Biology subjects

Jung, N.

Publications and source records attributed to Jung, N..

12 recordsLinked to original sources

bioRxiv2026

Proteomic Characterization of Prostate Cancer Bone Metastasis Subtypes Defines Progressive Remodeling States of the Bone-Tumor Microenvironment

Prostate cancer (PC) bone metastases (PCBM) are a debilitating disease morbidity that primarily affects the axial skeleton. PCBM are predominantly osteosclerotic with mixed osteosclerotic/osteolytic features, including irregular bone deposition, loss of collagen alignment and increased porosity that mimic features of woven bone formed during development and repair. We hypothesize that progression of PCBM is driven by interactions between tumor cells and the bone microenvironment and is associated with changes in the stromal compartment. We performed tandem mass tag mass spectrometry proteomics on lumbar vertebral specimens from 32 cadaveric PCBM specimens and 3 age-matched controls. Unsupervised clustering segregated specimens into three clusters associated with benign, mixed, and osteosclerotic lesion states, characterized by distinct immune-, tumor-, and osteogenic-associated protein signatures. Histological analysis showed tumor infiltration was accompanied by marrow fibrosis and increased collagen III deposition. We identified a marker signature associated with osteosclerotic remodeling and mapped this signature to independent metastatic PC transcriptomic datasets. The proteomics-derived markers reproduced similar molecular groupings at RNA level, and robustly segregated bone PCBMs from soft tissue metastases PC in RNA data. Together, these findings define molecularly distinct states of tumor-driven changes in PCBM that extend beyond histological classification and may inform biomarker development and therapeutic stratification.

cancer biology↗

bioRxiv2026

Big-team science reveals that patterns of inhibitory control variation in teleost fishes differ from those in mammals and birds

Understanding the evolution of cognition is fundamental to understanding the origins and nature of our own cognitive abilities, with cross-species comparisons providing a cornerstone of this endeavor. Yet, comparative cognition so far has largely focused on endothermic vertebrates, particularly mammals and birds, while ectotherms--representing a substantial proportion of vertebrate diversity--remain relatively understudied. Moreover, variation in experimental methods makes existing data difficult to compare across species. Big-team science offers a way to overcome these limitations by enabling standardized cognitive testing across broad taxonomic scales. Here, we apply this approach to fishes, using inhibitory control as a proof of concept for standardized comparative testing in 444 individuals across 22 teleost species spanning 19 genera. We demonstrate that standardized cognitive testing across such diversity is feasible, while also revealing substantial heterogeneity in the sample. Performance varied markedly among species, but, strikingly, showed only a weak phylogenetic signal, contrasting with patterns previously reported in mammals and birds. In addition, we detected great within-species variation, including differences across research groups, suggesting that performance may reflect not only species-level evolutionary history but also ontogenetic experience and the capacity to adjust decision rules to local conditions. Together, our findings highlight the importance of both evolutionary and environmental sources of cognitive variation and demonstrate that extending standardized comparative approaches beyond traditionally studied endotherms can reveal a more dynamic and complex picture of cognitive evolution across the vertebrate lineage.

animal behavior and cognition↗

bioRxiv2026

The coumarin derivative X6632 is a pan-ID protein inhibitor that suppresses tumor growth by targeting cancer cells and the tumor-associated microvasculature

Inhibitor of DNA binding (ID) proteins are key regulators of tumor cell stemness, therapy resistance and pathological angiogenesis in multiple cancer types and other diseases. Here, we characterize the coumarin-derived compound X6632 as a pan-ID inhibitor with dual activity against tumor cells and the tumor-associated microvasculature in a number of human and murine models. X6632 efficiently suppressed ID protein expression, inhibited the proliferation, migration, invasion of melanoma cells, and impaired multiple endothelial cell functions, including proliferation, migration, invasion, tube formation and sprouting in vitro. In back-to-back comparisons, X6632 exhibited an approximately ten-fold higher efficacy compared to the first-generation ID antagonist AGX51. In vivo, X6632 potently reduced pathological (neo)vascularization in established angiogenesis models, including oxygen-induced retinopathy and in Matrigel plug assays. It also significantly decreased blood vessel density in syngeneic melanoma models, delayed tumor growth and, when combined with immune checkpoint blockade, achieved superior tumor control compared with either monotherapy. Moreover, X6632 inhibited clonogenic growth in several breast cancer models, and robustly suppressed the growth of triple negative breast cancer in vivo, both in the highly aggressive 4T1 syngeneic model and in patient-derived xenografts. Collectively, these data establish X6632 as a second-generation, pan-ID protein inhibitor that can simultaneously target malignant cells and the tumor-supporting vasculature, and support the further pre-clinical development of the compound for the treatment of melanoma, breast cancer and potentially additional ID-dependent malignancies, as well as diseases driven by pathological neoangiogenesis.

cancer biology↗

bioRxiv2026

G-screen: Scalable Receptor-Aware Virtual Screening through Flexible Ligand Alignment

Virtual screening has long been a central computational tool for rational ligand discovery, enabling the systematic prioritization of candidate molecules from large chemical libraries. Although docking and related approaches that explicitly account for protein-ligand interactions have been developed and refined over several decades, achieving both reliable protein-aware interaction modeling and computational scalability remains an open challenge, particularly for ultra-large chemical spaces. Ligand-based methods are fast and robust but do not explicitly incorporate protein structure, whereas docking-based approaches model protein-ligand interactions more directly at substantially higher computational cost. Here, we present G-screen, a freely available and scalable protein-aware virtual screening framework designed for cases in which an experimentally determined or predicted reference protein-ligand complex structure is available. Rather than performing computationally intensive full docking with explicit pose sampling and optimization, G-screen rapidly generates alignment-guided pose hypotheses using a flexible global alignment algorithm (G-align). The resulting aligned poses are subsequently evaluated using protein-aware pharmacophore interactions derived from the reference complex, enabling explicit atomic-level interaction analysis while retaining the scalability and robustness of ligand-based alignment methods. Benchmarking on DUD-E, LIT-PCBA, and MUV datasets demonstrates that G-screen achieves competitive discrimination and early enrichment relative to representative ligand-based and docking-based methods, while maintaining millisecond-scale per-molecule runtimes under multi-threaded execution. These results position G-screen as a practical and scalable intermediate strategy between conventional ligand-based virtual screening and computationally intensive docking workflows for efficiently filtering ultra-large chemical libraries when a reference complex structure is available. G-screen and G-align are freely available at https://github.com/seoklab/gscreen and https://github.com/seoklab/galign, respectively. Scientific ContributionWe have developed a scalable virtual screening framework for efficiently filtering ultra-large chemical libraries using a flexible global alignment algorithm combined with protein- aware pharmacophore evaluations and alignment-guided pose hypotheses. Despite explicitly capturing atomic-level interactions, the method remains highly efficient, maintaining millisecond-scale per-molecule runtimes under parallel execution. It achieves competitive discrimination and early enrichment, serving as an intermediate strategy between conventional ligand-based virtual screening and computationally intensive docking while combining the speed of ligand-based approaches with the structural context of traditional docking.

biophysics↗

bioRxiv2025

Distinct 2-phenyl-imidazo pyridine derivatives drive ER degradation and selectively impair proliferation of ER+ breast cancer cells via the aryl hydrocarbon receptor

X15695 is a 2-phenyl-imidazo[1, 2] pyridine derivative identified as an orally active, selective oestrogen receptor (ER) degrader that inhibits the proliferation of ER+ breast cancer cells. Here, we show that X15695 is an aryl hydrocarbon receptor (AHR) ligand that stabilises the AHR more efficiently than its classical ligand, indirubin. X15695 enables AHR to form a complex with the ER, promoting its proteasomal degradation. In the presence of oestradiol, X15695 outperforms the standard of care drug fulvestrant in suppressing the growth of ER+ breast cancer cells, either expressing the wild-type or clinically relevant ER mutant forms (Y537S and D538G), and of patient-derived xenograft organoids established from ER+ tumours. Using computational techniques, we discovered that a low pKa value resulting from electron-withdrawing substituents in the 2-phenyl-imidazo[1, 2] pyridine compounds is a key feature that identify them as potent AHR ligands, leading to the potential discovery of additional derivatives for future therapeutic development.

cancer biology↗

bioRxiv2025

Raman spectroscopy reveals growth phase-dependent molecular differences in bacterial membrane vesicles

Bacterial membrane vesicles (BMVs) have attracted significant attention as highly efficient transport vehicles for molecules crossing biological barriers and as key mediators in infection processes. As interest in BMVs increases, the need for standardized isolation protocols and comprehensive analytical approaches becomes apparent. This study introduces Raman spectroscopy as a novel, chemically selective monitoring approach for the analysis of subtle biochemical changes in BMVs across different bacterial growth phases. BMVs derived from Pseudomonas aeruginosa, a Gram-negative human pathogen responsible for severe nosocomial infections, were isolated at six different time points and analyzed via established physicochemical and functional assays, as well as Raman spectroscopy. While established analytics revealed growth phase-dependent variations in protein content, surface charge, and immunogenic effects on human immune cells, Raman spectroscopy enabled the comprehensive analysis of molecular-level changes between isolation time points. Significant shifts in protein-to-lipid ratios, higher lipid saturation, and changes in protein secondary structure were detected in BMVs isolated from later growth phases. Further, the absence of spectral markers for nucleic acids enabled the identification of BMVs as outer membrane vesicles. These findings emphasize the critical influence of the isolation time point on BMV properties and highlight Raman spectroscopy as a powerful tool for semi-quantitative chemical profiling, revealing minuscule yet biologically significant changes in BMVs depending on isolation time points.

cell biology↗

bioRxiv2025

TITANiAN: Robust Prediction of T-cell Epitope Immunogenicity using Adversarial Domain Adaptation Network

T-cell immunogenicity, the ability of peptide fragments to elicit T-cell responses, is a critical determinant of the safety and efficacy of protein therapeutics and vaccines. While deep learning shows promise for in silico prediction, the scarcity of comprehensive immunogenicity data is a major challenge. We present T-SCAPE, a novel multi-domain deep learning framework that leverages adversarial domain adaptation to integrate diverse immunologically relevant data sources, including MHC presentation, peptide-MHC binding affinity, TCR-pMHC interaction, source organism information, and T-cell activation. Validated through rigorous leakage-controlled benchmarks, T-SCAPE demonstrates exceptional performance in predicting T-cell activation for specific peptide-MHC pairs. Remarkably, it also accurately predicts the ADA-inducing potential of therapeutic antibodies without requiring MHC inputs. This success is attributed to T-SCAPEs biologically grounded and data-driven multi-domain pretraining. Its consistent and robust performance highlights its potential to advance the development of safer and more effective vaccines and protein therapeutics.

bioinformatics↗

bioRxiv2024

Predicting nanocarrier permeation across the human intestine in vitro: Model matters

For clinical translation of oral nanocarriers, simulation of the complex intestinal microenvironment is crucial to evaluate interactions and transport across the intestinal mucosa for predicting the drugs bioavailability. However, permeation studies are often conducted using simplistic cell culture models, overlooking key physiological factors such as tissue composition, morphology, and additional diffusion barriers as constituted by mucus. This oversight may potentially lead to an incomplete evaluation of the nanocarrier-tissue interactions and an overestimation of permeation. In this study, we systematically investigated different 3D tissue models of the human intestine under static cultivation and dynamic flow conditions with respect to tissue morphology, mucus production, and their impact on nanocarrier permeation. Our results revealed that the cell ratio between the different cell types (enterocytes and goblet cells), as well as the choice of culture conditions, had a notable impact on tissue layer thickness, mucus secretion, and barrier impairment, all of which were increased under dynamic flow conditions. Permeation studies with polymeric nanocarriers (PLGA and PEG-PLGA) elucidated that the amount of mucus present in the respective model was the limiting factor for the permeation of PLGA nanocarriers, while tissue topography represented the key factor influencing PEG-PLGA nanocarrier permeation. Furthermore, both nanocarriers exhibited diametrically opposite permeation kinetics in a direct comparison to soluble compounds. In summary, these findings reveal the critical role of the implemented test systems on permeation assessment and emphasize that, in the context of preclinical nanocarrier testing, the choice of in vitro model matters.

pharmacology and toxicology↗

bioRxiv2023

Sclerotic prostate cancer bone metastasis: woven bone lesions with a twist.

Bone metastasis (BM) are the most severe and prevalent consequences of prostate cancer (PC) affecting more than 80% of patients with advanced PC. PCBM generate pain, pathological fractures, and paralysis. As modern therapies increase survival, more patients are suffering from these catastrophic consequences of PCBM. Radiographically, PCBM are predominantly osteosclerotic, but the mechanisms of abnormal bone formation, and how this "more and new" bone is related to fractures is unclear. In this study, we conducted a comprehensive analysis on a cohort of 76 cadaveric PCBM samples and 12 from non-PC donors as control. We used -CT to determine three-dimensional organization and quantify bone characteristics, quantitative backscattering electron microscopy to characterize mineral content and details in bone structure, nano indentation to determine mechanical properties, and we finalize with histological and immunohistochemical analysis of bone structure and composition. We define 4 phenotypes of PCBM, osteolytic, mixed lytic-sclerotic, and two subgroups of osteosclerotic lesions, those with residual trabeculae, and others without residual trabeculae. The osteosclerotic lesions are characterized by the presence of abnormal bone within the trabeculae surfaces and intertrabecular spaces. This abnormal bone is characterized by higher lacunae density, abnormal lacunae morphology and orientation. However, we observed no significant difference between this irregular bone and residual trabeculae in terms of mineral content, hardness, and elastic modulus at micron-scale. The collagen matrix of this abnormal bone presents with irregular organization and is accompanied by increased proteoglycan and phosphorylated glycoprotein content. These characteristics suggests the presence of woven bone in PCBM. However, the lack of subsequent bone remodelling, absence of lamellar bone deposition on its surface, absence of markers of matrix vesicles but evidence of alkaline phosphatase dominated mineralization and collagen-III structure, set up differences from woven bone, while the role of PC cells in inducing this irregular bone phenotype remains unclear.

cancer biology↗

bioRxiv2022

Herd immunity on chip: recapitulating virus transmission in human society

Virus transmission is affected by population density, social distancing, and vaccination. This has been simulated only by mathematical models. Here, we report the first experimental model to mimic herd immunity to a human coronavirus using a microfluidic device filled with host cells. The device consists of 444 microchambers filled with susceptible (S0), infected (I0), and unsusceptible (U0) cells at specific ratios. The transmission rate and reproduction numbers were directly proportional to S0 and I0 and inversely proportional to U0. Herd immunity was achieved when the proportion of U0 was at 80% in a fixed number of uninfected (S0+U0) cells. These results were consistent with those from a mathematical model. The device can be used for predicting virus transmission. One-Sentence SummaryWe present the first experimental model enabling the simulation of herd immunity in a microfluidic device filled with host cells to human coronavirus.

microbiology↗

bioRxiv2021

Kinetics and correlates of the neutralizing antibody response to SARS-CoV-2

A detailed understanding of antibody-based SARS-CoV-2 immunity has critical implications for overcoming the COVID-19 pandemic and for informing on vaccination strategies. In this study, we evaluated the dynamics of the SARS-CoV-2 antibody response in a cohort of 963 recovered individuals over a period of 10 months. Investigating a total of 2,146 samples, we detected an initial SARS-CoV-2 antibody response in 94.4% of individuals, with 82% and 79% exhibiting serum and IgG neutralization, respectively. Approximately 3% of recovered patients demonstrated exceptional SARS-CoV-2 neutralizing activity, defining them as elite neutralizers. These individuals also possessed effective cross-neutralizing IgG antibodies to SARS-CoV-1 without any known prior exposure to this virus. By applying multivariate statistical modeling, we found that sero-reactivity, age, time since disease onset, and fever are key factors predicting SARS-CoV-2 neutralizing activity in mild courses of COVID-19. Investigating longevity of the antibody response, we detected loss of anti-spike reactivity in 13% of individuals 10 months after infection. Moreover, neutralizing activity had an initial half-life of 6.7 weeks in serum versus 30.8 weeks in purified IgG samples indicating the presence of a more stable and long-term memory IgG B cell repertoire in the majority of individuals recovered from COVID-19. Our results demonstrate a broad spectrum of the initial SARS-CoV-2 neutralizing antibody response depending on clinical characteristics, with antibodies being maintained in the majority of individuals for the first 10 months after mild course of COVID-19.

immunology↗

bioRxiv2020

The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation

Transcription factors (TFs) bind DNA sequence motif vocabularies in cis-regulatory elements (CREs) to modulate chromatin state and gene expression during cell state transitions. A quantitative understanding of how motif lexicons influence dynamic regulatory activity has been elusive due to the combinatorial nature of the cis-regulatory code. To address this, we undertook multi-omic data profiling of chromatin and expression dynamics across epidermal differentiation to identify 40,103 dynamic CREs associated with 3,609 dynamically expressed genes, then applied an interpretable deep learning framework to model the cis-regulatory logic of chromatin accessibility. This identified cooperative DNA sequence rules in dynamic CREs regulating synchronous gene modules with diverse roles in skin differentiation. Massively parallel reporter analysis validated temporal dynamics and cooperative cis-regulatory logic. Variants linked to human polygenic skin disease were enriched in these time-dependent combinatorial motif rules. This integrative approach reveals the combinatorial cis-regulatory lexicon of epidermal differentiation and represents a general framework for deciphering the organizational principles of the cis-regulatory code in dynamic gene regulation. HIGHLIGHTSO_LIAn integrative multi-omic resource profiling chromatin and expression dynamics across keratinocyte differentiation C_LIO_LIPredictive deep learning models of chromatin dynamics reveal a high-resolution cis-regulatory DNA motif lexicon of epidermal differentiation C_LIO_LIModel interpretation enables discovery of combinatorial cis-regulatory logic of homotypic and heterotypic motif combinations C_LIO_LIMassively parallel reporter experiments validate temporal dynamics and cis-regulatory logic of the combinatorial motif lexicon C_LI

genomics↗