Biology subjects

Cho, Y.

Publications and source records attributed to Cho, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Macrophage-Derived 25-Hydroxycholesterol Drives Statin-Associated Acute Kidney Injury

Although acute kidney injury (AKI) has been implicated as a potential adverse effect of statin therapy, experimental evidence supporting this association remains limited. Here, we investigated whether elevated levels of 25-hydroxycholesterol (25-HC), an oxysterol produced by activated macrophages and monocytes, increase susceptibility to statin-induced AKI. We found that atorvastatin and fluvastatin induced cell death in proximal tubular cells when combined with 25-HC. Consistently, administration of low-dose lipopolysaccharide triggered AKI in statin-treated mice. Mechanistically, impaired protein prenylation disrupted the autophagosome-lysosome pathway in proximal tubular cells. AKI was attenuated by mevalonate supplementation, which restored protein prenylation, and by macrophage/monocyte depletion, which reduced the cellular sources of 25-HC. Furthermore, combined exposure to fluvastatin and 25-HC increased kidney injury molecule-1 (KIM-1) expression in human induced pluripotent stem cell-derived kidney organoids. Together, these findings suggest that elevated macrophage-derived 25-HC under conditions of infection, inflammation, or major surgery may increase susceptibility to statin-induced AKI. Our study identifies a previously unrecognized interaction between statins and 25-HC and suggests potential strategies to mitigate kidney injury while preserving the therapeutic benefits of statins.

pharmacology and toxicology↗

bioRxiv2026

Coffee induces chromatin structural changes associated with histone H4K16 hypoacetylation

Histone acetylation plays a critical role in the regulation of chromatin structure and gene expression. Among histone acetylation marks, acetylation of histone H4 at lysine 16 (H4K16ac) is particularly important for the maintenance of chromatin organization. We previously identified a reduction in H4K16ac following coffee treatment through mass spectrometry-based histone modification profiling. In the present study, we investigated the effects of coffee on H4K16ac levels, chromatin structure, and gene expression. Western blotting and immunofluorescence analyses confirmed that coffee treatment reduced H4K16ac levels in a cell type-dependent manner. Notably, H4K16ac was reduced in HCT116 cells, in which H3K27ac was not decreased by coffee treatment. Quantitative analysis of DAPI-stained nuclei revealed a significant increase in the coefficient of variation of nuclear fluorescence intensity following coffee treatment, indicating alterations in chromatin structure. Furthermore, integrative analysis of publicly available H4K16ac chromatin immunoprecipitation sequencing (ChIP-seq) data and previously generated RNA sequencing (RNA-seq) data revealed that H4K16ac-enriched genes exhibited transcriptional changes following coffee treatment in K562 cells, including the downregulation of MYC-associated genes. Together, these findings demonstrate that coffee treatment induces alterations in chromatin structure accompanied by H4K16 hypoacetylation and suggest a potential association between these epigenetic changes and transcriptional regulation.

molecular biology↗

bioRxiv2026

Cardiomyocyte Perm1 sustains cardiac bioenergetic reserve and exercise capacity

Objective: Mitochondrial oxidative capacity in striated muscle is governed by the PGC-1/ERR transcriptional axis, which controls not only steady-state respiration but the capacity to expand energy production when demand rises. Whether this reserve function requires dedicated downstream effectors, and whether its loss is detectable at rest, remains unclear. We asked whether Perm1, a striated muscle-specific PGC-1/ERR-induced regulator, is required cell-autonomously for cardiac bioenergetic reserve. Methods: We generated cardiomyocyte-specific Perm1 knockout mice using alphaMHC-nCre, complemented by an adult tamoxifen-inducible alphaMHC-MerCreMer model, and assessed cardiac development, structure, and function longitudinally through 12 months of age in both sexes. We evaluated mitochondrial function by high-resolution respirometry, transmission electron microscopy, and molecular profiling. We tested energetic capacity under load by treadmill exercise with tissue ATP quantification and by beta-adrenergic stimulation of cultured neonatal cardiomyocytes. Results: Perm1 deletion did not perturb embryonic cardiac development, and neither constitutive nor adult-onset deletion impaired cardiac structure, systolic function, or diastolic function through 12 months. Despite this preserved resting phenotype, mitochondrial respiration was reduced by 20 - 25% across complex I- and complex I+II-supported conditions, without changes in citrate synthase activity, mtDNA copy number, or OXPHOS subunit abundance. Instead, the MICOS components IMMT (MIC60) and CHCHD3 (MIC19) were selectively reduced, and mitochondrial subpopulations shifted toward fewer subsarcolemmal and more interfibrillar mitochondria, with unchanged total mitochondrial density. Functionally, Perm1-deficient mice of both sexes showed reduced treadmill capacity at 6 and 12 months and failed to maintain cardiac ATP during exercise at a submaximal workload, while skeletal muscle ATP and plasma lactate were unaffected. This deficit was cardiomyocyte-autonomous: beta-adrenergic stimulation increased ATP content in control cardiomyocytes, but Perm1 knockdown reduced this response. Conclusions. Perm1 is a cardiomyocyte-intrinsic determinant of bioenergetic reserve, dispensable for resting cardiac function but required to expand ATP production under increased demand. These findings identify a mode of metabolic regulation that is invisible to resting functional phenotyping.

physiology↗

bioRxiv2026

APOE4 reduction in vascular-associated fibroblasts decreases cerebral amyloid angiopathy and associated vascular dysfunction

Cerebral amyloid angiopathy (CAA), characterized by cerebrovascular amyloid deposition, is strongly associated with Alzheimers disease, ischemic injury, and lobar hemorrhages. APOE4 is the strongest known genetic risk factor for CAA, yet the cellular sources of APOE4 that drive pathogenic vascular amyloid deposition remain poorly defined. Here, we identify vascular-associated fibroblasts (VAFs) as a key source of APOE4 that mediates CAA and cerebrovascular dysfunction. In post-mortem brain tissue from APOE4/E4 patients with confirmed CAA, APOE colocalizes with COL6 VAFs around A{beta}-laden vessels. Therefore, to study the contribution of APOE4 derived from VAFs in CAA, independently of parenchymal sources, we used 5xFAD mice in which the endogenous mouse Apoe coding exons are replaced with floxed human APOE4 sequences. This enables selective suppression of APOE4 expression in VAFs upon tamoxifen induction of Col1a2-CreERT+. APOE4 reduction in VAFs markedly reduced CAA, with the strongest effects observed in leptomeningeal vessels. Additionally, A{beta}40, which preferentially associates with vascular amyloid, was significantly reduced in the vascular fraction. VAF APOE4 depletion resulted in fewer small microbleeds and an approximately 50% reduction in parenchymal border macrophages within the leptomeningeal compartment, whereas astrocytic reactivity remained unchanged. Functional assessment using in vivo multi-parametric photoacoustic microscopy revealed improved arterial dilation and blood flow in amyloid-laden leptomeningeal arteries. Together, these findings establish VAF-derived APOE4 as a compartment-specific driver of CAA and cerebrovascular dysfunction. One Sentence SummaryVascular-associated fibroblasts are a major source of pathogenic APOE4 that drives vascular dysfunction in cerebral amyloid angiopathy.

neuroscience↗

bioRxiv2026

Circular Data Analysis for Spatial Omics

Many biological quantities in omics are inherently periodic or directional, including circadian phase, cell-cycle position, and cellular orientation. Treating such quantities as ordinary linear variables can introduce artificial discontinuities and obscure biological interpretation. Circular methods are well developed across directional statistics, bioinformatics, and spatial statistics, but practical guidance for omics applications remains limited. We present key considerations for circular data analysis in single-cell and spatial omics, including the provenance and observational unit of the circular quantity, the inferential or predictive objective, the distinction between tissue composition and residual spatial dependence, and the choice of appropriate models. We describe relevant approaches for circular representation, inference, clustering, regression, machine learning, and spatial analysis, together with representative R software. Their practical use is illustrated through spatial transcriptomic case studies of derived circadian peak phase in a mouse model of Alzheimer's disease and latent cell-cycle position in human ovarian cancer. These examples demonstrate how the definition of the angle, its observational unit, and tissue heterogeneity shape appropriate analysis and interpretation, including when spatial modeling is not warranted. We conclude with practical recommendations and directions for future methodological development.

bioinformatics↗

bioRxiv2026

Coffee remodels the global histone acetylation landscape with reduced H3K27ac at the MYC promoter

Coffee consumption has been associated with reduced risks of multiple chronic diseases, including cancer; however, whether coffee regulates the epigenetic landscape remains largely unknown. Here, we show that coffee extensively remodels the histone acetylation landscape in human cells. Quantitative histone modification proteomics revealed a global reduction in active histone acetylation marks following coffee treatment. Mechanistically, coffee reduced the abundance of the histone acetyltransferases GCN5 and MOF, decreased 14-3-3 proteins, promoted the nuclear accumulation of class II histone deacetylases, and induced histone hypoacetylation that was reversed by the histone deacetylase inhibitor trichostatin A. Genome-wide H3K27ac profiling revealed widespread loss of promoter and enhancer acetylation, accompanied by broad transcriptional reprogramming. Integrative analyses of ChIP-seq, RNA-seq, proteomics and biochemical validation consistently demonstrated suppression of the MYC transcriptional program, including reduced MYC expression, decreased H3K27ac at MYC target promoters, downregulation of canonical MYC target genes and attenuation of MYC target gene signatures. Functionally, coffee inhibited cancer cell proliferation with minimal cell death. Collectively, our findings identify whole coffee as a previously unrecognized epigenetic modulator that remodels the histone acetylation landscape in association with suppression of the MYC transcriptional program, providing a molecular framework for understanding the epigenetic basis underlying the anti-tumor effects of coffee.

molecular biology↗

bioRxiv2026

Representative vs. Load-bearing Layers: A Dissociation in Genomic Foundation Models

Downstream use of genomic foundation models follows one of three conventions: aggregating representations across all layers (Pearce et al., 2026), defaulting to the last hidden state as a fixed feature extractor (Dalla-Torre et al., 2024), or picking a single intermediate layer via mechanistic-interpretability tooling (Brixi et al., 2026). None of these examines which layer a joint classifier actually relies on. We probe this question with a minimal training-free scalar, ||{Delta}h{ell}||2, defined as the L2 norm of the per-layer hidden-state shift at the variant token. We evaluate it on 8,008 ClinVar (Landrum et al., 2018) single-nucleotide variants in NT-v2 500M (Dalla-Torre et al., 2024), a masked language model (MLM), and Evo 2 7B (Brixi et al., 2026), a causal language model (CLM) with a hyena/attention hybrid. In both models the layer with peak single-feature AUROC (the representative layer) is not the layer a joint multi-layer classifier most depends on (the load-bearing layer, identified by leave-one-layer-out ablation drop and concordant with |SHAP| (Lundberg & Lee, 2017)). Representative layers sit mid-network in both models, whereas load-bearing depth lies at opposite ends of the depth axis: mid-shallow in the MLM, and deep in the CLM hybrid. The dissociation has direct downstream consequences. In NT-v2, a 1-dimensional mid-layer scalar exceeds the canonical 1024-dimensional last-layer mean-pool base-line by +0.049 AUROC. In Evo 2, the 4096-dimensional mean-pool is competitive with the joint ||{Delta}h{ell}||2 feature, so standard last-layer pooling leaves variant-relevant signal untapped specifically in MLM-based pipelines.

bioinformatics↗

bioRxiv2026

GDTR: Layer-wise Settling Depth Reveals Biological Grammar in Genomic Foundation Models

Genomic foundation models capture sequence regularities, yet existing interpretability tools rarely ask where in the layer stack a biological grammar becomes stable. We introduce GDTR (Genomic Deep-Thinking Ratio), a training-free residual-stream lens that assigns each nucleotide token a settling depth c(t): the first layer at which its representation stabilises against the post-final-norm reference. On Evo 2 7B, splice donor and acceptor sites settle approximately two layers earlier than intronic contexts, enhancer-like cCREs show a smaller but measurable shift, and a chr22 calibration transfers to held-out chr17. Perturbing canonical splice donors shows that the signal is bidirectional: disrupting the central GT motif deepens settling, whereas shuffling the flanking grammar makes the preserved motif settle earlier. Differential GDTR further reveals consequence-associated peak-disruption depths across ClinVar variants, with synonymous substitutions peaking deepest but with broad class overlap. GDTR therefore provides a layer-wise interpretability axis for genomic foundation models, complementary to existing prediction and variant-scoring tools.

bioinformatics↗

bioRxiv2026

Cell-specific dedifferentiation in Arabidopsis thaliana

Single somatic plant cells are capable of dedifferentiation and reversion to totipotency. However, understanding of dedifferentiation remains incomplete, limiting efforts to reprogram plant cell identities with engineered properties. Here, we describe the transcriptional dynamics of 125,091 Arabidopsis thaliana whole seedling protoplasts along a time course of dedifferentiation and varied chemical perturbations using single-cell RNA sequencing. We observed widespread cell-type-specific transcriptional reprogramming concomitant with cell wall digestion, including a guard cell-specific somatic embryogenic-like program initiated by ectopic LEC2 expression. Contrasting cell states between chemical perturbations across real time revealed that phytohormones antagonize cell aging, promote retention of transcriptional diversity, and selectively suppress wound responses. By dissecting time-resolved transcriptional dynamics, we discovered that dedifferentiation is accompanied by the activation of stem-cell and meristematic marker genes and associated with prevalent chromatin remodeling. Despite discovery of a shared regulatory program underlying early dedifferentiation, temporal modeling of diverse somatic cell types uncovered extensive bias in dedifferentiation potential, including epithem-derived dedifferentiated cells, variable dedifferentiation rates, and diversity in early reprogramming outcomes as a major source of in vitro heterogeneity. Taken together, these data showcase how initial somatic cell identity, phytohormone signaling, chromatin remodeling, and reprogramming heterogeneity combine to coordinate the early transcriptional events defining cellular dedifferentiation.

plant biology↗

bioRxiv2026

Accurate protein stability prediction for small domains using mega-scale experiments

Predicting absolute protein folding stability is a long-standing challenge in biophysics, with broad applications in protein design and in understanding genetic variation and evolution. Physics-based simulations have shown limited success at predicting stability and are often computationally intractable, and machine learning methods have been constrained by the lack of sufficiently large experimental datasets. We recently introduced cDNA display proteolysis, a cell-free approach that can measure folding stability for nearly one million protein domains in parallel. Here, we applied this method to measure stability for 1.8 million diverse protein domains 60-80 amino acids in length primarily taken from the MGnify metagenomic database and spanning over 200,000 sequence families. Using this new "MGnify Stability dataset", we developed the predictive models SaProt{Delta}G and ESM3{Delta}G, which accurately predict absolute folding stability for small domains with root mean squared error of 0.8 kcal/mol over a 6 kcal/mol range (Spearman rank correlation of 0.88). These predictors show high accuracy at predicting effects of substitutions, insertions, and deletions, successfully identify global trends toward higher stability in thermophilic organisms, and improve discrimination of stable and unstable computationally designed proteins. Our results illustrate how megascale biophysical measurements can complement existing evolutionary and structural data to enable accurate absolute stability prediction for small domains.

biophysics↗

bioRxiv2026

Single-cell characterization and machine learning-based classification reveal the transcriptional identity and antigen-experienced features of peripheral CD4+CD8+ double-positive T cells

T cell mediated immunity depends on diverse subsets with distinct regulatory and cytotoxic roles. While CD4 and CD8 T cells have traditionally been viewed as separate lineages, double-positive T (DPT) cells coexpressing both markers have emerged as a rare subset with potential roles in immune modulation, cytotoxicity, and memory. Here, we integrated single-cell RNA sequencing, single-cell TCR sequencing, and supervised machine learning to characterize transcriptionally defined DPT cells in human peripheral blood. DPT cells formed a reproducible transcriptional state distinct from conventional single-positive T cells and exhibited gene expression programs associated with cytotoxic effector potential, antigen experience, and immune cell migration. Clonal repertoire analysis revealed enrichment of expanded clonotypes and sharing of identical TCR clonotypes with both CD4 and CD8 T cell populations, indicating shared antigen-driven clonal histories rather than a distinct developmental lineage. To enable robust identification of this rare state, we developed a machine learning classifier trained on sorted CD4 and CD8 T cells, which outperformed marker-based approaches. Application of this framework to public COVID-19 single-cell datasets demonstrated its generalizability under immune perturbation. Together, these findings establish DPT cells as a reproducible, antigen-experienced transcriptional state with cytotoxicity- and migration-associated programs, and provide a framework for systematic identification of rare T cell states, expanding our understanding of T cell diversity.

immunology↗

bioRxiv2025

SuSiE 2.0: improved methods and implementations for genetic fine-mapping and phenotype prediction

Sum of Single Effects regression (SuSiE) has become widely adopted for genetic fine-mapping, yet its original implementation faces architectural limitations that hinder extensibility and performance. We present SuSiE 2.0, featuring a modular redesign for extensibility, up to 5x speed improvements for summary statistics applications, and several useful extensions including SuSiE-ash, a new method that improves calibration when strong signals coexist with moderate effects. Simulations and real data benchmarks demonstrate performance across diverse genetic architectures, highlighting improved calibration of SuSiE-ash for fine-mapping under complex polygenic backgrounds with 1.5-3x FDR reduction while maintaining power, and revealing SuSiE-based methods as effective yet underappreciated tools for TWAS prediction.

bioinformatics↗

bioRxiv2025

Nanobodies targeting hnRNPA2/B1 and tau

Heterogeneous nuclear ribonucleoprotein A2/B1 (hnRNPA2/B1) is an RNA-binding protein that mislocalizes to the cytoplasm and forms stress-induced granules in tauopathy and multisystem proteinopathy. It also preferentially interacts with oligomeric tau and is required for tau-mediated neurodegeneration in a mouse model of Alzheimers disease. To study endogenous hnRNPA2/B1 and tau, we generated nanobodies that specifically recognize these proteins. We screened yeast surface display nanobody libraries using an avidity-enhanced screening strategy that enabled selection of binders against short peptide ligands. This led to isolation of anti-hnRNPA2/B1 and anti-tau nanobodies with defined epitopes. Directed evolution of the anti-hnRNPA2/B1 nanobody improved binding affinity by over 20-fold but caused cytoplasmic aggregation, demonstrating a tradeoff between affinity and intracellular behavior. Although the final nanobodies retained modest affinities, they showed robust intracellular colocalization with their targets. Furthermore, fusion to ubiquitin ligase adaptor domains significantly decreased hnRNPA2/B1 and tau levels. Collectively, these nanobodies provide valuable tools for studying hnRNPA2/B1 and tau dynamics in their native cellular context.

bioengineering↗

bioRxiv2025

Hit or Miss: Understanding Emergence and Absence of Homo-oligomeric Contacts in Protein Language Models

Many proteins function not as isolated molecules but as symmetrical assemblies of identical subunits, from ion channels that gate cellular signals to metabolic enzymes that catalyze lifes essential reactions. Here, we reveal that single-sequence protein language models (pLMs), trained solely on individual protein sequences, implicitly learn the interface contacts of homo-oligomeric assemblies. As models grow larger, their ability to predict inter-subunit contacts continues to improve, whereas the accuracy for single-chain predictions shows only minimal gains. The largest ESM2 model can accurately distinguish genuine biological interfaces from crystallographic artifacts. MSA Pairformer and ESM2-15B perform comparably when broad sets of homologs are used, but restricting alignments to closer evolutionary neighbors reveals a clear difference: MSA Pairformer reaches an interface contact recovery rate of 0.44 compared with 0.33 for ESM2-15B. We hypothesized that one contributing factor is how models implicitly cluster homologous proteins. Comparing evolutionary constraints extracted from pLMs, we find correlation between homologs decrease as model size increases, consistent with larger models partitioning families into finer-grained subclusters and better separating distinct oligomeric interfaces. When models fail to detect known interfaces, these discrepancies may reflect annotation errors, proteins that adopt multiple assembly conformations, or intrinsic model limitations. Overall, our findings show that statistical patterns learned by pLMs encode key aspects of homo-oligomeric assembly organization and provide a basis for understanding how such interfaces diversify across evolution, even though structure is conserved over large evolutionary distances, oligomeric assembly may not be.

biophysics↗

bioRxiv2025

10,239 whole genomes with multiomic and clinical health information as the Korean population multiomic reference dataset

We present Korea10K, the largest genomic dataset of the Korean population, comprising 10,239 high-coverage whole genomes (mean depth 30x) with matched multiomic profiles and phenotype data. Korea10K achieves complete and near-complete discovery of very rare and ultra-rare alleles, respectively, at 9,000 Korean genomes. This dataset provides the high-quality population-specific imputation panel, enabling accurate inference of low-frequency variants. Admixture analyses confirm the genetic homogeneity of the Korean population, despite its diverse Y-chromosomal, mitochondrial, and HLA repertoires. This pattern reflects a long and continuous lineage history characterized by persistent internal admixture and genomic homogenization over thousands of years on the Korean peninsula. We also identified 16.8 million genomic variants that directly modify CG sites by creating or abolishing CG dinucleotides, providing the population-scale evidence of coordinated genomic-epigenomic regulatory mechanism in Koreans.

genomics↗

bioRxiv2025

Quantitative Calibration of a Spatial QSP Model Identifies Fibroblast Impact on HCC Immunotherapy

AbstractComputational models are increasingly used to predict treatment response and optimize cancer therapy strategies. Among these, quantitative systems pharmacology (QSP) models mechanistically simulate tumor progression and pharmacological interventions, enabling virtual clinical trials, model-informed drug development, and biomarker identification. Coupling QSP with an agent-based model yields a spatial QSP (spQSP) platform that captures tissue-level spatial organization of the tumor microenvironment (TME). However, parameterizing such models to represent tumor biology remains an open problem. In this study, we developed a calibration framework using the Approximate Bayesian Computation - Sequential Monte Carlo (ABC-SMC) approach to calibrate the spQSP model with a combination of clinical and spatial molecular data, reflecting the TME characteristics of human tumors. This calibration framework matches tumor architectures between spQSP model predictions and patient spatial molecular data by fitting statistical summaries of cellular neighborhoods. We demonstrate that model calibration using CODEX data from untreated HCC patients enables prediction of TME spatial molecular states in an independent cohort receiving immune-checkpoint inhibitor (ICI) and tyrosine kinase inhibitor (TKI) combination therapy. Finally, we identify spatial and non-spatial pretreatment biomarkers and assess their predictive power for therapeutic response. This workflow demonstrates how integrating spatial-omics with multiscale mechanistic models enables quantitative calibration, biological insight, and in silico biomarker discovery, providing a framework for personalized cancer therapy across tumor types. SignificanceDigital twins and computational models are increasingly used to simulate disease and guide therapy, but they often struggle to capture the immense complexity driving the spatiotemporal evolution of the TME. The challenge is compounded by clinical sample limitations, which typically provide measurements from only static snapshots of the TME for parameter estimation. We demonstrate how mechanistic modeling frameworks can overcome this limitation by enabling inference of spatiotemporal model parameters from static spatial data - an intractable task for purely data-driven approaches. Ultimately, our work presents a workflow that integrates spatial-omics with multiscale mechanistic models, enabling quantitative calibration, deeper biological insight, and in silico biomarker discovery.

bioinformatics↗

bioRxiv2025

Protein Hunter: exploiting structure hallucination within diffusion for protein design

1Interactions between proteins and other biomolecules underlie nearly all biological processes, yet designing such interactions de novo remains challenging. Capturing their specific interactions and co-optimizing sequence and structure are difficult and often require extensive computation. We present Protein Hunter, a fast, fine-tuning-free framework for de novo protein design. Starting from an all-X sequence, we find diffusion-based structure prediction models hallucinate reasonable looking structures that can be further improved through iterative sequence re-design and structure re-prediction. This lightweight strategy achieves high AlphaFold3 in silico success rates across both unconditional and conditional generation tasks, including binders to proteins, cyclic peptides, small molecules, DNA, and RNA. Protein Hunter also supports multi-motif scaffolding and partial redesign, providing a general and efficient platform for de novo protein design across diverse molecular targets.

molecular biology↗

bioRxiv2025

A steady state pool of calcium-dependent actin is maintained by Homer and controls epithelial mechanosensation

Epithelial cells are inherently contractile and in homeostasis, tissue integrity is maintained by balancing the uneven contractile forces in neighboring cells at the cell-cell interface. By contrast, epithelial cells can utilize an imbalance in contractile force to communicate various information to induce tissue-wide response as in wound healing. Contractility is generated and processed at the apical junctional complex (AJC) by the dynamic behavior of the actin cytoskeleton. Calcium signaling can pattern cellular responses based on its reach and amplitude and the actin cytoskeleton is supported by its wide ranging effects on actin regulators. Calcium transients regulate various cell behaviors associated with actin remodeling, such as in damage response and developmental morphogenesis. Here we report that calcium maintains an adaptive pool of AJC-associated actin that is sensitive to tension and encoded by calcium dynamics. For this, the recently identified epithelial polarity module Homer-MUPP1/PatJ is required. Homer regulates calcium signaling in various tissue contexts through interaction with numerous components of the endoplasmic reticulum (ER) and plasma membrane (PM) calcium signal toolkit. Knockout of either Homer or MUPP1/PatJ attenuated tension-induced calcium response and severely disrupted wound healing migration, which is dependent on guidance input through AJC tension. We also show that Homer is integral to early embryonic neurodevelopment as its suppression causes failure of neural tube closure. Our findings highlight the critical role of localized calcium dynamics on AJC actin remodeling and cellular behavior, elucidating the means of tissue coordination through intercellular tension. Significance statementThis study uncovers a novel mechanism by which localized calcium dynamics at the apical junctional complex maintain an adaptive, tension sensitive pool of actin, regulated by the epithelial polarity scaffolds Homer and MUPP1/PatJ. Importantly, this mechanism operates without perturbing epithelial polarity, indicating a specific means to modulate tissue mechanics. By linking mechanical forces to localized calcium amplification, this module enables precise mechanosensation, coordinating collective behaviors such as epithelial wound healing and neural tube closure in Xenopus. These findings redefine our understanding of intercellular tension sensing in epithelial tissues and highlight the Homer-calcium signaling axis as a key driver of tissue morphogenesis and homeostasis, with far reaching implications for developmental biology, regenerative medicine, and neural tube defect pathogenesis.

cell biology↗