Biology subjects

Huang, J.

Publications and source records attributed to Huang, J..

At least 91 records · Page 5Linked to original sources

bioRxiv2026

Profiling and Targeting of Regulatory RNAs to Upregulate Gene Expression

Transcription of long noncoding RNAs (lncRNAs), including enhancer RNAs (eRNAs) and promoter-associated RNAs (paRNAs), collectively termed regulatory RNAs (regRNAs), is a hallmark of active gene expression, yet it remains unknown whether regRNAs can be targeted to selectively enhance transcription in cis. We developed regRNA Capture-seq, a high-throughput method to profile regRNAs, and applied it to primary human hepatocytes, annotating thousands of regRNAs at [~]2,000 enhancers and promoters. Using this approach, we interrogated a genetically validated enhancer of the ornithine transcarbamylase (OTC) gene, mutations of which cause OTC deficiency (OTCD), the most common urea cycle disorder. Antisense oligonucleotides (ASOs) targeting enhancer-derived regRNAs led to dose-dependent upregulation of OTC in hepatocytes. Mechanistically, ASOs altered regRNA structure, elevated regRNA levels, displaced transcriptional repressors, and increased H3K27 acetylation at the targeted enhancer. This work establishes a potential therapeutic strategy for addressing haploinsufficiency and highlights regRNAs as actionable targets for ASO-mediated upregulation of gene expression.

genomics↗

bioRxiv2026

Zero-Shot Generation of Protein Conformational Ensembles Through AlphaFold Latent Flooding

Despite transformative advances in protein structure prediction, generating conformational ensembles directly from sequence in an efficient and accessible manner remains a central challenge. We introduce a heuristic importance sampling framework for the zero-shot prediction of functionally relevant, conformationally diverse structures, bypassing the need for extensive physics-based modeling or prior domain knowledge. Guided by an analysis of massive activations in AlphaFold (AF), we develop a latent flooding algorithm that enables adversarial exploration of the latent space while preserving local geometric integrity. We show that the AF latent flooding (AFLF) method robustly recovers experimental structural fluctuations, captures function-relevant conformational states, and reveals cryptic binding sites across diverse protein systems. These results suggest that AF latent features implicitly encode the biophysical principles of protein thermodynamics, which AFLF exploits. Finally, we show that AFLF is computationally accessible and interoperable, offering a test-time generalization of AF for investigating protein dynamics and accelerating structure-based ligand discovery.

biophysics↗

bioRxiv2026

In-silico cell sorting revealed granulocyte-specific single-cell-type gene expression from peripheral blood bulk expression data and its application as host response biomarkers to discriminate bacterial and viral infections

Peripheral Blood transcriptome analysis evaluated the bulk transcript abundance (TA) covering all leukocyte cell populations. However, there are 2 main problems in using bulk expression as biomarkers: (1) A long list of differential expression genes (DEGs) was found, and (2) DEGs cannot be attributed to a host response of any specific cell-type. TA assays after conventional cell sorting, as the gold-standard method, is too tedious for routine use. Recently, we showed that by using a ratio-based biomarker, RBB (ratio of two stringently selected genes), it is feasible to interrogate the gene expression of a single cell-type (monocyte and B lymphocyte) in peripheral whole blood (WB) directly. Here, we apply this in-silico cell sorting algorithm (DIRECT LS-TA, Direct Leukocyte Single cell-type Transcript Abundance) to granulocytes in WB samples to reveal RBBs specific to granulocytes. This DIRECT LS-TA approach without the need for cell-sorting was applied to public datasets to differentiate the 2 types of infection (bacterial vs viral infection). The following RBBs measured in WB correlate with the expression of target (numerator) genes in purified granulocytes, thus cell-sorting can be avoided by using these RBBs: ARG1/SRGN, ANXA3/SRGN, RSAD2/SRGN. Together with monocyte DIRECT LS-TA biomarkers, IFI27/PSAP, direct quantification of 4 genes provided optimal differentiation of viral from bacterial infection. Meta-analysis and unsupervised machine learning classification confirmed the superior performance of DIRECT LS-TA biomarkers. These RBBs found by prior In-silico cell-sorting identified pairs of genes that are used to formulate as ratio-based biomarkers (RBBs) to represent gene expression of granulocytes inside whole blood cell-mixture samples which was useful to triage febrile patients into two major categories of febrile diseases between viral and bacterial infection with high degree of sensitivity and specificity.

immunology↗

bioRxiv2026

Single-cell carbon storage dynamics drive conditional fitness in microbes

Microbes frequently encounter fluctuating environments, requiring dynamic energy management strategies for survival. While carbon storage polymers like polyhydroxybutyrate (PHB) are ubiquitous across bacterial taxa, their precise ecological advantage remains poorly understood.1 Here we show that carbon storage drives conditional fitness during environmental transitions. Using a high-throughput single-cell microfluidic platform, we tracked tens of thousands of Cupriavidus necator cells under precisely controlled carbon and nitrogen fluctuations. We found that PHB provides no advantage under nutrient abundance but becomes decisive at starvation boundaries: during carbon starvation, it enables [~]30% more progeny before arrest; during recovery from nitrogen starvation, it shortens lag and accelerates regrowth. Strikingly, at the single-cell level, PHB granules are inherited in an asymmetric, all-or-nothing fashion, concentrating resources into specific lineages to overcome the discrete energetic threshold required for cell division. Despite this single-cell variance, at the population level, PHB fractions robustly return to a common setpoint after nutrient shifts--a homeostatic behavior consistent with integral feedback control. These findings reveal that while PHB does not increase the basal exponential growth rate, it confers a distinct fitness advantage by prolonging the proliferative phase during nutrient depletion and facilitating successful recovery from starvation, explaining the evolutionary persistence of carbon storage in environments with pulsed resource availability.

microbiology↗

bioRxiv2026

Blockade of Tim-3 pathway in a mouse model of Toxoplasmosis: impact on brain leukocyte infiltration, parasite burden, and neuroinflammation

Cell-mediated immune responses are crucial for protecting the host against Toxoplasma gondii infection. However, impaired immunity, such as T-cell exhaustion, is a common phenomenon during chronic infection. This may represent a strategy employed by T. gondii to evade host defenses. T-cell immunoglobulin and mucin-domain containing 3 (Tim-3) is an important regulatory molecule involved in cell-mediated immunity. This study examined the expression of Tim-3 and the effects of its blockade in a mouse model of toxoplasmosis. In mice with chronic T. gondii infection, we found that Tim-3 is highly expressed in both cyst-bearing and non-cyst-bearing tissues, and its expression correlates with the parasite burden. Blocking the Tim-3 pathway with an anti-Tim-3 antibody enhances the immune response, resulting in elevated levels of cytokines (IFN-{gamma}, IL-12p70, IL-2, IL-9) and the chemokine CXCL1 in the serum, increased leukocyte infiltration (CD3+, CD14+ cells) in the brain, and downregulation of Tim-3 expression in microglial cells. As a result, the anti-Tim-3 treatment resulted in a 62% reduction in the number of tissue cysts and a trend towards an increase in the homeostatic signature, P2RY12, in microglia. Our study provides proof of concept for an anti-Tim-3 approach in treating chronic T. gondii infection and potentially other brain-residing pathogens.

microbiology↗

bioRxiv2026

Probabilistic Co-Control in Brain-Computer Interfaces: Uncertainty as a Control Signal in Brain-to-Text Decoding

Neural decoders serve as probabilistic interfaces in co-control brain-to-text BCIs, where predicted uncertainty shapes hypothesis generation and language model integration, enabling decisions to be made safely under uncertainty. However, it remains unclear whether these decoders produce reliable and informative uncertainty, or how training objectives shape these properties. This work characterizes and improves uncertainty representations in brain-to-text decoding. We extend two metrics, calibration error (ECE) and resolution (RES), to evaluate sequential probabilistic predictions from frame-level phoneme estimates to word-level hypotheses, quantifying the reliability and informativeness of model uncertainty. Using this framework, we analyze neural decoders trained with connectionist temporal classification (CTC). To isolate the causal role of uncertainty independent of accuracy, we manipulate predicted probability distributions while holding predicted sequences fixed. Motivated by the observed failures, we further examine the role of the training objective and propose a two-stage cross-entropy (CE) formulation that decouples alignment inference from classification. We show that widely used CTC-trained neural decoders in brain-to-text BCIs produce systematically over-confident predictions, with high confidence persisting even when predictions are incorrect. Controlled manipulations of the prediction reveal that improved ECE and RES enhance hypothesis generation and language-model integration by promoting diverse alternatives and more effective re-ranking of hypotheses aligned with user intent. Mechanistically, CTC relies on over-confident predictions to resolve alignment ambiguity. Replacing CTC with CE loss yields significantly more reliable and informative probabilistic predictions without degrading decoding accuracy. Uncertainty emerges as a system-level design variable in brain-to-text interfaces. Calibrated uncertainty from neural decoders enables effective integration with independently trained language models and reliable error detection. This work reframes uncertainty from a passive output into an active control signal, identifies key components and evaluation criteria for probabilistic co-control, and outlines a pathway toward next-generation BCIs that supports increasingly complex interactions with the world.

bioengineering↗

bioRxiv2026

Multicopper oxidase mediated single-carbon insertion for skeletal remodeling

Modern drug discovery demands efficient strategies for generating structurally diverse compound libraries. Skeletal editing--a transformative paradigm enabling precise atom-level modifications within molecular frameworks, offers a sustainable alternative to traditional synthetic routes. While carbene insertion-mediated approaches have dominated single-carbon insertion strategies, current methodologies are limited by their reliance on hazardous, unstable carbene precursors and harsh reaction conditions. Herein, we report a multicopper oxidase (MCO)-catalyzed skeletal editing that enables the direct, one-step transformation of phenolic and indole derivatives into functionalized tropones and quinoline analogues through exogenous single-carbon insertion. This platform employs stable and safe nitroalkanes as carbon sources and O2 as the sole terminal oxidant. It accommodates a broad substrate scope and yields products with superior antibacterial activity against to multidrug-resistant strains relative to their parent compounds. This work introduces the first biocatalytic platform for exogenous single-carbon insertion skeletal editing. This sustainable and scalable strategy overcomes key limitations of synthetic approaches, offering efficient skeletal remolding and rapid expansion of bioactive compound libraries. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/714988v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1f1d09borg.highwire.dtl.DTLVardef@b67c65org.highwire.dtl.DTLVardef@a0cd4dorg.highwire.dtl.DTLVardef@106afe5_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2026

The tumour suppressor RBM5 activates the helicase DHX15 to regulate splicing

Pre-mRNA splicing determines the expressed proteome and is frequently dysregulated in cancer. The tumour-suppressor RBM5 controls an exon network regulating apoptosis, yet its molecular mechanism is elusive. Using in vivo spliceosome capture and cryogenic electron microscopy, we determined structures of precatalytic spliceosomes arrested by RBM5 immediately after U2 snRNP branchpoint recognition. Despite intron diversity, the U2-pre-mRNA duplex, branchpoint adenine, and downstream polypyrimidine tract are well-resolved. RBM5 binds the outer SF3B1 HEAT surface and performs dual functions: First, its helix-loop-helix motif and upstream zinc-finger domain sterically block tri-snRNP and Prp8 docking and prevent progression to pre-B and Bact complexes; Second, its G-patch activates DHX15 and places this DExH-box helicase on the pre-mRNA as it exits SF3B1, poised for branch helix unwinding. DHX15 binding to SF3B1 is facilitated by U2SURP/SR140, which engages SF3B1 near RBM5s helix-loop-helix. Functional assays confirm that disruption of the RBM5 interfaces with either DHX15 or SF3B1 inhibit exon repression. Mutations at these regulatory interfaces are common in cancer genomes and predicted to disrupt its regulation of apoptotic isoforms. Thus, RBM5 acts as a dual-action spliceosome gatekeeper that couples helicase activation with physical stalling to enforce tumour-suppressive alternative splicing programmes.

molecular biology↗

bioRxiv2026

Cellular transcriptomics reveals evolutionary adaptation and rumination of vertebrate stomachs

Stomach, an essential digestive organ in vertebrates, has undergone significant changes in its morphology, chamber numbers, and physiology, in response to dietary diversity. However, its cellular and molecular basis underlying evolutionary adaptation remains largely uncharacterized. Here we report a single-cell and spatial transcriptomic atlas of stomachs from 23 vertebrate species with diverse feeding habits (e.g., omnivores, carnivores, and herbivores) and gastric architectures, including monogastric (e.g., primates), two- (e.g., aves), three- (e.g., camel) and four-chambered stomachs (e.g., sheep). We reveal conservation and divergence in cell-type composition, developmental trajectory, evolutionary origins and forces, spatial distribution, gene regulatory network and effect, metabolic signature and disease susceptibility associated with the feeding habits and stomach chamber. The diversification of plant based diets, particularly the expansion of coarse fibrous plants that required enhanced mechanical processing, driving the rapid evolution of specific cells and associated genes. Ruminants have evolved chamber-specific cell types with enhanced expressions of relevant functional genes (e.g., KRT6A in spinous cells of forestomach, LUC7L in SMCs of abomasum, and TSPYL4 in abomasal enteroendocrine cells), which were validated by fluorescence in situ hybridization and spatial transcriptomic profiling. The three cell-specific expressed genes showed significant effects on cell proliferation and migration by RNA interference to knock down their expressions. In particular, the knockdown of LUC7L in SMCs promotes a transition from a contractile to a synthetic phenotype, and Luc7l knock-out in mice delays gastric emptying and impairs gastric motility, demonstrating that this gene is essential for the coordinated multi-chambered gastric motility that underlies rumination in ruminants. These findings elucidate the cellular and molecular adaptations underlying the evolution of stomach coupled with the emergence of diet land plants. Our results provide potential cellular and gene targets for engineering monogastric animals to acquire functional rumination digestive capabilities as well as for medication of gastric mobility.

evolutionary biology↗

bioRxiv2026

SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data

Spatial transcriptomics data are largely available with RNA expression alone, limiting the detection of cell states defined by surface protein abundance. The lack of multi-omics spatial data limits the ability to identify immune cells and their signaling in the tumor microenvironment, as most solid tumors are immunologically poor and exhibit protein-RNA abundance discordance in critical immune cell surface markers. Although emerging technologies enable spatial multi-omics profiling, technical and cost constraints remain a hurdle. We introduce SR2P, a stacking-based machine-learning framework for predicting spatial protein abundance from RNA expression. SR2P integrates 11 complementary predictive models and consistently outperforms existing methods across multiple spatial multi-omics benchmark. We showcased an application of SR2P recovered macrophage-enriched regions and identified potential immune markers associated with therapeutic response from head-and-neck squamous cell carcinoma patients. SR2P enables protein-abundance inference from RNA-only spatial data, extending the analytical capabilities of current spatial platforms for studies of tumor immunology.

bioinformatics↗

bioRxiv2026

Deep-Palm：an integrated deep learning framework for structure-aware prediction of protein S-Palmitoylation

Protein S-palmitoylation is a reversible lipid modification that regulates protein localization, trafficking, and signaling. Its dysregulation has been implicated in cancer and therapeutic resistance, making accurate site annotation important for understanding disease-related regulatory mechanisms. However, experimental identification of S-palmitoylation sites remains labor-intensive, highlighting the need for computational tools that can support large-scale candidate-site prioritization. S-palmitoylation-site recognition requires the integration of multiple levels of information surrounding candidate cysteine residues, including sequence context, local protein properties and structural features. Here, we present Deep-Palm, a deep learning framework that integrates four complementary branches: amino acid sequence, physicochemical properties, ESM embedding and spatial structure. On the testing set, Deep-Palm achieved an AUC of 0.950 and outperformed the compared predictors, including pCysMod, MusiteDeep and GPS-Palm. Deep-Palm also showed stable performance across diverse cysteine sequence contexts and Gene Ontology functional groups. Feature-level analyses revealed distinct spatial structural and protein property patterns between palmitoylated and non-palmitoylated peptide windows. Independent mass spectrometry datasets further demonstrated the ability of Deep-Palm to sensitively identify previously unannotated S-palmitoylation sites. Together, Deep-Palm provides an accurate and biologically informative framework for S-palmitoylation-site prediction, facilitating the discovery of novel candidate S-palmitoylation sites.

bioinformatics↗

bioRxiv2026

The chromatin remodeling complex PRC2 safeguards cell fate in alveolar epithelial type 2 cells

Maintenance of the gas exchange surface throughout life and regeneration of the lung after injury requires tight regulation of epithelial cell fate and function. Alveolar epithelial type 2 (AT2) cells serve as the progenitors of the distal epithelium, differentiating into alveolar epithelial type 1 (AT1) cells or proliferating to maintain the quorum of AT2 cells. Here we describe the role of the chromatin regulator polycomb repressive complex 2 (PRC2) in the maintenance of AT2 cell fate in the adult alveolus. Cross-species single-cell transcriptomic analyses identified PRC2 activation in proliferative AT2 populations. PRC2 loss of function in human iPSC-derived AT2 (iAT2) cells and primary murine AT2 cells in vitro resulted in loss of AT2 cell state and emergence of programs reminiscent of alveolar-basal intermediate (ABI) cell states, while overexpression of the PRC2 enzymatic component EZH2 in human iAT2 cells augmented the AT2 cell program. Genetic loss of PRC2 function in the AT2 lineage in adult mice in vivo led to emphysematous remodeling of the lung and induced a time-dependent series of transitions of AT2 cells through an alveolar-basal intermediate (ABI) state into Krt5+ basal-like cells. Comparison of murine ABI cells to human disease-associated ABI cells demonstrates de-repression of canonical PRC2 targets during transition to ABI and basal-like states in human fibrosis, implicating PRC2 is a conserved regulator of AT2 cell fate. Together, these findings define PRC2 complex function during AT2 cell self-renewal as a critical guardrail for maintaining epithelial cell fate in the adult lung.

genetics↗

bioRxiv2026

Myristoylation licenses disordered viral VP4 protein to anchor to and perforate the membrane through phase separation

The VP4 protein of enteroviruses, such as Coxsackievirus B3, is a small, intrinsically disordered protein essential for perforating the host cell membrane during viral entry. A key feature of VP4 is its N-terminal myristoylation, which is required for infectivity in some enteroviruses but dispensable in others, suggesting a complex and context-dependent role that is not fully understood. The precise biophysical mechanisms by which this lipid anchor enables a disordered protein to breach a membrane remain unresolved. Here, using Coxsackievirus B3 VP4 as a model system and integrating multi-scale molecular dynamics simulations with confocal microscopy, we demonstrate that myristoylation is not a simple membrane tether but a multi-functional regulator that orchestrates VP4 activity through distinct, hierarchical roles. First, it provides the necessary hydrophobic anchor to recruit the disordered VP4 to the membrane interface. Second, the myristoyl group acts as a key molecular driver that promotes the liquid-liquid phase separation of VP4, leading to the formation of dynamic condensates on the membrane surface. These condensates actively remodel the membrane, generating substantial curvature that, in turn, lowers the free energy barrier for VP4 penetration. Furthermore, we find evidence that the myristoyl group plays a third role in stabilizing the final transmembrane pore. Our findings establish a novel paradigm where a single lipid modification empowers a disordered viral protein to form a functionally potent condensate that mechanically primes and physically breaches the target membrane, a mechanism that may explain the conditional myristoylation requirement across enteroviruses.

biophysics↗

bioRxiv2026

Opposing regulatory logics converge on ABA receptors to govern the trade-off between growth and drought acclimation

Plants dynamically balance growth with stress responses through abscisic acid (ABA). How ABA signaling, but how ABA sensitivity is finely tuned is unclear. Here, we uncover a regulatory network of leucine-rich repeat receptor-like kinases (LRR-RLKs) that directly controls ABA receptors stability. We identify GASSHO1 (GSO1) as a core component that phosphorylates the PYL2 and PYL4 receptors, targeting them for degradation and thus serving as a brake on ABA signaling. Under drought stress, ABA accumulates and suppresses expression of GSO1-activating CIF peptides, releasing this brake to increase PYLs and sensitivity. This mechanism opposes our previously identified CEPR2 pathway, where drought-induced CEP peptides inhibit the kinase to stabilize PYLs. The integration of these two antagonistic modules within an LRR-RLK network enables precise, dynamic control of ABA perception. This work reveals a molecular framework explaining how plants calibrate the critical balance between growth and drought acclimation.

molecular biology↗

bioRxiv2026

Surface Functionalized RBC Membrane-Derived Nanoparticles for Targeted Drug Delivery to Attenuate Fatty Liver Disease

Metabolic dysfunction-associated steatotic liver disease (MASLD) is marked by excessive hepatic lipid accumulation and is closely associated with hyperlipidemia. It poses significant health challenges and can progress to severe chronic liver disease if untreated. Several small-molecule pharmacological agents are either in clinical use (resmetirom) or advancing through preclinical development for the treatment of hepatic steatosis. However, some promising lead drug candidates have limited therapeutic potential due to poor solubility, low permeability, limited biocompatibility, and off-target effects. Cell membrane-derived nanoparticles (CMN), prepared from red blood cells, naturally exhibit immune-evasion properties and can overcome these limitations by encapsulating small molecules within their self-assembled structures. Further, CMN can be surface functionalized to enable precise targeting of liver hepatocytes. Here, we developed a hepatocyte-targeting CMN loaded with a model drug (resmetirom) for MASLD therapy. Using covalent bonds, we conjugated three different hepatocyte-targeting ligands to CMN and identified lactoferrin as the most effective ligand through comparative screening. We then confirmed the cellular internalization pathways of the selected ligand in both targeted CMN and non-functionalized CMN. Finally, in an in vitro hepatic steatosis model, the optimized targeted CMN demonstrated improved bioactivity, including significant reductions in lipid droplets, triglycerides, and liver enzyme levels. Altogether, this targeted CMN platform shows promising potential to enhance the therapeutic efficacy of small-molecule drugs for MASLD and may, overall, improve therapeutic outcomes in preclinical and clinical trials.

bioengineering↗

bioRxiv2026

HDAC1/2-mediated repression of Wnt receptor expression orients asymmetric division polarity in C. elegans.

Asymmetric cell division generates distinct daughter cells essential for tissue development, yet the mechanisms orienting division polarity within tissues remain incompletely understood. Here, we uncover a role for chromatin-mediated transcriptional repression in controlling polarity orientation during asymmetric division of C. elegans epidermal stem cells, known as the seam cells. We show that tissue- specific loss of the class I histone deacetylase hda-1, homologous to mammalian HDAC1/2, causes reversals in division polarity and reduces molecular asymmetry between daughter cells. Using Targeted DamID to profile HDA-1 genomic occupancy, we identify the Wnt receptors lin-17/Frizzled and cam-1/Ror as key targets. Single- molecule FISH reveals that these receptors display striking expression gradients along the body axis, with cam-1 enriched anteriorly and lin-17 posteriorly. In hda-1 mutants, both receptors are upregulated so that differences between seam cells are flattened. Overexpression of either receptor alone is sufficient to reproduce the polarity reversals observed in hda-1 mutants, and their co-overexpression produces additive defects. The polarity phenotype is independent of the canonical NuRD and SIN3 complexes, suggesting that HDA-1 acts through an alternative mechanism to regulate Wnt receptor expression. Our findings establish a direct link between histone deacetylase activity and Wnt receptor expression and support a model in which graded expression of Wnt receptors provides positional cues that orient asymmetric division polarity in response to Wnt signals.

developmental biology↗

bioRxiv2026

Leveraging Large Language Models to Extract Prognostic Pathology Features in Ewing Sarcoma

BackgroundCurrent risk stratification for Ewing sarcoma relies heavily on clinical factors such as metastatic status, failing to capture histologic heterogeneity as a potential prognostic indicator. Although pathology reports contain rich biological data, this information remains locked in unstructured narrative text, limiting large-scale retrospective analyses. We aimed to validate the utility of Large Language Models (LLMs) for scalable data abstraction and to identify prognostic histologic features from a large multi-institutional cohort. MethodsWe conducted a retrospective cohort study using data from six Childrens Oncology Group (COG) clinical trials. We utilized an LLM-based pipeline (OpenAI o3) to extract structured variables--including immunohistochemical (IHC) markers and CD99 staining patterns--from digitized, Optical Character Recognition (OCR)-processed pathology reports. Extraction accuracy was validated against a human-annotated ground truth (n=200) and cross-validated against senior experts (n=48). We assessed the association between extracted features and Overall Survival (OS) using Kaplan-Meier analysis and multivariable Cox proportional hazards regression, adjusting for metastatic status. FindingsWe analyzed 931 diagnostic pathology reports from 185 institutions spanning over 21-years. The LLM achieved a weighted average accuracy of 94% across 17 IHC markers; in a cross-validation subset, the LLM outperformed human annotators (weighted average accuracy over 15 IHC markers: LLM o3: 98.1%, a pediatric resident 91.4%, and a pediatric oncologist 95.9%). Survival analysis identified Neuron-Specific Enolase (NSE) and S100 as significant prognostic biomarkers. After adjusting for metastatic status, NSE positivity was associated with significantly inferior survival (HR 2.15, 95% CI 1.15-4.02, p=0.016); this risk was most pronounced in patients with non-metastatic disease (HR 5.64, p=0.0055). Conversely, S100 positivity was associated with improved survival (HR 0.58, 95% CI 0.34-1.00, p=0.046). InterpretationLLM-assisted extraction of pathology variables is highly accurate and scalable, capable of unlocking "dark data" from historical clinical trials. We identified NSE as a potent risk factor and S100 as a protective marker in Ewing sarcoma, particularly in localized disease. These findings suggest that AI-derived histologic data can refine risk stratification and, if validated, warrant inclusion in future prospective trials. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and Google Scholar for articles published up to December 2025, using terms such as "Ewing sarcoma", "prognosis", "risk stratification", "large language models", "natural language processing", and "pathology report extraction". Current risk stratification for Ewing sarcoma relies predominantly on clinical variables, specifically the presence of metastatic disease and tumor size. While histologic heterogeneity is well-documented, it is rarely incorporated into risk models because extracting structured data from narrative pathology reports is labor-intensive. Small, single-institution studies have suggested potential prognostic roles for markers like Neuron-Specific Enolase (NSE) or S100, but results have been inconsistent and limited by small sample sizes. Furthermore, while Large Language Models (LLMs) have demonstrated potential for extracting clinical data, their application to "rescue" data from legacy, multi-institutional clinical trial documents (scanned PDF images) for rare disease biomarker discovery has not been validated at scale. Added value of this studyTo our knowledge, this is the largest study to utilize AI to extract histologic data from Ewing sarcoma pathology reports, aggregating 931 patients from six distinct Childrens Oncology Group (COG) clinical trials spanning over 21 years. We validated an LLM-based pipeline that converted noisy, optically character-recognized text from diverse institutions into structured data with 98.1% accuracy, outperforming human annotators. This scalable approach revealed that NSE positivity is a significant independent risk factor for mortality (HR=2.15), particularly in patients with non-metastatic disease where it confers a more than 5-fold increased risk of death. Conversely, we identified S100 positivity as a protective factor associated with improved survival (HR=0.58). Implications of all the available evidenceThis study demonstrates that LLMs can reliably unlock "dark data" from historical clinical trials, rendering vast archives of unstructured medical documents accessible for retrospective analysis. The identification of NSE and S100 as robust prognostic biomarkers suggests that these widely available immunohistochemical stains provide valuable information beyond standard diagnostic information. These findings support the integration of automated data extraction tools in research workflows and suggest that NSE and S100 status should be considered in the design of future risk-stratified clinical trials for Ewing sarcoma.

bioinformatics↗

bioRxiv2026

Histone H2BK120 ubiquitination modulates PRC1 activity and H2AK119ub deposition on nucleosomes

Polycomb group and Trithorax group complexes antagonistically regulate developmental gene expression, yet the mechanisms coordinating their interplay remain poorly understood. Here, we uncover a synergistic histone crosstalk in which H2BK120ub (histone H2B ubiquitination at lysine 120), a Trithorax-associated active mark, enhances Polycomb repressive complex 1 (PRC1) activity by directly binding specific PCGF subunits of PRC1 on the nucleosome surface, as shown in cryo-electron microscopy (cryo-EM) analysis. Beyond regulating enzymatic activity, H2BK120ub also modulates the competitive binding between the catalytic RNF2-BMI1 module and the RYBP subunit of noncanonical PRC1.4 complexes to nucleosomes, potentially restricting H2AK119ub (histone H2A ubiquitination at lysine 119) spreading to neighboring nucleosomes. In mouse embryonic stem cells, H2BK120ub directs the deposition of H2AK119ub within the gene bodies of a subset of developmental genes, which exhibit more rapid activation compared to canonical H3K4me3/H3K27me3 bivalent genes. These findings reveal a previously unrecognized mechanism that links active and repressive histone modifications, providing new insights into the intricate chromatin regulation of gene expression during development.

molecular biology↗