Biology subjects

Jang, E.

Publications and source records attributed to Jang, E..

8 recordsLinked to original sources

bioRxiv2026

Therapeutic AIBP expression contributes to synaptic preservation and vision restoration against glaucomatous neuroinflammation

Lowering intraocular pressure (IOP) is the only approved treatment for glaucoma, yet retinal ganglion cell (RGC) degeneration and vision loss often progress despite reducing IOP. Apolipoprotein A-I binding protein (AIBP) stabilizes ATP-binding cassette subfamily A member 1 (ABCA1) and promotes cholesterol efflux, thus controlling cholesterol-rich lipid rafts and restraining lipid raft-dependent Toll-like receptor 4 (TLR4) inflammatory signaling. Here we show that AAV2-mediated expression of human AIBP (hAIBP) preserves inner retinal synaptic integrity and visual function in experimental glaucoma without altering IOP. In DBA/2J mice, a single intravitreal injection of AAV2-hAIBP at 5 months protected RGCs and preserved pattern electroretinogram and pattern visual evoked potential responses at 10 months, even though IOP was indistinguishable from empty-vector controls. AAV2-hAIBP preserved CaMKII and synapsin phosphorylation, restored synaptophysin and PSD95 in the inner plexiform layer, and reduced the expression of TLR4, complement C1q, IBA1, IL-1b, and IL-6 induction in Muller glia and microglia. Comparable protection was observed in microbead-induced ocular hypertension in C57BL/6 mice, both when the AAV2-hAIBP was delivered before microbead injection and when it was delivered after IOP elevation was established. Elevated IOP reduced endogenous retinal AIBP and ABCA1 expression, and AAV2-hAIBP restored both. Protection required the TLR4-binding domain, as an AIBP mutant lacking this domain failed to preserve RGCs. Conversely, AIBP knockout retinas exhibited increased CaMKII phosphorylation and reduced synaptophysin in the absence of elevated IOP. These findings identify synaptic preservation through the AIBP-TLR4 axis as an IOP-independent therapeutic mechanism and support AAV2-hAIBP as a candidate gene therapy for glaucoma.

neuroscience↗

bioRxiv2026

CellDF: Quality-controlled cell matching for whole-slide HE-IHC label transfer

A laboratory that stains immunohistochemistry (IHC) on a section adjacent to a hematoxylin and eosin (HE) section already holds the material to supervise HE models on its own cases. It goes unused because adjacent sections sample non-identical cells, and residual registration error prevents assigning IHC labels to individual HE cells. We present CellDF (Cell Displacement Field), which turns registered serial-section data into pairs of HE cells and the protein-expression labels measured on the adjacent section. CellDF estimates a locally adaptive residual displacement field through iterated kernel regression over each HE cell's K nearest IHC candidates; a sparse-kernel variant keeps it tractable at whole-slide cell counts, where pairwise matchers are not. The within-tile distribution of these displacements yields two ground-truth-free statistics, the directional scatter {sigma}{theta} and the between-tile angular deviation |{Delta}{theta}|, that localize matching quality more finely than landmark-based target registration error and drive a two-stage filter that withholds labels where matching is unreliable. On 54 same-section HyReCo pairs, {sigma}{theta} correlates only moderately with landmark error and flags localized restaining damage that global error misses; on 30 four-marker Acrobat serial-section cases, the same statistic identifies which IHC marker, if any, lies close enough to HE for cell-level transfer. As a proof of concept, transferred labels trained a cell classifier on HE embeddings that generalized to held-out cells within the sample (F1 0.85, AUROC 0.88). A laboratory can thereby generate cell-level protein-expression labels from its own sections and tune HE-only models on them, with each label set's reliability read from the data.

pathology↗

bioRxiv2026

Knowledge-Guided Learning with Curated Prior Genetic Biomarkers for Robust Model Interpretation

MotivationKnowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. ResultsIn this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasts. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementationThe open-source is publicly available at: https://github.com/datax-lab/BioExPL.

bioinformatics↗

bioRxiv2025

Prediction of TdP Arrhythmia Risk Through Molecular Simulations of Conformation-specific Drug Interactions with the hERG K+, NaV1.5, and CaV1.2 Channels

Unintended block of cardiac ion channels, particularly hERG (KV11.1), remains a key concern in drug development as disruption of ion channel function can lead to deadly arrhythmia. To assess proarrhythmic risk, we investigated how drugs interact with hERG in its open and inactivated states and whether drug interactions with other cardiac channels like NaV1.5 and CaV1.2 mitigate that risk. Using cryo-EM structures, we modeled open and inactivated conformations of these channels with Rosetta and AlphaFold. We then applied Site Identification by Ligand Competitive Saturation (SILCS), a physics-based pre-computed ensemble docking method, to predict drug binding affinities. SILCS leverages molecular simulation-generated free energy maps for high-throughput docking against hydrated lipid bilayer-embedded ion channel models. Bayesian machine learning was used to refine SILCS scoring using experimental IC50 values from 69 known hERG blockers outperforming Schrodinger Glide, AutoDock Vina, and OpenEye FRED drug docking predictions. Computed drug binding affinities for hERG and CaV1.2 channels were used to train machine learning models that successfully classified around 300 drugs from the CredibleMeds database. Cationic nitrogen SILCS fragment free energy scores were found to be top physical properties that are predictive of drug-induced Torsades de Pointes (TdP) arrhythmia risk. This approach, which relies on the predicted binding free energies and predicted physical properties of drugs rather than the chemical structure of the drugs themselves as features could be extended to facilitate the design of new drugs where rapid assessment of arrhythmia risk can be performed prior to experimental testing.

biophysics↗

bioRxiv2025

Empirical optimization of dual-sgRNA design for in vivo CRISPR/Cas9-mediated exon deletion in mice

CRISPR/Cas9 has transformed gene editing, enabling precise genetic modifications across species. However, existing sgRNA design prediction models based on in vitro data are difficult to generalize to in vivo contexts. In particular, approaches based on single-sgRNA design require additional filtering of in-frame mutations, which is inefficient in terms of both time and cost. In this study, we developed the first mammalian in vivo-trained prediction model to evaluate the efficiency of a dual-sgRNA-based exon deletion strategy. Using 230 editing outcomes of postnatal viable individuals, eight prediction models were constructed and evaluated based on generalized linear models and random forests. The final selected model, a Combined GLM, integrated the DeepSpCas9 score with k-mer sequence features, achieving an AUC of 0.759 (95% Confidence Interval: 0.697-0.821). Motif analysis revealed that CC sequences were associated with high efficiency and TT sequences were associated with low editing efficiency. This study demonstrates that integrating sequence-based features with existing design scores can improve sgRNA efficiency prediction in vivo. The proposed framework can be applied to the development of next-generation sgRNA design tools, with direct implications for gene therapy, effective animal model generation, and precision genome engineering. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/675005v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@12e6170org.highwire.dtl.DTLVardef@1ff2599org.highwire.dtl.DTLVardef@1fd61dborg.highwire.dtl.DTLVardef@23a426_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Low-intensity focused ultrasound enables temporal modulation of human midbrain organoid differentiation

Controlling the precise timing of biosignaling cues in complex 3D models such as organoids is critical for guiding cellular differentiation and functional maturation. Ultrasound stimulation, a next-generation neuromodulation modality, offers unique advantages due to its target specificity, ability to elicit mechanosensitive responses, and high spatial resolution. However, there is still a lack of precision stimulation platforms and comprehensive biomarker assays for selective control of organoid development. Here, we introduce a modular piezoelectric ultrasound stimulation platform that integrates seamlessly with conventional multi-well plates, enabling selective neuromodulation of midbrain organoids (mBOs). We demonstrate that ultrasound can specifically modulate cellular differentiation by promoting dopaminergic progenitor markers while delaying terminal differentiation. Importantly, ultrasound stimulation did not induce cellular damage, as confirmed by the absence of apoptosis and DNA damage markers. This work demonstrates the potential of focused ultrasound as a safe, non-invasive, and tunable biophysical cue for temporal regulation of organoid differentiation and maturation. Significance StatementOrganoids are three dimensional in vitro models derived from human tissue that recapitulate the complex features of organs. However, precise modulation of organoid differentiation relies on biochemical factors, which are limited in their temporal controllability. In this study, we demonstrate that low-intensity ultrasound stimulation enables temporal modulation of midbrain organoid differentiation using a modular multi-well stimulation platform. In particular, we show that ultrasound promotes the proliferation of dopaminergic progenitor cells. These findings suggest that ultrasound can serve as a supplementary mechanical cue to regulate midbrain organoid development.

developmental biology↗

bioRxiv2023

Darapladib, an inhibitor of Lp-PLA2, sensitizes cancer cells to ferroptosis by remodeling lipid metabolism

Arachidonic and adrenic acids in the membrane play key roles in ferroptosis, but how these fatty acids are manipulated in cells is largely unknown. Here, we reveal that lipoprotein-associated phospholipase A2 (Lp-PLA2) controls intracellular phospholipid metabolism and contributes to ferroptosis resistance. A metabolic drug screen identified that darapladib (SB-480848), an inhibitor of Lp-PLA2, synergistically induced ferroptosis with GPX4 inhibitors. Notably, darapladib was able to enhance ferroptosis under lipoprotein-deficient or serum-free conditions. Furthermore, Lp-PLA2 was located in the membrane and cytoplasm and suppressed ferroptosis, suggesting the critical role of intracellular Lp-PLA2. Lipidomic analysis showed that phosphatidylethanolamine (PE) species were generally enriched, while lysophosphatidylethanolamine (lysoPE) and free fatty acid levels were reduced, upon darapladib treatment. Finally, combination treatment with darapladib and PACMA31, a GPX4 inhibitor, efficiently inhibited tumor growth in a xenograft model. Our study suggests that inhibition of Lp-PLA2 is a potential therapeutic strategy to enhance ferroptosis in cancer treatment.

cancer biology↗

bioRxiv2021

Discrete-to-analog signal conversion in human pluripotent stem cells

During development, state transitions are coordinated through changes in the identity of molecular regulators in a cell state- and dose specific manner. The ability to rationally engineer such functions in human pluripotent stem cells (hPSC) will enable numerous applications in regenerative medicine. Herein we report the generation of synthetic gene circuits that can detect a discrete cell state, and upon state detection, produce fine-tuned effector proteins in a programmable manner. Effectively, these gene circuits convert a discrete (digital-like) cell state into an analog signal by merging AND-like logic integration of endogenous miRNAs (classifiers) with a miRNA-mediated output fine-tuning technology (miSFITs). Using an automated miRNA identification and model-guided circuit optimization approach, we were able to produce robust cell state specific and graded output production in undifferentiated hPSC. We further finely controlled the levels of endogenous BMP4 secretion, which allowed us to document the effect of endogenous factor secretion in comparison to exogenous factor addition on early tissue development using the hPSC-derived gastruloid system. Our work provides the first demonstration of a discrete-to-analog signal conversion circuit operating in living hPSC, and a platform for customized cell state-specific control of desired physiological factors, laying the foundation for programming cell compositions in hPSC-derived tissues and beyond. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/467377v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@150da71org.highwire.dtl.DTLVardef@135554borg.highwire.dtl.DTLVardef@72b5b3org.highwire.dtl.DTLVardef@f70f84_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗