Biology subjects

Chae, J.

Publications and source records attributed to Chae, J..

13 recordsLinked to original sources

bioRxiv2026

Generation of Human Taste Bud Organoids as a Human-Mimetic Platform for Modeling Taste Perception

We established a human taste bud organoid system derived from circumvallate papillae. This model has been highly anticipated in the field of taste research, where feasible approaches for validating taste biology discovered in rodent models have been limited. Through a stepwise exploratory strategy, we systematically identified and optimized the niche factors required to maintain taste bud organoids and promote their differentiation. This human taste bud organoid system comprises Type I-IV taste receptor cells (TRCs) as well as stem/progenitor cells, and its sensory receptor cells exhibit calcium responses to taste stimuli. Using this system, we identified robust Wnt signaling as a requirement for optimal TRC fate progression, uncovered a human-specific transcriptional program in LGR5 cells, and identified previously unrecognized molecular markers for Type I TRCs. By recapitulating native human taste bud cell diversity and function, this organoid provides a tractable platform for studying human taste biology and dysfunction.

developmental biology↗

bioRxiv2026

Detecting Manuscripts Related to Computable Phenotypes Using a Transformer-based Language Model

ObjectiveThe demand for a comprehensive phenomics library, which requires identifying computable phenotype definitions and associated metadata from an ever-expanding biomedical literature, presents a significant, labor-intensive, and unscalable challenge. To address this, we introduce a transformer-based language model specifically designed for identifying biomedical texts containing computable phenotypes and piloted its use in the Centralized Interactive Phenomics Resource (CIPHER) platform. Materials and MethodsWe fine-tuned a BioBERT model using a labeled dataset of 396 manuscripts. The model incorporates our novel sliding-window approach to effectively overcome token-length limitations, thereby enabling accurate classification of full-length manuscripts. For scalable deployment and continuous refinement, we developed a cohesive framework that integrates a web-based user interface, a control server, and a classification module. ResultsThe staged approach for model development yielded a final model with 95% accuracy. The web-based user interface was deployed on the CIPHER platform and enables user feedback for model retraining. DiscussionWe developed a model and user interface which are currently in use by data curators to identify computable phenotype definitions from the literature. ConclusionThrough this system, users can submit literature, assess classification results, and provide feedback directly influencing future model training, thereby offering an efficient and adaptive solution for accelerating phenotype-driven literature curation.

bioinformatics↗

bioRxiv2025

WAD: a wavelet-based linear programming method using L1-minimal reconstruction loss for accessible chromatin data deconvolution

Bulk tissue-based accessible chromatin studies provide summary annotations across all cell types within the tissue. These annotations can be skewed by varying proportions of individual cell types, especially in the context of disease studies. Estimated sample specific cell-type proportions can be used to mitigate effects of this variability while also addressing whether there exist significant alterations in cell proportions under certain conditions, like disease. We present WAD (Wavelet-based Accessible chromatin Deconvolution), a principled framework for robust estimation of cell type composition of bulk accessible chromatin data such as from the ATAC-seq assay. To determine informative reference cell profiles from single-cell accessible chromatin studies, WAD leverages wavelet-based denoising to suppress stochastic noise while preserving local chromatin continuity. Cell type proportion inference is reformulated as an L1-minimal linear programming problem, enabling scalable and interpretable solutions. Across 700 in silico pseudo-bulk mixtures generated from single-cell data, WAD achieved a consistently lower mean absolute error (MAE) and higher concordance (r > 0.85) than existing machine learning-based methods. These results demonstrate that wavelet-based feature extraction provides a biologically grounded and computationally efficient approach to chromatin signal deconvolution. A complete implementation of WAD is available at https://github.com/chae-jh/WAD.

bioinformatics↗

bioRxiv2025

STRUMP-I: Structure-based machine learning approach to pMHC-I binding prediction using force field energy features

The adaptive immune system monitors cellular integrity by recognizing short peptides from intracellular proteins presented on Major Histocompatibility Complex class I (MHC-I) molecules, collectively termed peptide-MHC complexes (pMHC), enabling detection of foreign or mutated proteins. With the rising importance of immunotherapies targeting neoantigens in cancers, the ability to accurately predict which peptides will bind to the diverse population of MHC alleles is critically important. Current computational methods for pMHC-I prediction fall broadly into sequence-based methods, which rely heavily on large training datasets, and structure-based methods that leverage structural modeling and energetics of pMHC binding. While sequence-based methods have been popularly used, their performance is dependent on the size and quality of training data. On the other hands, while structure-based approaches can generalize better across diverse MHC alleles, they traditionally depend on identifying a single global minimum energy conformation, an assumption that often fails due to the inherent binding promiscuity of MHC-I molecules. To address these limitations, we developed a STRUMP-I (STRUcture-based pMHC Prediction (for class I)), a novel pMHC binding prediction tool that directly leverages a broad set of force-field-derived energy terms as machine-learning features. STRUMP-I achieves performance comparable to state-of-the-art sequence-based models while significantly outperforming them on MHC alleles with limited representation in training data. Furthermore, STRUMP-I demonstrates strong synergy when integrated with sequence-based methods, notably enhancing prediction precision. The robustness and generalizability of STRUMP-I were confirmed by evaluating its predictive performance on independent, previously unseen datasets, including an experimentally validated cancer neoantigen dataset. This combined approach advances our capability to reliably identify clinically relevant neoantigen targets. The source code and trained models are available at https://github.com/yoonjoolab/STRUMP-I

bioinformatics↗

bioRxiv2025

InferPloidy: A fast ploidy inference tool accurately classifies cells with abnormal CNVs in large single-cell RNA-seq datasets

BackgroundAccurate inference of copy number variation (CNV) and ploidy from single-cell RNA-seq data is essential for resolving tumor heterogeneity and identifying malignant cells, yet existing tools such as CopyKat and SCEVAN are limited by long runtimes and reduced accuracy in large or heterogeneous datasets. ResultsHere, we present InferPloidy, a high-speed and robust ploidy inference method built on InferCNV that combines graph-based cell-grouping with iterative Gaussian mixture modeling. Across multiple cancer types--breast cancer, non-small cell lung cancer, pancreatic ductal adenocarcinoma, and colorectal cancer--InferPloidy achieved up to two orders of magnitude faster runtimes than existing tools, while maintaining superior classification accuracy. This accurate separation of aneuploid tumor cells enabled the discovery of subtype-specific therapeutic targets, including ERBB2, ESR1, EGFR, and MET, as well as recurrent surfaceome markers such as CD82, F11R, SLC2A1, TM9SF2, CXADR, and PLPP2, several of which have preclinical or clinical relevance. ConclusionThese results establish InferPloidy as a scalable platform for CNV-guided tumor cell identification and surfaceome-based biomarker discovery, offering broad utility for precision oncology and translational research.

bioinformatics↗

bioRxiv2025

Long-term innovative potential of genetic research and its suppression

Long-term innovation depends not only on discovering new elements but also on facilitating new recombination at the time of their co-availability. This notion of combinatorial innovation raises the question about whether scientific exploration and industry research persist over similar time horizons to meet innovative opportunities. Here, we analyze about 20 million papers and US, Chinese, and European patents to compare the courses of scientific and industrial activities in genetic research, a domain of far-reaching societal importance. We observe that research on new genes has declined since the early 2000s, but the exploration of novel gene combinations remains active for biotechnology innovation. Yet, fields of highly practical or commercial focus are less likely to test novel gene combinations. Furthermore, industry R&D on each gene tends to decline earlier than scientific interest in that gene. After this decline of the industrial momentum, continuous scientific research still sustains exploratory and innovative opportunities, involving novel gene combinations, clinical trials, and technologically-impactful work. Based on a hypothetical scenario of over-aligned science and industry, we estimate that up to 42.2-74.4% of the combinational opportunities would be unexplored if scientific research becomes hindered due to industrys retreat. This study suggests the potential complementary roles of scientific and industrial research with different temporal modes in innovation cycles, calling for balanced policies to foster innovation ecosystems.

scientific communication and education↗

bioRxiv2023

Serum albumin maintains Wnt water-solubility and activity

Wnt proteins regulate adult tissue homeostasis and repair by driving stem cell self-renewal and differentiation. High-performance Wnt preparations have enormous therapeutic potential, especially alongside various stem cell technologies. Currently, most of these Wnt preparations contain FBS or the detergent CHAPS to maintain Wnt solubility and activity. Recently, afamin was identified as a serum factor that solubilizes Wnt3a in conditioned media (CM), obviating the requirement for animal sera. Here, we report serum albumin (SA) is required for afamin-mediated solubilization of Wnt3a in CM. Moreover, SA-mediated solubilization of purified Wnt3a in tubes does not require afamin. This means conventional CHAPS-Wnt3a preparations can be modified into SA-purified Wnt3a (SA-pWnt3a) preparations by exchanging CHAPS for SA through dialysis. SA-pWnt3a preparations effectively promote the growth of human stem cell organoids. These data suggest SA as a physiological factor for maintaining Wnt3a activity in therapeutic applications.

biochemistry↗

bioRxiv2022

Deciphering the Evolutionary History of Complex Rearrangements in Head and Neck Cancer Patients Using Multi-Omic Approach

Despite the large efforts in international cancer genome consortium studies, there are still a large proportion of tumors with complex genomic rearrangement often remained without a clinically relevant molecular characterization. Integration of multi-omic data helps elucidating evolutionary history of such cases and identifying predictive molecular markers. Here we present the findings of our proof-of-principle study that investigated the evolutionary history of complex rearrangements in primary head and neck tumor genomes integrating long-read whole-genome, Hi-C, and RNA sequencing. We report a HPV-positive case with development of complex genomic rearrangements tracing back to HPV-mediated genomic instability and a HPV-negative case with an enhancer hi-jacking in a region of chromothripsis predicted to co-occur with a neoloop and a super-enhancer. These structural alterations resulted in overexpression of the oncogenes CCND1 and ALK, respectively, validated with immunohistochemistry assay. Furthermore, we introduce a novel analytic approach utilizing long-read whole-genome data distinguishing somatic mutations before and after structural variants. Our findings highlight the need for multi-modal sequencing strategies to increase our understanding of cancer evolution and rare biomarkers in poorly understood cancers.

genomics↗

bioRxiv2022

Revised Michaelis-Menten rate law with time-varying molecular concentrations

The Michaelis-Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry, biophysics, cell biology, systems biology, and chemical engineering. The MM rate law and its remedied form stand on the assumption that the concentration of the complex of interacting molecules, at each moment, approaches an equilibrium (quasi-steady state) much faster than the molecular concentrations change. Yet, this assumption is not always justified. Here, we relax this quasi-steady state requirement and propose the generalized MM rate law for the interactions of molecules with active concentration changes over time. Our approach for time-varying molecular concentrations, termed the effective time-delay scheme (ETS), is based on rigorously estimated time-delay effects in molecular complex formation. With particularly marked improvements in protein-protein and protein-DNA interaction modeling, the ETS provides an analytical framework to interpret and predict rich transient or rhythmic dynamics (such as autogenously-regulated cellular adaptation and circadian protein turnover), which goes beyond the quasi-steady state assumption.

biophysics↗

bioRxiv2020

Inhibition of fibrosis with multi-agent therapy in pulmonary fibrosis: Results of a drug library screening

AimsSuccessful management of IPF will likely require multi-drug therapy as its pathogenesis is thought to be both driven by both pro-inflammatory and pro-fibrotic pathways. We hypothesized that the available anti-fibrotic agents, pirfenidone and nintedanib, may exhibit synergy in suppressing lung fibroblast extracellular matrix protein generation when administered in combination with other orally active agents. Materials and MethodsA fibroblastic cell line (AKR-2B) was stimulated with TGF-{beta}1 and used to screen a library of over 1500 FDA approved drugs. Extracellular matrix protein generation was assessed via fibronectin ELISA assay and maintenance of cell viability confirmed with XTT assay. ResultsThe screening revealed sixty-two drugs from the repurposed drug-screening library that were shown to significantly suppress fibronectin expression and not result in cell death. Specifically drugs within the category of NSAIDs, steroids, azole antifungal agents, and antipyrine were associated with significant suppression of fibronectin on ELISA analysis. Surprisingly, we observed anti-fibrotic activity across a number of the azole antifungal compounds. We next assessed whether combination of azoles would exhibit synergy when combined with current anti-fibrotic therapies in the stimulated fibroblasts. As proof of concept, we demonstrated in vitro synergy between oxiconazole and nintedanib in suppressing fibroblast generation of extracellular matrix fibronectin. ConclusionsThese results suggest an approach to identify potential combinations of therapy that may improve patient outcomes by reducing cost and potential toxicities during treatment.

cell biology↗

bioRxiv2019

Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations

Molecular Dynamics (MD) simulation have been emerging as an excellent candidate for understanding complex atomic and molecular scale mechanism of bio-molecules that control essential bio-physical phenomenon in a living organism. But this MD technique produces large-size and long-timescale data that are inherently high-dimensional and occupies many terabytes of data. Processing this immense amount of data in a meaningful way is becoming increasingly difficult. Therefore, specific dimensionality reduction algorithm using deep learning technique has been employed here to embed the high-dimensional data in a lower-dimension latent space that still preserves the inherent molecular characteristics i.e. retains biologically meaningful information. Subsequently, the results of the embedding models are visualized for model evaluation and analysis of the extracted underlying features. However, most of the existing visualizations for embeddings have limitations in evaluating the embedding models and understanding the complex simulation data. We propose an interactive visual analytics system for embeddings of MD simulations to not only evaluate and explain an embedding model but also analyze various characteristics of the simulations. Our system enables exploration and discovery of meaningful and semantic embedding results and supports the understanding and evaluation of results by the quantitatively described features of the MD simulations (even without specific labels).

bioinformatics↗

bioRxiv2018

S100A8/A9 mediate the reprograming of normal mammary epithelial cells induced by dynamic cell-cell interactions with adjacent breast cancer cells.

To understand the potential effects of cancer cells on surrounding normal mammary epithelial cells, we performed direct co-culture of non-tumorigenic mammary epithelial MCF10A cells and various breast cancer cells. Firstly, we observed dynamic cell-cell interactions between the MCF10A cells and breast cancer cells including lamellipodia or nanotube-like contacts and transfer of extracellular vesicles. Co-cultured MCF10A cells exhibited features of epithelial-mesenchymal transition, and showed increased capacity of cell proliferation, migration, colony formation, and 3-dimensional sphere formation. Transcriptome analysis and phosphor-protein array suggested that several cancer-related pathways are significantly dysregulated in MCF10A cells after the direct co-culture with breast cancer cells. S100A8 and S100A9 showed distinct up-regulation in the co-cultured MCF10A cells and their microenvironmental upregulation was also observed in the orthotropic xenograft of syngeneic mouse mammary tumors. When S100A8/A9 overexpression was induced in MCF10A cells, the cells showed phenotypic features of directly co-cultured MCF10A cells in terms of in vitro cell behaviors and signaling activities suggesting a S100A8/A9-mediated transition program in non-tumorigenic epithelial cells. This study suggests the possibility of dynamic cell-cell interactions between non-tumorigenic mammary epithelial cells and breast cancer cells that could lead to a substantial transition in molecular and functional characteristics of mammary epithelial cells.

cancer biology↗

bioRxiv2017

SID-1 domains important for dsRNA import in C. elegans

In the nematode Caenorhabditis elegans, RNA interference (RNAi) triggered by double-stranded RNA (dsRNA) spreads systemically to cause gene silencing throughout the organism and its progeny. We confirm that Caenorhabditis nematode SID-1 orthologs have dsRNA transport activity and demonstrate that the SID-1 paralog CHUP-1 does not transport dsRNA. Sequence comparison of these similar proteins, in conjunction with analysis of loss-of-function missense alleles identifies several conserved 2-7 amino acid microdomains within the extracellular domain that are important for dsRNA transport. Among these missense alleles, we identify and characterize a sid-1 allele, qt95, which causes tissue-specific silencing defects most easily explained as a systemic RNAi export defect. However, we conclude from genetic and biochemical analyses that sid-1(qt95) disrupts only import and speculate that the apparent export defect is caused by the cumulative effect of sequentially impaired dsRNA import steps.Thus, consistent with previous studies, we fail to detect a requirement for sid-1 in dsRNA export, but demonstrate for the first time that SID-1 functions in the intestine to support environmental RNAi.

genetics↗