Biology subjects

Cho, H. J.

Publications and source records attributed to Cho, H. J..

At least 19 recordsLinked to original sources

bioRxiv2026

Integrated coding-noncoding genome annotation expands single-cell transcriptomic discovery and identifies clinically relevant noncoding RNAs in multiple myeloma

Although the human genome encodes a vast repertoire of noncoding RNAs that regulate gene expression, the noncoding genome remains underexplored due to technical challenges. Specifically, during transcriptomic sequencing data alignment, the overlap between noncoding and coding loci can create ambiguous read alignments that are subsequently discarded from downstream analysis. For this reason, most of the noncoding genome is excluded from standard genomic annotations used for sequencing alignment. To address this challenge and enable concurrent profiling of the coding and noncoding transcriptome, we systematically integrated standard coding (GENCODE) and noncoding (LncBook) genome annotations, preserving coding gene annotations and removing overlapping noncoding regions. The resulting integrated genome annotation expanded the number of annotated noncoding genes from 40,785 to 138,296 while preserving all coding genes and reducing ambiguous read assignment. To evaluate the utility of our integrated genome annotation for uncovering novel, biologically relevant noncoding RNAs (ncRNAs), we realigned CD138-positive bulk RNA-seq (N = 942) and CD138-negative single-cell RNA-seq (N = 478) data from the MMRF CoMMpass study, generating a comprehensive coding-noncoding atlas of the myeloma bone marrow microenvironment with noncoding genes representing 51% of highly variable genes and displaying significant cell type specificity. Tumor expression profiling based on this integrated profiling identified 15 clusters, including two enriched for amp(1q21) or t(4;14) and associated with shorter progression-free survival (PFS). Differential expression and systematic filtering yielded 19 candidate high-risk ncRNAs, including previously uncharacterized ENSG00000310209, which was associated with poor PFS (HR = 1.141, P = 0.0025), increased IRF4 activity, Wnt pathway activation, CCL5 signaling, and the accumulation of anergic-like CD8+ T cells. These findings establish integrated coding-noncoding analysis as a strategic approach for discovering functional ncRNAs from transcriptomic sequencing data.

cancer biology↗

bioRxiv2026

Highly Constrained Kinetic Models for Single-Cell Gene Expression Analysis

Advances in single-cell RNA sequencing (scRNA-seq) and high-resolution imaging techniques, such as single-molecule tracking (SMT) of RNA and transcription factors, allow researchers to quantitatively explore dynamics and variation but have never been integrated into a single coherent model. In this study, we propose a kinetic model that intakes multiple data types, including steady-state and time-resolved datasets, to simulate and fit stochastic models of gene transcription to experimental data. We find that 3-state models provide an essential improvement over the widely used 2-state model for most genes and have the property of kinetic proofreading, which we argue is advantageous in the cellular context. We further identify two dimensionless quantities derived from the rate equations which are broadly conserved across genes. Finally, we extend this model to scRNA-seq datasets to infer kinetic rates under defined perturbations and reveal biochemical insight into the mechanism of action of transcription factors.

bioinformatics↗

bioRxiv2026

Modeling Synthetic Audience Reactions to Social-Cognitive Narratives: A Generative GSR Model to Predict Real-World Autonomic Alignment during Film Viewing

While media consumption can be a solitary act, it produces a shared, socially coordinated experience where audiences bodies align in response to shared narrative events that are often social-affective in nature. Despite this recognition, traditional descriptive models of Galvanic skin response (GSR) have existed for decades, yet the socially coordinated aspect remains to be fully reflected in physiological models with the field of communication often treating the underlying generators of autonomic activity as a black box. To bridge this gap, we introduce a computational framework that models the underlying neural driver and its convolution to sweat gland physiology to explain how narrative events translate into measurable conductance. By leveraging multimodal AI models to "interpret" the social-cognitive content of a film, we generated a predictor timeline for a synthetic audience comprised of digital agents (i.e. artificial body systems responding to the film events with GSR responses). We then test this computational audience model by comparing its predictions against an empirical dataset collected as audience members (N = 96) processed the same stimulus, finding that AI-identified social triggers, like moments of comedic violence or shared emotional shifts, significantly predict the GSR time-course of audience engagement. In sum, this paper moves beyond simple and often retrospective labels like "arousal" to offer a computational account of how shared social narratives grip the human nervous system. We provide a scalable and expandable framework and a set of tools to predict media impact and understanding the psychophysiological basis of media.

animal behavior and cognition↗

bioRxiv2026

Kinetic proofreading as a mechanism for transcriptional specificity in living human cells

How target genes selectively respond to their specific transcription factors (TF) amid the vast excess of non-specific TFs in the nucleus remains a fundamental question in gene regulation. Here, we develop an integrated single-molecule imaging framework to quantitatively link TF dynamics with nascent transcription kinetics at endogenous gene loci to address the transcription factor specificity. Using endogenously Halo-tagged glucocorticoid receptor (GR), we show that ligand activation rapidly increases GR chromatin binding and residence times without substantially altering search kinetics. Live-cell nascent RNA imaging reveals that GR is essential for Dex induction of ERRFI1 transcription via increased burst frequency. Gene locus-specific, dual-color tracking demonstrates that GR display longer residence times in general near its target gene ERRFI1 compared to the non-target MYH9 locus, consistent with a kinetic proofreading model. A high-throughput imaging-based CRISPR screen identifies ATP-dependent processes, including neddylation and chromatin remodeling, and similarly, acute inhibition of the TFIIH XPB selectively impairs ERRFI1 transcription while sparing MYH9, implicating ATPase activity in GR target discrimination. In all, these findings establish that promoters function as dwell-time rather than occupancy detectors, discriminating specific from non-specific TF interactions through energy-dependent mechanisms.

biophysics↗

bioRxiv2026

The Signal in the Noise: Hierarchy and Robustness of Physiological Audience Alignment during Narrative Media

Communication research traditionally prioritizes receiver variance, often overlooking how a single message induces commonality across distinct systems. Drawing on classical theory, we propose that effective messages entrain the biological rhythms of the audience. In a large-scale experiment (N=198) monitoring eye-tracking, heart rate, and electrodermal activity during a film, we mapped this alignment. Results reveal a processing hierarchy: alignment was strongest for information acquisition (gaze) but also extended to downstream autonomic regulation. Crucially, a temporal manipulation confirmed this state is content-locked: alignment vanished when the narrative sequence was altered but was restored upon computational unscrambling of the physiological time series. This confirms that responses carry a temporal fingerprint of the content. We conclude that messages function as alignment devices, reducing individual noise to create a network of receivers exhibiting shared processing states, thus providing a materially grounded, signal-based definition of the audience.

animal behavior and cognition↗

bioRxiv2025

Transcriptional Profiling of D1-MSNs Reveals SIRT1-Dependent and -Independent Responses to Chronic Social Defeat Stress

SIRT1 is a critical regulator of neuronal functions and has been implicated in various neurological disorders, including depression. In a previous study, we demonstrated the pro-depressive roles of SIRT1 in the nucleus accumbens (NAc) using the chronic social defeat stress (CSDS) model, a well-validated preclinical mouse model of depression. We found that SIRT1 modulates synaptic functions and exhibits pro-susceptible effects specifically in D1-medium spiny neurons in the NAc. In this study, we extended our investigation of SIRT1 functions by employing cell-type-specific transcriptomics to compare control and susceptible groups under CSDS. Our results revealed that in wild-type mice with functional SIRT1, cellular metabolism is crucial for inducing susceptibility to depression. Conversely, in the absence of functional SIRT1 expression, circadian clock-controlled transcriptional regulation becomes a key factor. These findings suggest that SIRT1 differentially modulates key pathophysiological mechanisms of depression via its deacetylase functions, highlighting its role in both metabolic and circadian regulation in the brain. This research provides new insights into the molecular underpinnings of depression and identifies potential targets for therapeutic intervention.

neuroscience↗

bioRxiv2025

Kinetic Plasticity of Nitrite-Oxidizing Bacteria Containing Cytoplasmic Nitrite Oxidoreductase

Nitrite oxidation, the second step of nitrification, is essential to the global nitrogen cycle. Nitrite-oxidizing bacteria (NOB) are classified into two groups based on the cellular localization of their key enzyme nitrite oxidoreductase (NXR): periplasmic (pNXR) and cytoplasmic (cNXR). The use of a cNXR by NOB has been reported to be linked to a lower nitrite affinity and energy efficiency of nitrite oxidation, indicating adaptation to nitrogen-rich environments. In this study, cNXR NOB model strains demonstrated nitrite concentration-dependent shifts in optimal growth pH, a behavior not observed in pNXR NOB. Nitrobacter winogradskyi Nb-255 (cNXR NOB), grown at 1 mM nitrite (pH 7.5), exhibited a high nitrite affinity in terms of apparent Km (25.9 M) and a high specific affinity a{degrees} (440.5 l g cells-1 h-1), both comparable to pNXR NOB in microrespirometry-based kinetic assays. Unexpectedly, cells pre-grown at 10 mM nitrite (pH 7.5) achieved a pNXR-like affinity at pH 5.5 without prior adaptation to acidic conditions. In contrast, pNXR NOB exhibited consistent kinetic behavior across different pH conditions. Kinetic inhibition in the presence of nitrate suggested that this plasticity is driven by a regulated interplay between nitrite uniport and nitrite/nitrate antiporter systems. Our findings indicate that Nitrobacter can dynamically modulate nitrite affinity in response to both nitrite concentration and pH, conferring a flexible adaptation strategy that features traits of both r-and K-strategists across a range of environmental conditions. This adaptive plasticity likely extends to other cNXR-containing NOB in response to fluctuating environmental conditions.

microbiology↗

bioRxiv2025

Longitudinal multi-omic profiling uncovers immune escape and predictors of response in multiple myeloma

Multiple myeloma (MM) is an incurable malignancy of clonally expanded plasma cells shaped by complex interactions with the immune microenvironment. To investigate immune factors driving treatment response and resistance, we conducted multi-omics profiling including CD138neg single-cell RNA sequencing of 243 bone marrow samples from 102 patients (631,226 cells) and CD138pos bulk RNA and whole-genome sequencing from 209 samples. Longitudinal analyses revealed that interferon gamma signaling impairs T cell memory after autologous stem cell transplant, while naive B cell abundance and immunoglobulin diversity correlated with improved progression-free survival (HR = 0.48, p = 2.3e-4). At disease progression, MM cells upregulated cancer-testis antigens and immune effector genes, with concurrent B cell depletion, enrichment of myeloid-derived suppressor cell genes in monocytes, and T cell exhaustion. These findings highlight dynamic immune-tumor interactions, identifying naive B cell reconstitution as a biomarker of durable response, and cancer-testis antigens as potential targets for high-risk disease at progression. Statement of SignificanceLongitudinal profiling of multiple myeloma and the immune microenvironment revealed dynamic immune-tumor interactions across the disease course. Dysfunctional CD8 T cells limited memory formation post-transplant, while naive B recovery associated with sustained treatment response. At progression, cancer-testis antigen expression associated with immunosuppression, revealing novel mechanisms of immune escape.

cancer biology↗

bioRxiv2025

Novel transcription factor BTNL9 enhances tumor suppression and drug sensitivity in non-small cell lung cancer through cell cycle regulation

BackgroundButyrophilins (BTNs) are immunoglobulin superfamily proteins involved in immune regulation. Among them, BTNL9 has unique structural features, including a bZIP-like domain, suggesting a potential transcriptional role. While BTNL9 is known to suppress T cell activation, its function in cancer remains largely unexplored. Recent studies suggest it may inhibit tumor progression and correlate with improved prognosis in multiple cancers. However, its molecular mechanisms and regulatory impact on lung cancer remain unclear. This study investigates the role of BTNL9 as a transcription factor and its implications for tumor progression and therapy. MethodsChIP-seq identified BTNL9-binding sites, followed by RNA-seq to assess transcriptomic profiles and validated by western blot. Drug sensitivity was evaluated through cytotoxicity assays. A xenograft model was applied to assess the effect of BTNL9 on tumor growth. TCGA data analysis examined correlations with survival, cell cycle regulators, and immune infiltration. ResultsChIP-seq identified 26,610 BTNL9 binding peaks, mapping to 9,707 genes near transcription start sites. RNA-seq and western blotting showed BTNL9 regulates cell cycle (E2F1, CDKN1A, CDK1, CDC25C, FOXM1), DNA replication (MCM2/3/7, ORC6), and p53-related transcription (BBC3, GADD45A). Integrative analysis found that 74.8% of differentially expressed genes were directly regulated by BTNL9. Functionally, BTNL9 overexpression induced cell cycle arrest, reduced proliferation, and suppressed tumor growth in vivo. BTNL9 enhanced bortezomib sensitivity in both A549 and NCI-H460 cells, with etoposide effects being more pronounced in A549. Higher BTNL9 levels strongly suppressed the expression of FOXM1, CDC25C, CDK1, CDK2, CCNA2 and CCNB1 and negatively correlated with these markers in LUAD TCGA data. Elevated BTNL9 expression was associated with improved survival, complete remission, and increased immune infiltration, including macrophages, CD8+ T cells, NK cells, and B cells in cancer tissues. ConclusionsBTNL9 functions as a transcription factor, suppresses tumor growth, and enhances drug sensitivity. Its correlation with survival and immune infiltration suggests potential role as a tumor suppressor and predictive biomarker for chemotherapy response.

cancer biology↗

bioRxiv2025

Memory Inception through Gaze-Contingent Message Exposure: Using Virtual Reality to Study Media Influence

The messages we encounter in our environment can shape our knowledge about the world. However, much research on mediadriven influence via messages focuses on population-level effects and aggregate exposure statistics, obscuring how individual and self-determined behaviors affect message intake, processing, and effects. To address this gap, we use virtual reality (VR) to create a controlled messaging environment. Participants navigate a simulated urban street lined with billboard messages while their visual attention is tracked via eye-tracking. We introduce an inception-style manipulation: overlooked billboards are strategically reintroduced, creating additional exposure opportunities. Our results demonstrate that this subtle manipulation - unnoticed by participants - boosts message retention. This study bridges communication theory and psychology, elucidating the blurred line between voluntary and involuntary information intake in the digital age. It also highlights a potential vulnerability in the future metaverse media ecosystem, where undetected information manipulations can influence individual and collective attention and memories.

animal behavior and cognition↗

bioRxiv2024

Using VR and eye-tracking to study attention to and retention of AI-generated ads in outdoor advertising environments

In contemporary urban environments, advertisements are ubiquitous, capturing the attention of individuals navigating through cityscapes. This study simulates this situation by using VR as an advertising research tool and combining it with eye-tracking to rigorously assess attention to and retention of visual advertisements. Specifically, participants drove through a virtual city with 40 AI-generated, experimentally manipulated, and randomly assigned advertisements (20 commercial, 20 health) distributed throughout. Our results confirm theoretical predictions about the link between exposure, visual attention, and incidental memory. Specifically, we found that attended ads are likely to be recalled and recognized, and concurrent task demands (counting sales signs) decreased visual attention and subsequent recall and recognition of the ads. Finally, we identify the influence of ad placement in the city as a very important but previously hard-to-study factor influencing advertising effects. This paradigm offers great flexibility for biometric advertising research and can be adapted to varying contexts, including highways, airports, and subway stations, and theoretically manipulate other variables. Moreover, considering the metaverse as a next-generation advertising environment, our work demonstrates how causal mechanisms can be identified in ways that are of equally high interest to both theoretical as well as applied advertising research.

animal behavior and cognition↗

bioRxiv2024

Eyes on VR: Unpacking the Causal Chain Between Exposure, Reception, and Retention for Emotional Billboard Messages

The causal chain from message exposure to reception to effects is widely accepted as the basic explanatory model for communication outcomes. Problematically, the chains links are often studied in isolation, leaving measurement gaps that compromise the ecological validity and practical utility of experimental research. Here we introduce a VR-based paradigm that encompasses a realistic message reception context, i.e., a simulated car ride on a highway flanked by billboards. We varied attentional message factors (emotional content) as well as contextual task distractions (trash-counting). VR-integrated eye trackers were used to capture participants incidental message exposure dependent on their actual gaze behavior. Consistent with our predictions, results show that 1. exposure gates all subsequent effects; 2. distraction impacts likelihood of exposure; 3. both the manipulation of emotional content and distraction affect retention. This comprehensive analysis of the exposure-reception-retention chain can be broadly applied to a variety of message reception contexts that will be discussed.

animal behavior and cognition↗

bioRxiv2024

Generating Synthetic Single Cell Data from Bulk RNA-seq Using a Pretrained Variational Autoencoder

Single cell RNA sequencing (scRNA-seq) is a powerful approach which generates genome-wide gene expression profiles at single cell resolution. Among its many applications, it enables determination of the transcriptional states of distinct cell types in complex tissues, thereby allowing the precise cell type and set of genes driving a disease to be identified. However, scRNA-seq remains costly, and there are extremely limited samples generated in even the most extensive human disease studies. In sharp contrast, there is a wealth of publicly available bulk RNA-seq data, in which single cell and cell type information are effectively averaged. To further leverage this wealth of RNA-seq data, methods have been developed to infer the fraction of cell types from bulk RNA-seq data using single cell data to train models. Additionally, generative AI models have been developed to generate more of an existing scRNA-seq dataset. In this study, we develop an innovative framework that takes full advantage of powerful generative AI approaches and existing scRNA-seq data to generate representative scRNA-seq data from bulk RNA-seq. Our bulk to single cell variational autoencoder-based model, termed bulk2sc, is trained to deconvolve pseudo-bulk RNA-seq datasets back into their constituent single-cell transcriptomes by learning the specific distributions and proportions related to each cell type. We assess the performance of bulk2sc by comparing synthetically generated scRNA-seq to actual scRNA-seq data. Application of bulk2sc to large-scale bulk RNA-seq human disease datasets could yield single cell level insights into disease processes and suggest targeted scRNA-seq experiments.

bioinformatics↗

bioRxiv2023

A primary culture method for the easy, efficient and effective acquisition of oligodendrocyte lineage cells

Oligodendrocytes (OL) are myelin forming glial cells in the central nervous system. In vitro primary OL culture models provide the benefit of a more readily controlled environment facilitating the evaluation of diverse OL stages and convoluted dynamics. Conventional methods of primary OL culture do exist, but their performance in terms of efficiency and simplicity has room for improvement. We present a novel method of primary OL culture, namely the E3 (Easy, efficient, and effective) method, which greatly improves cell yield and reduces time required to oligodendrocyte progenitor cell (OPC) acquisition and maturation into OLs. We also provide optimal media compositions for augmentation of OPC proliferation and a more robust maturation into myelin forming OLs. In vitro characteristics of the OL lineage discovered during the development of the E3 method present implications for further research on OL physiology and pathophysiology.

neuroscience↗

bioRxiv2023

DARDN: Identifying transcription factor binding motifs from long DNA sequences using multi-CNNs and DeepLIFT

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facilitate oncogenic transcription programs by cooperating with transcription factors bound at flanking distal regions. Identification of DNA sequence features from a broad genomic region that distinguish cancer-specific CTCF binding sites from regular CTCF binding sites can help find oncogenic transcription factors in a cancer type. However, the long DNA sequences without localization information makes it difficult to perform conventional motif analysis. Here we present DNAResDualNet (DARDN), a computational method that utilizes convolutional neural networks (CNNs) for predicting cancer-specific CTCF binding sites from long DNA sequences and employs DeepLIFT, a method for interpretability of deep learning models that explains the models output in terms of the contributions of its input features [1], for identifying DNA sequence features associated with cancer-specific CTCF binding. Evaluation on DNA sequences associated with CTCF binding sites in T-cell acute lymphoblastic leukemia (T-ALL) and other cancer types demonstrates DARDNs ability in classifying DNA sequences surrounding cancer-specific CTCF binding from control constitutive CTCF binding and identifying sequence motifs for transcription factors potentially active in each specific cancer type. We identified potential oncogenic transcription factors in T-ALL, acute myeloid leukemia (AML), breast cancer (BRCA), colorectal cancer (CRC), lung adenocarcinoma (LUAD), and prostate cancer (PRAD). Our work demonstrates the power of advanced machine learning and feature discovery approach in finding biologically meaningful information from complex high-throughput sequencing data.

bioinformatics↗

bioRxiv2023

The VR Billboard Paradigm: Using VR and Eye-tracking to Examine the Exposure-Reception-Retention Link in Realistic Communication Environments

Exposure is the cornerstone of media and message effects research. If a health, political, or commercial message is not noticed, no effects can ensue. Yet, existing research in communication, advertising, and related disciplines often fails to measure exposure and demonstrate the causal link between quantified exposure to outcomes because actual exposure (i.e., whether recipients were not only exposed to messages but also took notice of them) is difficult to capture. Here, we harness Virtual Reality (VR) technology integrated with eye tracking to overcome this challenge. While eye-tracking technology alone can capture whether people attend to messages in their communication environment, most eye-tracking research is bound by laboratory-based screen-reading paradigms that are not representative of the broader communication environments in which messages are encountered. Emerging eye-tracking field research suffers from an inability to control and experimentally manipulate key variables. Our solution is to measure eye-tracking within an immersive environment in VR that resembles a realistic message reception context. Specifically, we simulate driving down a highway alongside which billboards are placed and use VR-integrated eye-tracking to measure whether the drivers look at individual billboard messages. This allows us to rigorously quantify the nexus between exposure and reception, and to link our measures to subsequent memory, i.e., whether messages were remembered, forgotten, or not even encoded. We further demonstrate that manipulating drivers attention directly impacts gaze behavior and memory. We discuss the large potential of this paradigm to quantify exposure and message reception in realistic communication environments and the equally promising applications in new media contexts (e.g., the Metaverse).

animal behavior and cognition↗

bioRxiv2021

Isolation disrupts social interactions and destabilizes brain development in bumblebees

Social isolation, particularly in early life, leads to deleterious physiological and behavioral outcomes. Few studies, if any, have been able to capture the behavioral and neurogenomic consequences of early life social isolation together in a single social animal system. Here, we leverage new high-throughput tools to comprehensively investigate the impact of isolation in the bumblebee (Bombus impatiens) from behavioral, molecular, and neuroanatomical perspectives. We reared newly emerged bumblebees either in complete isolation, small groups, or in their natal colony, and then analyzed their behaviors while alone or paired with another bee. We find that when alone, individuals of each rearing condition show distinct behavioral signatures. When paired with a conspecific, bees reared in small groups or in the natal colony express similar behavioral profiles. Isolated bees, however, showed increased social interactions. To identify the neurobiological correlates of these differences, we quantified brain gene expression and measured the volumes of key brain regions for a subset of individuals from each rearing condition. Overall, we find that isolation increases social interactions and disrupts gene expression and brain development. Limited social experience in small groups is sufficient to preserve typical patterns of brain development and social behavior.

animal behavior and cognition↗

bioRxiv2021

BET inhibitors synergize with anti-PD1 by rescuing TCF1+ progenitor exhausted CD8+ T cells in Acute Myeloid Leukemia

Many acute myeloid leukemia (AML) patients exhibit hallmarks of immune exhaustion, such as increased myeloid derived suppressor cells (MDSCs), suppressive regulatory T cells (Tregs) and dysfunctional T cells. We have developed a mouse model of AML driven by Flt3-ITD and Tet2 deficiency displays these immune-related features, including CD8+ T cells exhibiting a terminally exhausted phenotype (TEx). This T cell subset has been shown to be refractory to immune checkpoint blockade (ICB) monotherapy. Here we show that small molecule inhibitors which target bromodomain and extra-terminal domain (BET) proteins affect both tumor-intrinsic factors but also rescue T cell exhaustion and ICB resistance. Ex vivo treatment of cells from AML mice and AML patients with BET inhibitors (BETi) reversed CD8+ T cell exhaustion by restoring proliferative capacity and expansion of the more functional precursor exhausted T cells (TPEx). This reversal is enhanced by combined BETi and anti-PD1 treatment. Finally, we show that BETi synergizes with anti-PD1 in vivo, resulting in the reduction of circulating leukemia cells, enrichment of CD8+ T cells in the bone marrow, and increased expression of Tcf7, Slamf6, and Cxcr5 in CD8+ T cells. In total, we show the potential efficacy of combining BETi and ICB therapy in the treatment of AML.

cancer biology↗