Biology subjects

Bao, Y.

Publications and source records attributed to Bao, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Causal event structure shapes convergent reactivation signatures in human cortex and language model

Humans adapt to changing environments by understanding the latent causal structure of events, and modern large language models (LLMs) increasingly exhibit analogous abilities. However, elucidating how causal knowledge is represented and integrated in the human brain and in LLMs poses a substantial challenge for cognitive science. To address this, we combine topological analysis of representational manifolds with neuroscientific analyses of reactivation. We find that event-level causal relations modulate temporally structured neural reactivation in the human brain, particularly in the angular gyrus, precuneus and prefrontal cortex. By contrast, LLMs express these relations through positionally structured representational reactivation. Despite these differences, the resulting reactivation patterns exhibit similar intrinsic manifold structure across the two systems. Together, these findings suggest that the human brain and LLM share a common principle for organizing causal knowledge.

neuroscience↗

bioRxiv2026

A druggable chromatin vulnerability in suppressive neutrophils restores antitumor immunity

Many cancers evade immunity by converting neutrophils into polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs), but the chromatin mechanisms that maintain this pathological state remain unclear. Here we show that tumor-induced suppressive neutrophils depend on mSWI/SNF chromatin remodeling. Two orally bioavailable mSWI/SNF ATPase antagonists, a degrader and a catalytic inhibitor, produced immune-dependent tumor control across syngeneic models, including tumors resistant to PD-1 blockade, and enhanced checkpoint therapy without overt toxicity. Treatment rapidly depleted intratumoral PMN-MDSCs, restored functional CD8+ T cells, and blocked tumor explant-induced expansion and suppressive activity of mouse and human PMN-MDSCs. mSWI/SNF antagonism collapsed chromatin accessibility and PU.1/C/EBP{beta} occupancy at regulatory elements that sustain PMN-MDSC identity. Thus, suppressive neutrophils harbor a druggable chromatin vulnerability that can be exploited to restore cancer immunity.

cancer biology↗

bioRxiv2026

Folic acid polyglutamylation triggers MAP2K5 signaling to induce adipocyte lipid catabolism

Adipocyte signaling adaptively responds to macronutrients, but how essential micronutrients impact lipid homeostasis remains poorly understood. Here, we demonstrate that folate polyglutamylation, the sequential conjugation of glutamate residues to folate, serves as a dynamic biochemical process regulating metabolic signaling. Using our folate metabolomics platform, we show that polyglutamylated folic acid accumulates in healthy adipose tissue, but is depleted in obesity across mice and humans, independent of circulating folate levels. Genetic ablation of the polyglutamylation enzyme folylpolyglutamate synthase (Fpgs) in adipocytes suppresses lipid catabolism to induce cell-autonomous lipid accumulation, operating independently of canonical adipogenesis or downstream one-carbon flux. Target-engagement proteomics identifies monoglutamylated folic acid as a Map2k5-interacting molecule that inhibits Map2k5 activity, whereas Fpgs-mediated folic acid polyglutamylation acts as a chemical switch that disrupts this interaction to promote lipolysis. In vivo, whole-body inhibition of Map2k5 or adipose-targeted genetic depletion of Fpgs increases body fat mass in the absence of dietary obesogenic triggers. Furthermore, single-cell and single-nuclear transcriptomic analyses establish the Fpgs-Map2k5 pathway as a core transcriptional signature of mouse and human adipose tissues. These findings uncover a non-canonical signaling role for folate that regulates adipocyte lipid homeostasis.

biochemistry↗

bioRxiv2026

Catch-bond engineering surpasses high-affinity maturation for T cell receptor therapies against solid tumors

The sensitivity of T cell receptors (TCRs) has traditionally been attributed to their affinity, a principle that has long underpinned both T cell biology and TCR engineering. Currently, high-affinity maturation remains the predominant strategy employed to enhance TCR sensitivity; however, this approach has been associated with severe off-target toxicities in clinical settings. In this study, using the only FDA-approved TCR-T therapy as a clinical reference, we demonstrate that force-induced catch bonds, rather than static affinity, primarily determine TCR sensitivity and facilitate the development of more effective TCR engineering strategies. Among polar and charged amino acids, histidine was determined to be the most effective residue for pinpointing engineering hotspots. TCRs engineered to form catch bonds exhibited superior performance compared to the FDA-approved TCR subjected to high-affinity maturation, improving the efficacy of TCR-T cell therapies against solid tumors without eliciting off-target toxicity or alloreactivity. Mechanistically, a correlation was observed between TCR sensitivity and the strength of catch bonds, whereas no such relationship was found with affinity. The duration of T cell-tumor cell interactions, immunological synapse formation, and the intensity of subsequent signaling are governed by catch bonds rather than by affinity. Structural and computational investigations have demonstrated that force-induced reconfiguration of the ligand-receptor interface, along with the formation of a novel hydrogen bond network, augments the specificity of interactions between the TCR and antigenic peptides. Furthermore, bispecific T cell engagers utilizing the TCR scaffold also formed catch bonds with tumor antigens, indicating intrinsic mechanosensory properties of the extracellular domains of TCRs and suggesting a novel therapeutic modality based on catch-bond-engineered TCRs.

immunology↗

bioRxiv2026

Dynamic Changes in the Urinary Proteome of Normal Pregnant Women and Their Correlation with Fetal Developmental Progression - A Methodological Exploration Based on "One-versus-Many" Urinary Proteomic Comparative Analysis

This study employed a "one-versus-many" (a single pregnant woman compared with multiple non-pregnant women) urinary proteomic comparative framework to examine whether fetal development-related signals can be captured through changes in the urinary proteome under conditions of limited sample size. The experimental group consisted of urinary proteomic data from three women with normal pregnancies (R6, R15, R16) at three gestational time points ([~]6-8 weeks, 22-24 weeks, and 32-34 weeks; Wang et al., 2022), while the control group consisted of urinary proteomic data from six healthy non-pregnant women (Bao & Gao, ChinaXiv: 202302.00108v2). A total of nine independent "1 vs. 6" differential protein analyses and DAVID GO Biological Process enrichment analyses (P < 0.05) were performed. The results showed that all three pregnant women exhibited a large number of differentially expressed proteins at each time point, and all enriched GO BP terms highly relevant to concurrent fetal organ development (nervous system, lung, eye, ear, kidney, etc.). This suggests that the pregnancy urinary proteome can reflect fetal development signals, corroborating the findings reported by Wang et al. (2025) in a rat model. This study demonstrates that the one-versus-many comparative approach maintains high sensitivity under small-sample conditions and can provide a methodological reference for personalized pregnancy medicine.

biochemistry↗

bioRxiv2026

OCellus: A Language-Model Framework for Single-Cell, Spatial, and Perturbation Biology with Natural-Language Reasoning

Computational modeling of cellular behavior--the virtual cell--has emerged as a stated grand challenge at the intersection of artificial intelligence and biology, yet existing foundation models remain specialized: single-cell models process dissociated transcriptomes only, spatial models require dedicated spatial-aware architectures, and perturbation predictors depend on manually curated knowledge bases that cap generalization. Here we introduce OCellus, a single nine-billion-parameter language model (Qwen3.5-9B) fine-tuned on twenty-two biological tasks that simultaneously addresses all three limitations through three coordinated technical contributions on a shared backbone. First, EvenClock encodes two-dimensional spatial coordinates as eighteen clockface sectors of text, enabling spatial reasoning on a vanilla language model without architectural modification; on ten spatial transcriptomics tasks OCellus attains 77 percent spatial-neighborhood accuracy, 96 percent spatial-cellchat accuracy, and 0.70 proportion-cosine similarity on spatial deconvolution, all without any spatial-aware architectural components. Second, per-gene language-model embeddings replace the Gene Ontology annotations that GEARS depends on, achieving Pearson correlation 0.945 on the Replogle 2022 perturbation benchmark versus 0.84 for GEARS across 457 completely unseen knockout genes. Third, OCellus-Agent provides a Planner-Router-Verifier natural-language interface that achieves 75 percent pipeline accuracy on eighty multi-task queries. Removing language-model embeddings collapses perturbation Pearson to 0.06, confirming that learned functional representations--not graph topology--drive the gain. As a cell-type encoder, OCellus ranks first among fourteen foundation models in linear-probe accuracy at 95.1 percent across four benchmark datasets, and reaches 72.6 percent average across twenty-two evaluated biological tasks--a 57-percentage-point absolute gain over the strongest baseline configuration. As a language model, OCellus uniquely generates natural-language explanations of its predictions, a capability absent from all competing methods. Code, pre-trained model weights, the graph-neural-network module, and the agent system will be made available upon publication.

bioinformatics↗

bioRxiv2026

FCRL5 is a fucose-sensitive IgG-Fc receptor with binding properties distinct from classical Fcγ receptors

Fc receptor-like protein 5 (FCRL5) is a low-affinity IgG receptor expressed on B cells, with emerging therapeutic relevance due to its expression on multiple myeloma cells, and a potential role in regulating B cell responses. Previous reports on the FCRL5-IgG interaction vary widely in reported affinities, binding differences across IgG subclasses, and molecular requirements for maximal binding. Furthermore, the impact of Fc-engineering strategies, as used in (therapeutic) monoclonal antibodies, remains poorly understood. Here, we provide a comprehensive biochemical analysis of the FCRL5-IgG interaction. We demonstrate that FCRL5 is a true IgG Fc-receptor, binding with very low affinity (60-80 {micro}M). FCRL5 binds IgG in a manner involving primarily the two N-terminal domains of FCRL5, and the third domain for maximal binding, but with distinct essential residues in the IgG Fc-tail. Surface plasmon resonance analysis of the binding of FCRL5 to the various IgG subclasses revealed a preference for IgG1 and IgG4. Interestingly, various Fc-engineered IgG variants commonly used for silencing or enhancing of Fc receptor binding do not impact FCRL5 binding. Screening the binding of a set of IgG antibodies carrying defined sets of Fc-mutations to FCRL5 revealed E293 as a key binding determinant and led to the discovery of E293R as a mutation that selectively abrogates FCRL5 binding while preserving binding to other classical Fc{gamma}Rs. Lastly, we show that FCRL5 has considerable preference for binding afucosylated IgG. Together, our results define the essential characteristics of the IgG-FCRL5 interaction and demonstrate the potential of both naturally occurring IgG variants as well as therapeutically explored bioengineered IgG formats to differentially engage FCRL5.

immunology↗

bioRxiv2026

First community challenge for automated virus taxonomy

The rapid rate of virus discovery renders manual curation by taxonomy experts increasingly impractical, creating a need for reliable software that can reproducibly assign viral contigs to taxa at all fifteen ranks of the virus taxonomy. We led an open community challenge for the computational taxonomic classification of viruses and assembled a dataset of virus sequences combining expert-curated and metagenomic sequences. Seventeen teams contributed a total of thirty-four automated, fully reproducible classification pipelines. Most tools correctly assigned viruses belonging to established species, genera, or families, but viruses that are unclassified at those lower ranks remain challenging. This study provides datasets, open-source software, novel approaches, and recommendations to benchmark computational taxonomic classification of viruses, and support organizing the many viruses discovered in big omics data.

microbiology↗

bioRxiv2026

HiCPotts: An R/Bioconductor package to identify significant interactions in chromosome conformation capture data and model sources of biases.

MotivationChromosome Conformation Capture methods, including Hi-C, micro-C or Capture-C, are used to map chromatin interactions genome-wide. Most of the existing computational methods do not account for sources of biases (such as DNA accessibility, GC content or TE content) in the data. ResultsWe previously developed ZipHiC, a Bayesian method based on a the hidden Markov random field (HMRF) model and the Approximate Bayesian Computation (ABC), that uses zero-inflated Poisson distribution to model the noise, signal and false signal of the data and showed that this approach was able to detect biases from DNA accessibility, GC content and TE content in both Hi-C and micro-C data. Here, we present HiCPotts, another Bayesian method based on the HMRF model and the ABC that uses a zero-inflated Negative Binomial distribution instead to model the noise and signal of the data. We systematically show that HiCPotts reduces false positives and increases recovery of true interactions compared to ZipHiC, but also compared to other methods such as FastHiC, Juicer and HiCExplorer. Most importantly, we provide an R/Bioconductor package that allows modelling the noise, signal and false signal using various distributions such as the zero-inflated Negative Binomial (ZINB) and the zero-inflated Poisson distribution (ZIP). Availabilityhttps://bioconductor.org/packages/HiCPotts/

bioinformatics↗

bioRxiv2026

Large language model inference of macromolecular complex composition via model consensus and experimental data integration

Large language models (LLMs) are poised to reshape how biologists retrieve specialized knowledge at scale. Yet their performance on deep, domain-specific queries is poorly defined because much biological information resides in structured databases or large experimental datasets rather than in a free text format. One such gap in cellular biology lies in identifying major macromolecular complexes, conserved biological units essential to many cellular processes. Cataloging large complexes, such as the ribosome or RNA polymerase, along with their constituent genes, presents a significant challenge for LLMs because of their tendency to hallucinate and to produce incomplete or inconsistent lists of components. Here, we systematically evaluate six state-of-the-art LLMs on the task of retrieving the gene components of 91 protein complexes and develop an integrative framework that combines LLM output consensus with experimental multi-omics data to reconcile and filter model responses. We found that two extensions of a basic single-LLM baseline, (i) aggregating LLM outputs into a consensus and (ii) integrating LLM predictions with the experimental data, each improved retrieval accuracy. Furthermore, a consensus of LLM outputs integrated with the incomplete experimental data using a graph-theoretic approach achieved the highest accuracy (F1 score of 82.5%), compared to the best stand-alone singe LLM (F1 score of 76.4%). These results show that optimized integration of predictions from multiple LLMs and high-throughput experimental data can support scalable, semi-automated curation of specialized biological resources, providing a general template for benchmarking and deploying LLMs for the structured knowledge retrieval tasks in molecular biology.

bioinformatics↗

bioRxiv2026

ppGpp regulates transcription elongation via direct and indirect inputs to RNA polymerase pausing and nucleotide addition

The signaling molecules guanosine 5'-tri/diphosphate 3'-diphosphate, (p)ppGpp, control bacterial protein synthesis rates and cell growth by targeting transcription, translation, NTP synthesis, and other functions. In lineages like E. coli, (p)ppGpp produced in response to charged-tRNA deficiency directly targets transcribing RNAP polymerase (RNAP) to match its pace to the pioneering ribosome on the nascent RNA (transcription-translation coupling). However, the mechanism by which (p)ppGpp slows RNAP is poorly defined. (p)ppGpp may allosterically stimulate RNAP pausing, inhibit catalysis, promote backtracking, compete for substrate GTP, inhibit GTP synthesis, or uncouple transcription-translation by inhibiting translation. Using a combination of cryo-EM, biochemical assays, and quantitative nascent elongating transcript sequencing (qNET-seq), we establish that (p)ppGpp allosterically regulates pausing and nucleotide addition via distinct motions of the RNAP swivel module and both competes with and lowers GTP in vivo. (p)ppGpp stimulates swiveling at pause sites to delay escape but may also inhibit counter-swiveling required in every round of nucleotide addition. HighlightsO_LIppGpp biases RNAP toward swiveling and away from a catalytically-competent state C_LIO_LIppGpp effects on RNAP conformation explain ppGpp stimulation of RNAP pausing C_LIO_LIppGpp stimulation of RNAP pausing in vivo is mediated mainly by reduced GTP levels C_LIO_LIppGpp may allosterically slow RNAP and increases gene occupancy in vivo C_LI

molecular biology↗

bioRxiv2026

RNAP-seq: in vitro genome-scale transcription reveals preferential RNA polymerase pausing on Clostridioides difficile antisense DNA

Mechanistic dissection of gene transcription and its regulation in diverse organisms remains a central challenge for molecular biology. In vivo analyses using high-throughput DNA sequencing readouts (e.g., RNA-seq, Term-seq, and NET-seq) have revealed that transcriptional pausing by RNA polymerase (RNAP) underpins the regulation of gene transcription. However, these in vivo methods are difficult to apply in non-model organisms and lack the power of bottom-up biochemical analysis that can deconvolute the confounding effects of the cellular environment. Here we report a method for unbiased transcription of entire genomes in vitro called RNAP-seq, which is capable of defining pause, arrest, and termination sites with single-nucleotide resolution and of defining the regulatory contributions of cis-acting RNA and DNA sequences and dissociable transcription factors with complete control over transcription conditions. Using RNAP-seq to compare transcription by recombinant RNAPs from Clostridioides difficile (Cdf) and Escherichia coli (Eco), we discovered that CdfRNAP exhibits pausing signatures distinct from EcoRNAP. CdfRNAP pauses more frequently and more strongly at T-rich sequences, particularly on antisense regions of genes. We also defined the action of CdfNusG at genome scale, revealing its preferential action in riboswitch control regions. These findings demonstrate lineage-specific pausing features and suggest that genome composition and RNAP co-evolve to shape gene expression. RNAP-seq is broadly adaptable to diverse bacterial species and offers a powerful framework for uncovering fundamental principles of transcription that govern gene expression. HighlightsO_LIRNAP-seq enables unbiased transcription of every base-pair in an organisms genome C_LIO_LIRNAP-seq reveals distinct E. coli (Eco) and C. difficile (Cdf) pause sequences C_LIO_LICdfNusG broadly enhances pausing and termination, notably in riboswitch-encoding DNA C_LIO_LICdfRNAP but not EcoRNAP pauses more on the T-rich antisense strand of Cdf genes C_LI

genomics↗

bioRxiv2026

Cerebellar Microcircuits and Cortico-Cerebellar Cooperation Enable Robust Decisions

The cerebellum is increasingly implicated in perceptual decision-making, yet how cerebellar circuits-- without prominent recurrent excitation and slow reverberant loops--could support sparse evidence accumulation over behavioral timescales remains unclear. We present a biologically constrained modeling framework showing that cerebellar microcircuits can implement graded accumulation and competition without cortical-like excitatory recurrence. Type-II Purkinje-neuron excitability generates firing-rate hysteresis that prolongs the impact of brief inputs far beyond intrinsic membrane and synaptic time constants, enabling accumulation across long inter-event intervals. Purkinje neuron collateral inhibition produces competitive divergence and tunes temporal evidence weighting, revealing a trade-off between commitment and primacy bias. In a bidirectionally coupled cortico-cerebello-cortical model, cerebellar processing reduces primacy while cortical processing reduces indecision, improving robustness. Together, these results propose a mechanistic division of labor that positions the cerebellum as an active computational partner in perceptual decisions.

neuroscience↗

bioRxiv2026

Combination of engineered cell type-specific promoters and a high-efficiency AAV capsid restores hearing in adult DFNB1 mice model with demonstrated safety in nonhuman primate

A major challenge in gene therapy for GJB2-related hearing loss (DFNB1)--the most common form of hereditary deafness--is achieving efficient and precise connexin 26 delivery. Herein, we engineered two cell type-specific promoters (GJB2-1 and WFS1-2274) and developed an AAV capsid, AAV-MAS012, with enhanced transduction efficiency in mature cochlear cells. Our AAV-mediated gene therapy systems restored hearing of low-to-mid-frequencies in newborn Gjb2 cKO mice to wild-type levels and maintained for 45 weeks. Additionally, our therapeutic systems restored low-to-mid-frequencies hearing function to wild-type levels in adult Gjb2 cKO mice. A humanized version of the therapy, AAV-MAS012-WFS1-2274-hGJB2, rescued hearing function in two distinct Gjb2-deficient mouse models, and demonstrated a favorable safety profile in nonhuman primates. This study represents the first successful hearing restoration in adult Gjb2-deficient mice. The significant therapeutic efficacy of the humanized gene therapy system shows great potential for clinical translation in DFNB1 patients.

genetics↗

bioRxiv2026

Balancing Inhibition and Sparsity for Stable, Accurate Cerebellar Learning

The cerebellums structured circuitry supports learning across motor and cognitive domains, yet the coding strategies in granule cells that enable this versatility remain unclear. Using a theoretical cerebellar model, we examined how feedforward inhibition (FFI) and feedback inhibition (FBI) shape granule cell activation patterns to optimize learning in two tasks: complex trace learning and pattern identification. For trace learning, performance depends on spatiotemporally ordered granule cell activity shaped by FBI, with temporal sparsity emerging as the key determinant of accuracy. For pattern identification, both pathways support high accuracy, with only slight sensitivity to pathway choice. In both tasks, spatial sparsity becomes critical in incremental learning to prevent memory interference, a role reinforced by advanced synaptic plasticity strategies. These findings identify sparse granule cell activation as a unifying principle for cerebellar learning and reveal task-dependent roles of inhibitory pathways, providing a mechanistic framework for understanding stability-plasticity trade-offs in cerebellar learning.

neuroscience↗

bioRxiv2026

Meningeal neutrophil infiltration drives inflammation-exacerbated pediatric stroke through IL-36γ signaling

Infection and systemic inflammation have been identified as major risk factors for pediatric stroke; however, the immune mechanisms underlying these clinical observations remain poorly understood. Here, we utilized an LPS-sensitized photothrombotic stroke model (LPS/PT) to investigate the contribution of immune cell infiltration to pediatric stroke pathogenesis and identified a previously unrecognized route of neutrophil entry into the injured brain. Compared with photothrombotic stroke alone (PT), LPS/PT mice exhibited markedly increased neutrophil infiltration accompanied by a hyperactivated phenotype. Depletion of neutrophils, but not monocytes, significantly reduced infarct size in LPS/PT animals, indicating a central role for neutrophils in inflammation-exacerbated pediatric stroke. Notably, we observed that neutrophils first accumulated within the leptomeningeal space before entering the brain parenchyma during the early phase of stroke, suggesting that the meninges may serve as an initial staging site for neutrophil recruitment. Using KikGR photoconvertible reporter mice combined with two-photon imaging, we further demonstrated that neutrophils infiltrate the ischemic brain through a compromised meningeal barrier following stroke. Transcriptomic analysis of infiltrating neutrophils revealed distinct gene expression signatures between meningeal neutrophils and circulating blood-derived neutrophils. Among these, IL-36{gamma} was highly enriched in meninges-associated neutrophils during pediatric stroke. Consistently, single-cell RNA sequencing of meningeal immune cells confirmed elevated IL-36{gamma} expression within the neutrophil cluster in LPS/PT animals. Importantly, intracisternal administration of IL-36 receptor antagonist (IL-36Ra) or anti-ICAM-1 antibody significantly reduced infarct volume in this pediatric stroke model. Together, our findings identify the meningeal barrier as a critical gateway for neutrophil infiltration and reveal IL-36{gamma} as a key inflammatory mediator regulating neuroinflammation in pediatric stroke, highlighting a potential therapeutic target for limiting immune-mediated brain injury.

neuroscience↗

bioRxiv2026

Beyond gene length: Exon-intron architecture and isoform potential in the evolution of eukaryotic complexity

Alternative splicing is a major source of human transcriptomic and phenotypic variation, yet its evolutionary contribution to genomic complexity remains unresolved. It has been shown that a mean gene length can be the most basic but remarkably efficient proxy for multicellular genome complexity, whereas mean protein length is not, as it plateaus abruptly early in eukaryotic evolution. Here, we show that, across 2,683 genomes, exon count continues to increase beyond this transition and then rapidly saturates at [~]10 exons per gene, supporting its role as an additional dimension of genomic complexity linked to exon-intron architecture. A simple stochastic exon-splitting model reproduces the observed biphasic exon-number growth pattern and identifies minimal exon length as a key determinant.

evolutionary biology↗

bioRxiv2026

MATR3 is essential for oocyte growth and maturation quality through a dual molecular mechanism

The molecular mechanisms governing mRNA accumulation during oocyte growth, essential for developmental competence, remain poorly understood. This study investigates the role of Matrin-3 (MATR3), a highly expressed RNA-binding protein in growing oocytes (GOs), using oocyte-specific knockout mouse models and human oocyte maturation arrest (OMA) samples. The results showed that MATR3 was more abundant in GOs than fully-grown oocytes (FGOs), highly expressed in the nucleus of non-surrounded nucleolus (NSN) oocytes, and exited the nucleus during the NSN-to-surrounded nucleolus (SN) transition. In OMA patients, MATR3 nuclear localization was missed, with smaller oocytes than FGOs. Further, Matr3 deletion in mouse GOs caused restricted oocyte growth, global transcription disorders, follicle development failure, blocked GO-granulosa cell communication (via reduced Gdf9 and Radixin expression), and infertility. Mechanistically, MATR3 regulated transcription by recruiting H3K9me2-demethylating lysine-specific demethylase 3B or binding target gene promoters, like Radixin. These findings reveal a critical role of MATR3 in orchestrating transcription and paracrine signaling during oogenesis and suggest its potential as a diagnostic and therapeutic target for OMA.

physiology↗