Biology subjects

Lee, J.

Publications and source records attributed to Lee, J..

At least 19 recordsLinked to original sources

bioRxiv2026

ChAI-Net learns a regulatory code for chromatin co-accessibility

The regulation of chromatin accessibility is critical for cell function and identity. Advances in single-molecule long-read footprinting have enabled near-nucleotide-resolution readout of chromatin fiber organization, capturing both coordination of transcription factor (TF) binding and variation in nucleosome positioning previously obscured by bulk methods. Here we combine this technique with deep learning to study how protein-DNA interactions are coordinated within different types of cis-regulatory elements in mouse embryonic stem cells. First, we quantify the frequency with which pairs of positions are simultaneously accessible on individual molecules. Then, we train ChAI-Net, a neural network that predicts these locus-specific chromatin co-accessibility maps from the underlying DNA sequence. Through model interpretation and in silico experiments, we show that ChAI-Net learns unique combinations of sequence motifs underlying co-accessibility within enhancers, promoters, and CTCF-bound sites. Many of these motifs are missed when modeling average accessibility, suggesting a specific role in coordinated binding. ChAI-Net's regulatory grammar also highlights the importance of TF motif orientation, spacing, positioning, and co-occurrence in driving co-accessibility. Thus, our framework for extracting regulatory rules from footprinting data expands understanding of TF cooperativity, while laying the groundwork to elucidate how these rules vary across cellular contexts as single-molecule genomics is deployed at scale.

genomics↗

bioRxiv2026

Predicting conformational changes induced by biomolecular interactions

Biomolecular interactions trigger structural changes of protein complexes essential for cellular signaling, regulation, and drug action. Modern co-folding models can now generate detailed structures of multi-component complexes directly from sequence. However, recent evaluations reveal that they frequently fail to capture specific conformational states induced by a ligand or partner. Here, we show that explicitly modeling the structural transition from an unbound to a bound state, rather than generating the bound complex alone, better resolves these interaction-induced states. Our generative learning framework, K-Fold, transforms independently predicted unbound component priors into an assembled complex through an ultrafast diffusion bridge, operating entirely without multiple sequence alignments. By eliminating co-evolutionary dependencies, K-Fold accurately recovers non-coevolved interfaces in antibody-antigen and targeted-degrader complexes, as well as ligand-directed regulatory states in GPCRs and kinases. Furthermore, by coupling this diffusion bridge with diverse monomeric equilibrium ensembles, K-Fold provides a computationally tractable foundation for sampling complex multi-state landscapes without retraining. These results demonstrate that formulating molecular assembly as an explicit structural transition moves structure prediction beyond static geometries toward capturing biomolecular dynamics.

molecular biology↗

bioRxiv2026

Functional Validation of Known BEST1 Variants Using a Robust Halide-Sensitive YFP Quenching Assay

Bestrophinopathies comprise a heterogeneous group of inherited retinal degenerative diseases caused by mutations in the Bestrophin-1 (BEST1) gene. Although more than 250 pathogenic BEST1 variants have been reported, only a small subset has undergone functional interrogation. OPGx-BEST1 is an investigational gene therapy designed to restore BEST1 function through augmented wild-type BEST1 expression in retinal pigment epithelium (RPE) cells. Previous studies in animal models and patient-derived cellular systems have demonstrated that loss-of-function (LOF) BEST1 variants can be complemented with wild-type protein expression and are therefore amenable to gene augmentation strategies. In contrast, known gain-of-function (GOF) mutations appear to retain pathogenic activity despite augmentation with wild-type BEST1. Therefore, given the therapeutic relevance of LOF/GOF disease mechanisms in bestrophinopathies, a comprehensive genotype-phenotype characterization across the BEST1 mutational landscape is critical to inform clinical trial enrollment. Conventional approaches to profile BEST1 variants have relied upon patient-derived RPE models and electrophysiologic assessment of channel function. While these systems provide valuable translational insight, they lack the scalability, speed, and experimental flexibility needed for a systematic evaluation of many variants for patient selection. Here, we describe a functional screening platform that leverages an engineered halide-sensitive reporter cell line to quantitatively assess the activity of BEST1 mutant proteins.

genetics↗

bioRxiv2026

Muscarinic signaling sustains delay-period selectivity in prefrontal cortex by preserving thalamic drive

Short-term memory (STM) requires the temporary maintenance of task-relevant information in prefrontal cortex (PFC). Reciprocal interactions between the mediodorsal thalamus (MD) and PFC have been implicated in sustaining this activity, but how thalamofrontal communication is maintained despite the biophysical constraints of repetitive synaptic transmission remains unclear. Here, we show that MD--PFC synapses exhibit profound frequency-dependent short-term depression, causing repetitive thalamic input to progressively lose its ability to drive PFC neurons. During STM performance, acetylcholine (ACh) levels in PFC rose rapidly following cue presentation and remained elevated throughout the delay period. Muscarinic ACh receptor (mAChR) activation did not alleviate synaptic depression, but instead increased the intrinsic excitability of PFC neurons, thereby preserving their responsiveness to weakened thalamic input. Conversely, mAChR blockade impaired STM performance and progressively disrupted delay-period directional representations at both the single-neuron and population levels, while largely sparing initial encoding. These findings identify muscarinic signaling as a postsynaptic gain mechanism that compensates for declining thalamofrontal efficacy and sustains task-relevant prefrontal representations during STM.

neuroscience↗

bioRxiv2026

Nanoscale remodeling of mitochondrial architecture duringmtRNA surveillance failure

Mitochondria within a cell exhibit heterogeneity in their composition and metabolic functions, and how this specialization shapes the responses of individual organelles to stress is unclear. We applied fluorescence microscopy and in situ cryo-electron tomography to examine the composition of mitochondrial stress bodies and their relationship to inner mitochondrial membrane architecture in the context of a mitochondrial genome expression defect. We discovered that defective mitochondrial RNA processing prompted a loss of inner membrane domain identity, leading to the mislocalization of ATP synthase from cristae edges to the boundary membrane, perturbed cristae morphometrics, and complete cristae loss in a subset of organelles. Mitochondrial stress bodies were enriched with nucleic acid and protein components linked to oxidative pathways, while separate and distinct mitochondrial domains became enriched with oligomerized P5CS, a marker of reductive proline synthesis. We found that consolidation of mitochondrial RNA into stress bodies, as well as ATP synthase remodeling, were regulated independently from P5CS concentration into a subset of the mitochondria. We propose that stress compartmentalization enables independent tuning of the quality control of oxidative pathway components via spatial sequestration from the rest of the mitochondrial network.

cell biology↗

bioRxiv2026

Integrating Multisource Target Evidence with Machine-Learning Potency Prediction for Alzheimer's Disease Drug Discovery

Alzheimer's disease (AD) target evidence is distributed across biological, chemical, structural, regulatory, and trial-registry sources, complicating reproducible target evaluation and modeling. We developed a traceable data-to-model platform that integrates AGORA-centered evidence through Target Explorer, Drug-Target Lookup, and Model Portfolio workspaces. The resource contains 967 targets, 452,249 compounds, and 1,430,260 measurements. On a curated PIK3CA dataset, target-specific fine-tuning of the Boltz-2 affinity head increased Pearson r from 0.248 to 0.560 and reduced mean absolute error from 0.900 to 0.706 on 469 held-out compounds. Separately, we screened 42 regression models over 50 random splits for ten moderate-sized target datasets; a random forest model for XDH achieved the highest mean R^2 (0.815). The platform connects curated evidence, model-ready datasets, reproducible evaluation, and public results while clearly separating contextual evidence from therapeutic claims and random-split performance from generalization to unseen chemical scaffolds.

bioinformatics↗

bioRxiv2026

Perceived social value is more strongly linked to gossip decisions with age in adolescence

Gossip is central to social exchange, yet how adolescents select what to share and with whom remains unclear. Across two independent samples aged 10-18 years (behavioural, n = 98; fMRI, n = 101 scanned), adolescents read information about absent third parties, chose whether and with whom to share it, and reported their appraisals. Sharing frequency showed no detectable age association. Valuation-to-decision coupling strengthened with age for interest in both samples, with a smaller age interaction for usefulness. Adolescents selected close friends more often with age when sharing. During scenario reading, before participants reported their choices, BOLD responses in a left inferior frontal cluster tracked subsequent interest ratings more strongly with age (n = 92), also within regions engaged by a separate mental-state judgment task. Together, these findings link age in adolescence to stronger valuation-to-decision coupling for interest and a greater proportion of sharing directed to close friends.

neuroscience↗

bioRxiv2026

Limits of marker information transfer]{Limits of marker information transfer in cross-panel cytometry imputation

Marker imputation can bridge cytometry datasets acquired with different panels and platforms. Its validity depends on whether common markers retain information about unmeasured target markers and whether those relationships transfer across assay contexts. We evaluated these limits in two paired flow cytometry-CyTOF cohorts comprising 134 patients and 257 specimens. A patient-held-out framework distinguished within-platform same-cell reconstruction from cross-platform distribution translation without cell correspondence. We compared conventional machine-learning and deep generative estimators with median imputation and used permutation controls to estimate the contributions of patient matching and source-exclusive markers. Evaluation covered marker values, specimen distributions, phenotype utility, and directional predictive relationships among markers. Within platforms, exact k-nearest neighbors reduced mean absolute error by 12.8-35.0% relative to median imputation. However, the median-based reconstructions were contracted, with a median ratio of predicted to observed interquartile range below 1 in every context and evaluable population. Patient matching improved cross-platform distribution recovery, whereas adding source-exclusive markers or higher-capacity estimators did not consistently produce net gains. In the independently annotated acute myeloid leukemia (AML) cohort, reconstructed markers largely preserved broad-lineage classification but did not recover fine-phenotype information available through direct measurement. In AML-CyTOF, the measured full panel outperformed the reconstructed representation by 10.25 percentage points for fine phenotypes. Predictive relationships among markers were context-specific. Most targets were better explained by several markers than by one. Marker selection based on these relationships showed a trade-off between panel size and pairwise coverage. Paired samples and imputation can therefore connect selected marker distributions across panels but should not be treated as substitutes for direct measurement. Cross-panel cytometry should be evaluated with patient-held-out tests matched to the resolution of available ground truth and the intended downstream use.

bioinformatics↗

bioRxiv2026

Decomposing scanner batch effects and their impact on pathology foundation models

Pathology foundation models (PFMs) are expected to represent the same tissue in the same way on any scanner, but their embeddings still carry scanner signals. Scanner-related image differences and the scanner sensitivity of PFMs have each been described. How tissue-dependent changes in color and spatial frequency relate to shifts in PFM representations, and what this means for correction, is less understood. We imaged 103 slides from 37 normal tissue types on six scanners (PanNormal; 95,780 matched locations) and used the real paired images as targets. We used the public PLISM dataset for external validation. First, we decomposed scanner differences into image properties. Color and spatial frequency changed together, by amounts that depended on tissue type. Second, corrections that matched these measured properties brought images closer to the target more reliably than representations. Pix2Pix reduced the image residual by 69% but increased UNI distance to the target by 27%. The same frequency matching helped one scanner and hurt another. Probes still distinguished corrected from real target embeddings after every correction. Third, changing one frequency band at a time showed that PFMs differ in how strongly each band moves them and whether it moves them toward the target. Across eight PFMs pretrained on whole-slide image tiles, the models that moved least under these small changes were the most robust to real scanner differences (Spearman {rho}=-0.90). Aligning images and aligning representations are therefore different goals. The gap between them depends on scanner, tissue, and model.

bioinformatics↗

bioRxiv2026

Programming Targeted Immune Cell Migration Using Orthogonal Chemokine-Receptor Pairs

Chemokines orchestrate the migration of immune cells throughout the body, but harnessing chemokine signaling to engineer targeted cell trafficking is challenging because of their overlapping interactions and broad expression patterns. Here we used structure-guided protein design to engineer orthogonal chemokine-receptor pairs with minimal crosstalk to native systems. T cells engineered with an ortho-receptor selectively infiltrated tissues producing the ortho-chemokine, in vitro and in vivo. By engineering T cells that both sense and secrete the ortho-chemokine, we generated collective swarming behavior in which pioneer cells attract follower cells via an ortho-chemokine gradient self-organized by positive feedback. By providing orthogonal genetic control over both the generation and sensing of chemokine gradients, this system enables the construction of autonomous spatial targeting programs in engineered cells.

synthetic biology↗

bioRxiv2026

Cross-state stability and maturational similarity of the social brain connectome in children and adolescents

The intra-individual stability of functional connectivity (FC) across two or more scans and the inter-individual similarity of FC patterns relative to peers capture developmental variability in functional brain organization. Despite their different referents, these measures are strongly coupled at the whole brain level; however, it remains unclear whether this relationship emerges in development, whether it extends to the social brain, and whether it differs by sex. Based on the importance of social development in this age range, we hypothesized that known developmental relationships and sex-specific effects would be more pronounced in the social brain relative to the whole brain. Using a large psychiatrically enriched sample of 769 youth ages 6-21 (female N = 327, mean age = 12.27 years), we computed FC during rest and movie-watching scans and quantified stability across scans as well as similarity to mature subjects (n=36) within each condition. We then tested whether sex-dependent age relationships were captured by these measures or by the strength of their coupling. Our results showed that cross-state stability and maturational similarity are strongly coupled in development, and that this coupling is enhanced in the social brain relative to the whole brain. Furthermore, the strength of stability-similarity coupling increased as a function of age in females, while males showed the opposite effect. When assessing relationships between age and each FC measure independently, no individual effects were significant after FDR correction. However, our multivariate age-prediction models fared much better, demonstrating that edgewise stability and maturational similarity measures can capture spatially heterogeneous developmental effects that are lost when collapsing across edges. Together, these results support the emerging idea that the degree of FC stability across scans is related to how closely an individuals functional architecture aligns to a shared template. Here, we newly show that this stability-similarity coupling is stronger in the social brain and varies with age in sex-specific ways. Future longitudinal work is needed to establish whether these cross-sectional relationships also characterize within-person developmental change.

neuroscience↗

bioRxiv2026

AMMPER-2: A spatially explicit agent-based model of microbial radiobiology with redox dye simulation

To reduce health risks for human space exploration, it is important to model the effects of deep-space radiation on biological systems. The budding yeast Saccharomyces cerevisiae is a common model organism in space radiobiology, but little is known about how radiation damage to individual cells translates into the population-level effects that experiments measure. The Agent-Based Model for Microbial Populations Exposed to Radiation (AMMPER) is a spatially explicit agent-based model that simulates the effects of ionizing radiation on S. cerevisiae cells and produces population-level results. Its goal is to improve experiment design and data interpretation. AMMPER-1 demonstrated that agent-based simulations can qualitatively reproduce the effects of proton radiation on the growth rate of wild-type and DNA-repair mutant yeast. Here, we present AMMPER-2, which addresses limitations of the first model and expands its capabilities. AMMPER-2 incorporates the dynamics of the redox dye alamarBlue, a data type commonly generated in spaceflight microbiology experiments, and models the fate of reactive oxygen species in more detail. It also adds an improved graphical interface and runs faster. AMMPER-2 remains easy to execute in Python on a standard personal laptop, providing space biologists a tool to explore experimental conditions, generate hypotheses, and interpret data from microbial space radiation experiments.

microbiology↗

bioRxiv2026

Synthesis-aware generative design in trillion-scale chemical spaces for automated drug discovery

Autonomous drug discovery via generative molecular design is critically bottlenecked by the production of chemically intractable structures. Multi-trillion-scale make-on-demand libraries guarantee synthetic feasibility, but conventional virtual screening cannot efficiently navigate these vast spaces or address multi-parameter optimization. We introduce Hyper Screening X, a generative AI framework integrating structure-based design with automated synthesis hardware. By encoding deterministic reaction logic in a generative flow network, the framework evaluates only 10 million candidates to navigate an 11-trillion-compound space, jointly optimizing physicochemical properties while ensuring compatibility with automated synthesis. Prospective validation against an SLC1A5 variant with a cryptic interface yielded a 96% synthesis success rate and a 50% functional hit rate, identifying two first-in-class lead compounds. This synthesis-aware strategy advances autonomous, closed-loop drug discovery.

bioinformatics↗

bioRxiv2026

Flexible 3D multimodal organoid interface reveals functional subnetworks and coordinated circuit activity in cortical organoids

The balance between excitatory and inhibitory (E/I) neurons is fundamental to cortical function, and its disruption underlies a wide range of neurological disorders, including epilepsy, Alzheimers disease and autism spectrum disorder. Cortical organoids offer a promising model to study this balance, as they recapitulate key features of brain development, including the emergence of E/I circuitry. However, no existing technology can simultaneously resolve neuronal identity, spatial organization, and dynamic network activity within intact organoids, limiting precise interrogation of E/I circuit function. Here, we report INFORM, an INtegrated Flexible Organoid Recording Multimodal neural interface that conformally wraps organoids of any size and shape, enabling long-term recordings over 371 days with above 90% of channels detecting spikes. INFORM interface combines four distinct modalities; electrophysiology, two-photon calcium imaging, targeted electrical microstimulation, and chemical intervention to monitor and modulate neural activity across multiple spatial and temporal scales spanning from action potentials from single neurons to synchronized bursts from large-scale populations. Such high-resolution interrogation enables probing of emergence of functional connectivity in fusing assembloids and resolves spatially discrete functional subnetworks that can be selectively recruited and modulated by INFORM. Neurotransmission blockade confirms that stimulus-evoked suppression of neuronal spiking is mediated by GABAergic inputs providing direct functional evidence of the presence of inhibitory signaling in organoids. This work allows single-cell dissection of functional networks in brain organoids, providing a powerful platform for modelling and studying cortical development, neurological diseases, and the E/I imbalances underlying circuit dysfunction.

neuroscience↗

bioRxiv2026

CD300a Blockade attenuates renal fibrosis by enhancing macrophage efferocytosis and neutrophil MMP9 production

Renal fibrosis represents an irreversible endpoint of chronic kidney disease. Although macrophage efferocytosis regulates the response to acute renal injury, the mechanisms by which early efferocytosis confers long-term protection against chronic fibrosis remain elusive. Here, we show that phagocyte-specific deletion of the inhibitory receptor CD300a (Cd300afl/flLyz2-Cre) mitigates obstructive renal injury and subsequent fibrosis. CD300a deficiency enhanced efferocytosis by tissue-resident macrophages during the early phase of injury, thereby suppressing the release of damage-associated molecular patterns (DAMPs) and TGF-{beta} expression. This early attenuation of the inflammatory microenvironment altered the composition of recruited neutrophils, shifting them from profibrotic SiglecF+ toward non-profibrotic SiglecF- subsets. Furthermore, loss of CD300a cell-autonomously primed neutrophils for enhanced matrix metalloproteinase-9 (MMP9) production upon injury, whereas neutrophil depletion abolished the protective effect of CD300a deficiency. Importantly, administration of a neutralizing anti-CD300a antibody to wild-type mice recapitulated these protective effects, promoting the shift toward SiglecF- neutrophils and significantly attenuating renal fibrosis. Together, our findings identify CD300a as a regulator of early macrophage efferocytosis and neutrophil-derived MMP9 production, and establish pharmacological CD300a blockade as a potential therapeutic strategy for progressive renal fibrosis.

immunology↗

bioRxiv2026

Loss of DNA Demethylase Enhances Arsenic Resistance Via Reducing of PIN2 Antisense Long Noncoding RNA

DNA methylation dynamics regulate diverse plant stress responses, but the mechanisms governing arsenic (As) tolerance through epigenetic pathways remain largely unknown. Here we report that Arabidopsis mutants lacking the DNA demethylase, REPRESSOR OF SILENCING (ROS1), show substantially enhanced As resistance. Integrating transcriptome and methylome data, we found that PIN-FORMED 2 (PIN2), encoding an auxin efflux carrier that also transports As out of cells, represents a key ROS1 target. In ros1, we observed increased methylation in the last exon of PIN2, a region functioning as the promoter for an antisense long non-coding RNA (lncRNA). Hypermethylation of the promoter suppresses the antisense lncRNA, which follows reduction of sense PIN2 transcription. As a result, PIN2 expression increases in ros1 mutants during As exposure, enhancing As tolerance. Analysis of ros1 pin2 double mutants confirmed that PIN2 acts downstream of ROS1 in As tolerance. Our study demonstrates an epigenetic pathway in which ROS1 regulates As tolerance by controlling PIN2 expression through antisense lncRNA-mediated transcriptional interference, offering new epigenetic strategies using plants in heavy metal contaminated environments.

plant biology↗

bioRxiv2026

Surfactant-Assisted Colorimetric Signal Enhancement in Paper-Based Glucose Sensing

Paper-based colorimetric sensors offer a low-cost and accessible platform for point-of-care (POC) analysis, but enzyme activity loss during coating and drying can weaken analytical signals and require high enzyme loadings or complex immobilization procedures. Although surfactants are widely used to improve wettability in paper-based assays, their potential contribution to colorimetric performance beyond these effects remains unclear. Here, we investigated surfactant-assisted colorimetric signal enhancement in a glucose assay implemented on a 96-puddle paper plate (96-PPP) and identified Tween 20 as the most effective surfactant. Its effect on detection performance became more pronounced as glucose oxidase (GOx) loading decreased; at 0.1 mg/mL GOx, Tween 20 lowered the limit of detection (LoD) from 0.113 to 0.034 mg/mL (approximately 3.3-fold) over a working range of 0-5 mg/mL, despite no statistically significant change in the measured contact angle at this loading. Tween 20 had no appreciable effect on the reaction in solution but preserved 95% of the apparent reaction rate constant after drying, compared with 11% without it, and atomic force microscopy (AFM) revealed a more dispersed dried enzyme morphology on mica. Tween 20-containing sensors also showed slower signal decay during repeated wetting-drying cycles and thermal stress, retained 77% (vs 26%) of the response at 400 mM NaCl, and exhibited within-PPP and between-batch coefficients of variation (CVs) below 10% (vs 12.3-19.5%), while maintaining glucose selectivity over potentially interfering molecules. These results indicate that Tween 20 enhances paper-based glucose sensing beyond wettability, in part by retaining enzyme cascade activity during drying, although the contributions of the individual enzymes and the underlying mechanism remain to be established.

bioengineering↗

bioRxiv2026

A climate-resilient microalga, Desmodesmus ecotolerans sp. nov., for biophotovoltaic applications

Microalgae are promising biological platforms for sustainable energy production because they can convert photosynthetic activity into electricity in biophotovoltaic (BPV) systems. Identifying strains that grow well at elevated temperatures and under reduced irradiance is an important step toward BPV applications in seasonally variable environments. Microalgae were isolated from Seondong Lake (Seoul, Republic of Korea) during three sampling periods and screened for growth at different temperatures. Among 45 isolates, KHUC-11 showed the highest weighted growth index based on growth rates at 25, 30, and 35 under dark conditions and was selected for further characterization. Phylogenetic reconstruction of the 45S rRNA gene cluster, internal transcribed spacer 2 (ITS2) secondary-structure comparison, and morphological characterization supported the description of KHUC-11 as Desmodesmus ecotolerans sp. nov. D. ecotolerans produced more biomass than Chlamydomonas reinhardtii CC-125 under both moderate- and low-light conditions. Under low light, its final dry biomass concentrations were 1.81 and 1.58 times those of CC-125 under mixotrophic and photoautotrophic conditions, respectively. Despite its smaller cell size, D. ecotolerans showed higher photosynthetic performance than CC-125, and screen-printed electrode (SPE)-based electrochemical assays demonstrated enhanced anodic current generation and CV-derived estimated power output. Taken together, these findings identify D. ecotolerans as a novel climate-resilient Desmodesmus species with bioelectrochemical properties that support further evaluation for BPV applications.

microbiology↗