Biology subjects

Sung, J.-Y.

Publications and source records attributed to Sung, J.-Y..

10 recordsLinked to original sources

bioRxiv2026

Gene Program Negotiation Defines Cellular Identity in Single-Cell Transcriptomes

Single-cell transcriptomics has transformed the characterization of cellular heterogeneity by enabling systematic analysis of biological gene programs. However, existing computational approaches primarily quantify the activity of individual programs independently and therefore provide limited insight into how multiple simultaneously active programs collectively determine cellular identity. Here we present Gene Program Negotiation (GPN), a graph-based computational framework that models regulatory decision-making among concurrently active biological programs. GPN reconstructs cell-specific program interaction networks from local transcriptional neighborhoods and quantifies regulatory organization using the Gene Program Coherence Index (GPCI) together with measures of local regulatory conflict, program diversity, and dominance. These graph-derived properties enable the classification of individual cells into five regulatory decision states: Consensus, Competition, Negotiation, Dominance, and Low activity. Applying GPN to gastric cancer single-cell transcriptomes revealed that cells sharing the same dominant biological program frequently occupied distinct regulatory decision states, demonstrating that dominant program identity alone does not uniquely define cellular regulatory organization. Competition states consistently exhibited elevated local regulatory conflict and were preferentially enriched among transition-like cells, indicating that regulatory competition is closely associated with transcriptional plasticity. Independent validation using glioblastoma single-cell transcriptomes reproduced these regulatory patterns without modification of the computational framework, supporting the robustness and generalizability of the approach across biologically distinct malignancies. These findings establish regulatory negotiation as an additional layer of cellular organization beyond conventional gene-program activity analysis. By explicitly modeling interactions among simultaneously active biological programs, GPN provides a general computational framework for investigating regulatory coordination, cellular plasticity, and dynamic cell-state organization in single-cell transcriptomic data.

bioinformatics↗

bioRxiv2026

FateLimit quantifies the prediction horizon of cell fate

Single-cell technologies have enabled increasingly detailed reconstruction of developmental trajectories, yet a fundamental question remains unresolved: when does future cellular identity become predictable from a cells current molecular state? Existing approaches infer lineage relationships, transition probabilities or future transcriptional dynamics, but do not directly quantify the emergence of fate predictability during cellular state transitions. Here we present FateLimit, an information-theoretic framework for measuring the temporal dynamics of cell-fate predictability from single-cell omics data. FateLimit combines probabilistic fate assignment, fate entropy and mutual information to quantify how information about future cellular outcomes is encoded in present molecular states. We introduce two quantitative descriptors: the Fate Information Half-Life (FIHL), which measures the characteristic timescale of fate-information dynamics, and the Prediction Horizon (PH), defined as the earliest developmental stage at which observed fate predictability exceeds the 95th percentile of a permutation-derived null distribution. We applied FateLimit across developmental, lineage-tracing and reprogramming systems, including pancreatic endocrinogenesis, CellTag reprogramming, human hematopoiesis and zebrafish embryogenesis. Across all datasets, FateLimit identified significant fate information and reproducible prediction horizons that were robust to cell-state representation, lineage structure and biological context. Comparative analysis revealed that prediction horizons differ substantially among cellular lineages, indicating that distinct developmental programs acquire predictive information at different rates. FateLimit establishes a general framework for quantifying the predictability of future cellular identity from present molecular states. By transforming developmental trajectories into predictability landscapes, FateLimit enables systematic comparison of commitment dynamics across biological systems and establishes prediction horizons as a quantitative measure of cell-fate determination.

bioinformatics↗

bioRxiv2026

Leigh syndrome as a disorder of protein glass dynamics disrupting electron transport in mitochondrial Complex I

Leigh syndrome is the most common pediatric mitochondrial encephalopathy, yet the physical mechanisms linking diverse pathogenic mutations to respiratory-chain failure remain poorly understood. Here we show that Leigh syndrome mutations are not randomly distributed within human mitochondrial Complex I but are preferentially enriched near the electron-transfer axis connecting flavin mononucleotide and iron-sulfur cofactors. By integrating structural mutation mapping with residue-level free-volume analysis, packing-density measurements, and a protein glass index (PGI), we identify a distinct class of mutation-associated microenvironments characterized by reduced free volume, elevated packing density, and increased structural constraint. These protein-glass-like microenvironments were associated with elevated reorganization-energy proxies and increased transport vulnerability. Structure-informed Marcus analyses further identified a dominant bottleneck that was primarily associated with reduced electronic coupling arising from donor-acceptor separation, while local microenvironmental constraints further amplified transport sensitivity. Structure-informed Marcus analyses identify the emergence of a dominant kinetic bottleneck within the iron-sulfur cluster network, and a Lindblad-based open quantum transport model indicates that local microenvironmental perturbations can propagate into network-level transport efficiency across the Complex I redox chain. Notably, pathogenic mutations preferentially accumulate in structural neighborhoods that are intrinsically sensitive to electron-transfer perturbation, suggesting that disease-associated variants may amplify pre-existing transport vulnerabilities embedded within the protein architecture. Collectively, our findings suggest a structural-energetic link between mutation landscapes, local protein-glass-like organization, and mitochondrial electron transport. We propose that Leigh syndrome can be viewed, in part, through a protein-glass lens in which pathogenic mutations preferentially map to structural-energy landscapes surrounding redox cofactors that are predicted to be vulnerableto electron-transfer perturbations. This framework provides a physical perspective for understanding genotype-to-phenotype convergence in mitochondrial disease and identifies local protein-glass analogous microenvironments as previously unrecognized determinants of respiratory-chain dysfunction.

biophysics↗

bioRxiv2026

Information Geometry of Intracellular Compartment Coupling Reveals Transcriptomic State Transitions in Single Cells

Single-cell transcriptomic analyses typically characterize cellular states using gene-expression variability, dimensionality reduction, and trajectory inference. However, existing approaches provide limited insight into how transcriptomic information is organized across interacting intracellular compartments. Here we introduce Compartment Coupling Entropy (CCE), an information-geometric framework that quantifies the organization of transcriptomic coupling between spliced and unspliced RNA compartments. CCE constructs a cross-compartment coupling operator from compartment-resolved transcriptomic profiles and characterizes its singular-value spectrum using coupling entropy, effective coupling dimension, and coupling susceptibility. These metrics measure how transcriptomic information is distributed across coupling modes and provide a quantitative description of transcriptomic organization beyond conventional expression-based statistics. Applying CCE to pancreatic endocrine differentiation revealed substantial remodeling of coupling architecture along developmental trajectories. Coupling entropy and effective coupling dimension underwent transient collapse and re-expansion during lineage progression, while coupling susceptibility identified discrete intervals of rapid transcriptomic reorganization corresponding to candidate cell-state transition regimes. Across cell states, coupling entropy showed weak correspondence with classical mutual information, indicating that spectral coupling organization captures information not represented by conventional information-theoretic measures. An organization ratio and spectral excess information further quantified the divergence between classical and coupling-based descriptions of transcriptomic structure. Robustness analyses demonstrated stability of the framework under bootstrap resampling, gene subsampling, spectral truncation, and trajectory discretization. Application to an independent dentate gyrus developmental dataset revealed similar hierarchical coupling spectra and susceptibility-defined transition regimes, suggesting that transient reorganization of compartment-coupling architecture may represent a general feature of cellular state transitions. CCE provides a general methodology for quantifying the information geometry of intracellular transcriptomic organization and complements existing single-cell analytical approaches by revealing coupling architectures that are inaccessible to conventional expression-based analyses.

bioinformatics↗

bioRxiv2026

Quantum transport in mitochondrial complex I is governed by a conserved structural bottleneck

Electron transport in mitochondrial complex I is mediated by a chain of redox centers, yet how electrons traverse this network beyond the canonical pathway remains unclear. While prior models treat transport as a sequential process, they do not resolve whether alternative pathways contribute to functional electron flow. Here, we formulate electron transport as a continuous-time quantum walk on a structure-derived redox network and systematically map pathway-level electron flux inferred from quantum-walk dynamics across species. We identify a conserved structural bottleneck at the N5-N6a interface that suppresses direct electron transfer. Strikingly, quantum-walk flux analysis indicates that this bottleneck does not simply limit transport, but can redistribute electron flux into residue-mediated alternative pathways. Across species, these alternative routes support substantial flux and, in several cases, are comparable to or can exceed the canonical direct pathway, indicating a conserved mechanism of pathway-level flux redistribution. This behavior arises from geometric constraints encoded in protein structure and persists under environmental decoherence, demonstrating that architecture governs not only transport efficiency but also the organization of electron flow within the network. Together, our findings suggest a network-level organization of electron transport in complex I, in which a structurally encoded bottleneck reshapes flux through alternative pathways, consistent with a structurally encoded link between protein geometry and quantum transport behavior. We note that the bottleneck-dominated and flux-redistribution observations are not in tension: suppression of the direct N5-N6a step is precisely what redirects amplitude into the parallel residue-mediated routes.

biophysics↗

bioRxiv2026

A Spin-Glass Metabolic Hamiltonian optimized by Quantum Annealing Reveals Thermodynamic Phases of Cancer Metabolism

Understanding why specific metabolic states become stable in cancer has remained a fundamental challenge, as current pathway-centric frameworks lack a unifying physical principle governing global metabolic organization. We introduce the Metabolic Spin-Glass (MSG) model, which represents cellular metabolism using a thermodynamically informed effective Hamiltonian that integrates reference reaction free energies, cofactor-mediated network couplings, and patient-specific transcriptomic fields within a frustrated many-body optimization framework. The Hamiltonian is formulated as a binary optimization problem and solved using hybrid quantum annealing. Embedding gastric cancer transcriptomes (n = 497) reveals that malignant phenotypes occupy distinct low-energy configurations within the effective metabolic landscape rather than representing isolated pathway perturbations. A thermodynamic order parameter stratifies patients into prognostically distinct subtypes independently of transcriptomic classification, suggesting clinically applicable non-redundant biomarkers. This work establishes a thermodynamically informed spin-glass energy-landscape framework for patient-specific characterization and stratification of cancer metabolic organization.

biophysics↗

bioRxiv2026

Programmable domestication of thermophilic bacteria through removal of non-canonical defense systems

Thermophilic bacteria offer major advantages for industrial biotechnology, yet most remain genetically intractable because cellular defense systems block efficient DNA acquisition. Here, we present a programmable domestication strategy that converts wild Geobacillus strains into genetically tractable thermophilic hosts. We developed the Domestication of Non-Model Bacteria (DNMB) Suite, a multi- omics-guided computational framework that systematically identifies genetic barriers to transformation. DNMB analysis revealed that non-canonical nuclease-based defense systems, including Wadjet II, constitute dominant barriers to DNA uptake in previously intractable Geobacillus strains. Targeted deletion of these loci increased transformation efficiency by up to six orders of magnitude. We further established a hierarchical thermophilic engineering toolkit that integrates plasmid artificial modification, conjugation-assisted DNA delivery, and genome editing using an endogenous CRISPR-Cas9 system. The resulting domesticated strains support stable heterologous expression and tunable genetic control at elevated temperatures. Together, these results establish a generalizable framework for transforming genetically intractable thermophiles into programmable industrial chassis.

bioengineering↗

bioRxiv2025

Keratin degradation reflects a starvation survival strategy in Fervidobacterium islandicum AW-1

Keratin is a highly cross-linked, disulfide-rich protein that resists proteolysis, which poses a major challenge for microbial degradation. Here, we show that Fervidobacterium islandicum AW-1 initiates a starvation-induced keratinolytic program involving membrane-associated proteases and redox-mediated sulfitolysis. Multi-omics integration reveals that nutrient limitation triggers global metabolic reprogramming, promoting sulfur assimilation, biofilm formation, and chemotaxis-linked persister-like adaptation. Substrate-specific transcriptomics identified a temporally regulated protease repertoire tightly coordinated with sulfitolytic activity, facilitating efficient feather decomposition under starvation. Protein-protein interaction networks uncovered stress-responsive transcriptional regulators that govern this process. Time-resolved gene expression analysis and metabolomic profiling further revealed that cyclic-di-GMP signaling, stringent response, and flagella assembly mediate transitions between motility and sessile growth, contributing to surface colonization and persistence. Together, our findings establish a starvation-responsive survival mechanism that couples keratin degradation to stress adaptation in extreme environments, offering insights into microbial persistence and potential strategies for keratin valorization.

microbiology↗

bioRxiv2025

Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE

Recent advances in spatial transcriptomics (ST) technologies have transformed our ability to profile gene expression while retaining the crucial spatial context within tissues. However, existing ST platforms suffer from high costs, long turnaround times, low resolution, limited gene coverage, and small tissue capture areas, which hinder their broad applications. Here we present iSCALE, a method that predicts super-resolution gene expression and automatically annotates cellular-level tissue architecture for large-sized tissues that exceed the capture areas of standard ST platforms. The accuracy of iSCALE were validated by comprehensive evaluations, involving benchmarking experiments, immunohistochemistry staining, and manual annotation by pathologists. When applied to multiple sclerosis human brain samples, iSCALE uncovered lesion associated cellular characteristics that were undetectable by conventional ST experiments. Our results demonstrate iSCALEs utility in analyzing large-sized tissues with automatic and unbiased tissue annotation, inferring cell type composition, and pinpointing regions of interest for features not discernible through human visual assessment.

genomics↗

bioRxiv2024

Deep Gaussian Process with Uncertainty Estimation for Microsatellite Instability and Immunotherapy Response Prediction Based on Histology

Determining tumor microsatellite status has significant clinical value because tumors that are microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) respond well to immune check-point inhibitors (ICIs) and oftentimes not to chemotherapeutics. We propose MSI-SEER, a deep Gaussian process-based Bayesian model that analyzes H&E whole-slide images in weakly-supervised-learning to predict microsatellite status in gastric and colorectal cancers. We performed extensive validation using multiple large datasets comprised of patients from diverse racial backgrounds. MSI-SEER achieved state-of-the-art performance with MSI prediction, which was by integrating uncertainty prediction. We achieved high accuracy for predicting ICI responsiveness by combining tumor MSI status with stroma-to-tumor ratio. Finally, MSI-SEERs tile-level predictions revealed novel insights into the role of spatial distribution of MSI-H regions in the tumor microenvironment and ICI response.

bioinformatics↗