC-seq data demonstrated consistent downregulation of pivotal inflammatory pathways, such as NF-{kappa}B, Toll-like receptor, and TNF signaling. ConclusionsWith potent activity against viral replication and inflammation in cellular and animal models, MNS emerges as a promising candidate for the treatment of viral infections and hyperinflammatory conditions.

systems biology↗

bioRxiv2025

Mosaic pattern: lung functional heterogeneity at the alveolus level

Inhaled particles carrying pathogens, pollutants (e.g., microplastics, smoke), therapeutics, and diagnostics are increasingly relevant to public health, yet real-time tracking of aerosol transport in functional alveoli remains challenging. Here, we used the recently developed crystal ribcage to investigate aerosol transport in ex vivo lungs during active ventilation, obtaining the first real-time observations of single aerosol droplet transport and deposition in functional alveoli. We discovered deterministic heterogeneity at both intra- and inter-alveolar levels, with aerosol distribution forming a characteristic "mosaic" pattern in which only specific alveolar clusters received particles. The pattern was consistently formed in vivo during spontaneous breathing and ex vivo using both positive- and negative-pressure ventilation. This pattern was also consistent across a range of aerosols, including small molecules, nanobodies, nanoparticles, microplastics, therapeutics, and pathogens. Additionally, the pattern was observed in murine, porcine, and human lungs, and evolved from birth through aging in mice. The post-deposition stability of the pattern depended on particle type and lung age, lasting from a few minutes for small molecular weight particles to multiple days for cell-binding particles. These alveolar-level heterogeneities may uncover previously unrecognized biological and immunological heterogeneities associated with the mosaic pattern, including its role in postnatal lung development, susceptibility to inhaled airborne hazards such as pollutants and infectious agents, and early pathogenesis and response to inhaled therapeutics in respiratory diseases such as pneumonia, COPD, asthma, and lung cancer.

bioengineering↗

bioRxiv2025

Enhancing protein structure prediction accuracy by prioritizing important residues using protein language models

Accurate prediction of protein tertiary structures from amino acid sequences remains a fundamental challenge in computational biology. Although AlphaFold2 represents a major advance, systematic discrepancies persist between its predictions and experimentally determined structures. Given that individual residues contribute differentially to protein function, we hypothesized that incorporating residue-specific importance metrics could improve prediction accuracy. Here, we develop i-Fold (importanceFold), an enhanced neural architecture enhances the AlphaFold2 architecture by integrating protein language model ESM-derived residue importance scores (RIS) as dynamic positional weights during training. Our approach dynamically weights amino acids using RIS during structure prediction, thereby directing computational attention toward functionally critical residues and regions. Evaluation on a benchmark test set of 3,559 protein structures reveals that i-Fold significantly improves accuracy (reduction in r.m.s.d., p = 0) and achieves a higher prediction success rate (7.6% improvement: 55.1% [->] 62.7%). Notably, i-Fold demonstrates particular improvements for targets that are typically challenging for AlphaFold2, including ribosomal proteins, membrane proteins, and orphan proteins. Consistent results were obtained on a completely independent test set of 167 recently released protein structures, where i-Fold again exhibited a higher prediction success rate (6.0% improvement: 43.7% [->] 49.7%) compared to AlphaFold2. Our findings indicate that explicit integration of RIS can advance the state-of-the-art in protein structure prediction, producing more accurate and generalizable models without substantially increasing computational cost.

bioinformatics↗

bioRxiv2025

Molecular pathology of acute spinal cord injury in middle-aged mice

The median age of sustaining a spinal cord injury has steadily increased from 29 to 43 over the last several decades. Although more pre-clinical studies in aged rodents are being done to address this shift in demographics, there have not been comprehensive transcriptomic studies investigating SCI pathobiology in middle-aged mice. To address this gap in knowledge, we compared behavioral, histopathological, and transcriptional outcomes in young (2-4 months-old) and middle-aged (10-12 months-old) mice. In contrast to previous studies, open field tests showed no differences in locomotor recovery between the young and middle-aged mice over a one-month period. The injury site also demonstrated similar histopathology in terms of lesion size, and numbers of macrophages and fibroblasts. Acutely after injury, proliferation of macrophages, fibroblasts, and astrocytes were also similar between the two age groups. In addition, spatial transcriptomics showed similar transcriptionally defined regions around the injury site at 3 days post injury. However, single cell RNA-sequencing of the cells at the injury site and surrounding spared tissue showed differences in select cell subpopulations. Taken together, our results indicate that although young and middle-aged mice display similar locomotor recovery and histopathology after SCI, changes in cell subpopulations may underly a decline in repair mechanisms that are manifested after this age.

neuroscience↗

bioRxiv2025

Highly accurate prediction of broad-spectrum antiviral compounds with DeepAVC

Lethal viruses, such as HIV, pose a significant threat to human life, with each pandemic causing millions of fatalities globally. Small-molecule antiviral drugs provide an efficient and convenient approach to antiviral therapy by either inhibiting viral activity or activating the host immune system. However, conventional antiviral drug discovery is often labor-intensive and time-consuming due to the vast chemical space. Although some existing computational models mitigate this problem, there remains a lack of rapid and accurate method specifically designed for antiviral drug discovery. Here, we propose DeepAVC, a universal framework based on pre-trained large language models, for highly accurate broad-spectrum antiviral compounds discovery, including two models, DeepPAVC for phenotype-based prediction and DeepTAVC for target-based prediction. As a result, DeepAVC greatly outperforms other in silico methods. More importantly, in the top predictions, MNS and NVP-BVU972 were identified as novel compounds with promising broad-spectrum antiviral activities by in vitro experiments. Finally, DeepAVC demonstrates high interpretability, one of the bottlenecks of current AI methods, due to its ability of analyzing key functional groups of antiviral compounds and important binding sites on targets.

bioinformatics↗

bioRxiv2025

CellFIE: Integrating Pathway Discovery With Pooled Profiling of Perturbations Uncovers Pathways of Huntington's Disease, Including Genetic Modifiers of Neuronal Development and Morphology

Genomic screens and GWAS are powerful tools for identifying disease-modifying genes, but it is often challenging to understand the pathways by which these genes function. Here, we take an integrated approach that combines network analysis and an imaging-based pooled genetic perturbation study to examine modifiers of Huntingtons disease (HD). The computational analysis highlighted several genes in a subnetwork enriched for modifiers of neuronal development and morphology. To test the functional roles of these genes, we developed an experimental pipeline that allows pooled CRISPRi KD of 21 genes in human iPSC-derived neurons followed by optical analysis of genotypes, neuronal arborization, multiplexed pathway activity and morphological fingerprint readout. This approach recovered known genes involved in morphology and confirmed unexpected links from the network between several genetic modifiers of HD and morphology. Our approach overcomes challenges in pooled measurement of neuronal function and health and could be adapted for other phenotypes in HD and other neurological diseases.

neuroscience↗

bioRxiv2024

Cell-type-specific splicing of transcription regulators and Ptbp1 by Rbfox1/2/3 in the developing neocortex

How master splicing regulators crosstalk with each other and to what extent transcription regulators are differentially spliced remain unclear in the developing brain. Here, cell-type-specific RNA-Seq of the developing neocortex uncover that transcription regulators are enriched for differential splicing, altering protein isoforms or inducing nonsense-mediated mRNA decay. Transient expression of Rbfox proteins in radial glia progenitors induces neuronal splicing events preferentially in transcription regulators such as Meis2 and Tead1. Surprisingly, Rbfox proteins promote the inclusion of a mammal-specific alternative exon and a previously undescribed poison exon in Ptbp1. Simultaneous ablation of Rbfox1/2/3 in the neocortex downregulates neuronal isoforms and disrupts radial neuronal migration. Furthermore, the progenitor isoform of Meis2 promotes Tgfb3 transcription, while the Meis2 neuron isoform promotes neuronal differentiation. These observations indicate that transcription regulators are differentially spliced between cell types in the developing neocortex.

developmental biology↗

bioRxiv2024

The prion-like protein Doppel: A soluble biomarker steering ovarian cancers peritoneal to circulatory dissemination

Detecting ovarian cancer (OC) early using existing biomarkers, e.g., cancer antigen 125 (CA125), is challenging due to its ubiquitous expression in many tissues. Doppel, a prion-like protein, expresses in male reproductive organ but absent in female reproductive systems and healthy tissues, but plays an important role in neoangiogenesis. Here, we have shown two platforms, soluble Doppel in sera/ascites and Doppel expressed circulating tumor cells (Dpl+CTC) in the whole blood, to detect subsets of epithelial OC (EOC). Increased level of Doppel in the sera of OC patients, in three different cohorts, confirm Doppel as OC specific biomarker. Serum Doppel level distinguishes EOC subtypes and early stages HGSOCs from non-cancerous conditions with high sensitivity and specificity. Stratifying the EOCs based on Doppel level, we categorized them into Doppel-high (Dplhi) and Doppel-low (Dpllow) groups. Using ascites-derived organoids and single cell sequencing of whole ascites of Dplhi and Dpllow patients, we identify that Doppel induces epithelial-mesenchymal transition (EMT) and creates an immunosuppressive microenvironment, respectively. Doppel levels in the sera/ascites correlate with the changes of Dpl+CTC number in whole blood, highlighting the association of Doppel-induced EMT with CTC dissemination in circulation. Thus, Doppel-based detection of EOC subtypes could be a promising platform as clinical biomarker and link Doppel-axis with OC dissemination.

cancer biology↗

bioRxiv2024

ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction

The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly focus on direct physical interactions, ignoring the broader context of nonphysical connections through intermediate proteins, thus limiting their effectiveness. The emergence of Large Language Models (LLMs) provides a new opportunity for addressing this complex biological challenge. By transforming structured data into natural language prompts, we can map the relationships between proteins into texts. This approach allows LLMs to identify indirect connections between proteins, tracing the path from upstream to downstream. Therefore, we propose a novel framework ProLLM that employs an LLM tailored for PPI for the first time. Specifically, we propose Protein Chain of Thought (ProCoT), which replicates the biological mechanism of signaling pathways as natural language prompts. ProCoT considers a signaling pathway as a protein reasoning process, which starts from upstream proteins and passes through several intermediate proteins to transmit biological signals to downstream proteins. Thus, we can use ProCoT to predict the interaction between upstream proteins and downstream proteins. The training of ProLLM employs the ProCoT format, which enhances the models understanding of complex biological problems. In addition to ProCoT, this paper also contributes to the exploration of embedding replacement of protein sites in natural language prompts, and instruction fine-tuning in protein knowledge datasets. We demonstrate the efficacy of ProLLM through rigorous validation against benchmark datasets, showing significant improvement over existing methods in terms of prediction accuracy and generalizability. Our results highlight the potential of LLMs to transform the field of PPI, serving as a robust potential tool for various categories of biological and medical research. The code is available at: https://github.com/MingyuJ666/ProLLM.

bioinformatics↗

bioRxiv2024

Comprehensive prediction and analysis of human protein essentiality based on a pre-trained protein large language model

Human essential genes and their protein products are indispensable for the viability and development of the individuals. Thus, it is quite important to decipher the essential proteins and up to now numerous computational methods have been developed for the above purpose. However, the current methods failed to comprehensively measure human protein essentiality at levels of humans, human cell lines, and mice orthologues. For doing so, here we developed Protein Importance Calculator (PIC), a sequence-based deep learning model, which was built by fine-tuning a pre-trained protein language model. As a result, PIC outperformed existing methods by increasing 5.13%-12.10% AUROC for predicting essential proteins at human cell-line level. In addition, it improved an average of 9.64% AUROC on 323 human cell lines compared to the only existing cell line-specific method, DeepCellEss. Moreover, we defined Protein Essential Score (PES) to quantify protein essentiality based on PIC and confirmed its power of measuring human protein essentiality and functional divergence across the above three levels. Finally, we successfully used PES to identify prognostic biomarkers of breast cancer and at the first time to quantify the essentiality of 617462 human microproteins. Key PointsO_LIPIC outperformed existing computational methods for predicting essential proteins. C_LIO_LIPIC could comprehensively predict human protein essentiality at levels of human, human cell lines and mice orthologues at the same time. C_LIO_LIPES could serve as a potential metric to quantify the essentiality of both human proteins and human microproteins. C_LI

bioinformatics↗

bioRxiv2024

A systems-biology approach connects aging mechanisms with Alzheimer's disease pathogenesis

Despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimers disease, the most common neurodegenerative disorder, remain incompletely understood. To define relevant pathways, we integrated the results of an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in Drosophila with human and Drosophila Alzheimers disease-associated multi-omics. We measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimers disease and identified Alzheimers disease human genetic variants that modify expression in disease-vulnerable neurons. We used a network optimization approach to integrate these data with previously published Alzheimers disease multi-omic data. We computationally predicted and experimentally demonstrated how HNRNPA2B1 and MEPCE enhance tau-mediated neurotoxicity. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate DNA damage in Drosophila and human iPSC-derived neural progenitor cells. Our work identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimers disease.

systems biology↗

bioRxiv2024

Sequence modeling and design from molecular to genome scale with Evo

The genome is a sequence that completely encodes the DNA, RNA, and proteins that orchestrate the function of a whole organism. Advances in machine learning combined with massive datasets of whole genomes could enable a biological foundation model that accelerates the mechanistic understanding and generative design of complex molecular interactions. We report Evo, a genomic foundation model that enables prediction and generation tasks from the molecular to genome scale. Using an architecture based on advances in deep signal processing, we scale Evo to 7 billion parameters with a context length of 131 kilobases (kb) at single-nucleotide, byte resolution. Trained on 2.7M prokaryotic and phage genomes, Evo can generalize across the three fundamental modalities of the central dogma of molecular biology to perform zero-shot function prediction that is competitive with, or outperforms, leading domain-specific language models. Evo also excels at multi-element generation tasks, which we demonstrate by generating synthetic CRISPR-Cas molecular complexes and entire transposable systems for the first time. Using information learned over whole genomes, Evo can also predict gene essentiality at nucleotide resolution and can generate coding-rich sequences up to 650 kb in length, orders of magnitude longer than previous methods. Advances in multi-modal and multiscale learning with Evo provides a promising path toward improving our understanding and control of biology across multiple levels of complexity.

synthetic biology↗

bioRxiv2023

Sequential genetic, epigenetic alterations in human pluripotent stem cells for recurrent abnormality

Human embryonic stem cells (hESCs) are naturally equipped to maintain genome integrity to minimize genetic mutations during early embryo development. However, genetic aberration risks and subsequent cellular changes in hESCs during in vitro culture pose a significant threat to stem cell therapy. While a few studies have reported specific somatic mutations and copy number variations (CNVs), the molecular mechanisms underlying culture-adapted phenotype acquisitions of hESCs are largely unknown. Therefore, we conducted comprehensive genomic, single-cell transcriptomic, and single-cell ATAC-seq analyses of an isogenic hESC model displaying definitive culture-adapted phenotypes. Notably, hPSCs with a copy number gain of 20q11.21 during early passage did not present culture-adapted phenotypes nor BCL2L1 induction. Single-cell RNAseq and ATACseq analyses revealed active transcriptional regulation at 20q11.21 loci at late-passaged hESCs with the induced BCL2L1 and TPX2 to trigger culture-adapted phenotypes was associated with epigenetic changes facilitating TEA domain (TEAD) binding. These results suggest that copy number 20q11.21 gain and additional epigenetic changes are necessary for expressing culture-adapted phenotypes by activating gene transcription at this specific locus.

cell biology↗