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Pan, Y.

Publications and source records attributed to Pan, Y..

At least 55 records · Page 3Linked to original sources
bioRxiv2026

Intratumoral expression of JAML on NK cells is controlled by tumor microenvironment and MHC class I interaction

Junctional adhesion molecule-like (JAML) is an adhesion molecule known to promote T cell activation and T cell-mediated tumor rejection. In the current study, we show that JAML expression is enriched on mouse intratumoral NK cells compared with splenic NK cells. JAML+ NK cells were associated with tissue residency and co-expressed the immune checkpoints PD-1 and LAG3. JAML expression could be induced on splenic NK cells by IL-2 and further enhanced by IL-21. JAML levels were inversely correlated with inhibitory signaling, as NK cells expressing self-recognizing Ly49 receptors had reduced JAML expression, suggesting regulation of JAML expression by MHC class I molecules. Interaction with the JAML ligand CXADR also reduced JAML surface expression, indicating that tumor-mediated membrane stripping may represent a mechanism of immunoediting. Although JAML RNA transcripts were detectable in human NK cells, JAML protein was found only intracellularly. Together, these findings identify the JAML-CXADR interaction as a potential regulatory pathway in NK cell-mediated killing of tumors.

immunology↗
bioRxiv2026

Deep mapping of structural perturbations to energetics enables precise TCR design

Affinity optimization and cross-reactivity profiling are pivotal for T-cell receptor (TCR) engineering and immunotherapy, yet remain hindered by the vast diversity of the TCR repertoire and limited structural insights. Here, we present mpTCRai, a deep learning framework that captures the sequential characteristics of antigen presentation and recognition with high precision. Leveraging these precise structural predictions, we established a contact hotspot-based scoring mechanism that explicitly maps structural perturbations to energetic changes, yielding a strong correlation with experimental affinity (r = -0.88). In application, the model identified critical molecular switches, such as specific G-to-A substitutions, and revealed their dependence on structural context. Furthermore, it effectively distinguished functional variants from dangerous cross-reactive mutations like Y5W, thereby mitigating the risk of off-target toxicity. Guided by these insights, we computationally designed four novel A6-TCR variants, demonstrating a rational strategy for candidate selection in adult T-cell leukemia. This work establishes a unified platform integrating structural and energetic constraints to advance precise, safe TCR therapeutic design.

immunology↗
bioRxiv2026

FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) integrate genome wide association studies with expression quantitative trait locus reference panels to identify genes associated with traits of interest. However, linkage disequilibrium and correlated gene expression can induce spurious TWAS signals, motivating fine mapping methods to prioritize putatively causal genes within associated loci. The rapid growth of large-scale phenomic resources (e.g. electronic health records (EHRs)) has shifted genetic studies from single-trait analyses to phenome-wide investigations that jointly evaluate many closely related phenotypes. We introduce FM-GPT (Fine-mapping of causal Genes for Phenome-wide Transcriptome-wide association studies), a novel Bayesian fine mapping method for prioritizing causal genes across multiple correlated phenotypes with potentially mixed outcome types (e.g., binary, count or continuous) in phenome-wide TWAS. FM-GPT performs gene-guided dimension reduction of the phenotypes and reveals pleiotropic or phenotype-specific effects of the identified genes. In simulations, FM-GPT identified true causal genes more accurately than other fine mapping methods while controlling false positives. We applied FM-GPT to two applications using data from UK Biobank: a brain-wide genetic analysis of MRI data derived regional cortical thickness measures and a phenome-wide genetic analysis of clinical phenotypes derived from EHR data. FM-GPT greatly narrowed down the set size of putatively causal genes and identified: 1. genes with pleiotropic effects on regional cortical thickness across the cerebral cortex, including five genes BCAS3, LRRC37A, NOS2P3, ARL17B and UBB on chromosome 17 regulating neuronal morphology and cortical organization; and 2. genes that influence multiple medical conditions across the circulatory, metabolic, digestive, respiratory and genitourinary systems, revealing two major axes of variation among these conditions that point to a potential trade-off in gene regulation between immune and metabolic functions. These results highlight FM-GPTs power to disentangle complex gene-phenotype relationships in large-scale phenome-wide studies, uncovering shared biological mechanisms across diverse human traits and advancing translational and comorbidity research. Author SummaryWe developed a novel fine mapping method called FM-GPT, to identify putatively causal genes from correlated noise influencing a wide range of human traits and diseases with potentially mixed outcome types (e.g., binary, count or continuous). The rapid expansion of large-scale phenomic datasets has shifted the single-trait genetic studies to phenome-wide analyses, enabling the study of genetic architecture across many related traits simultaneously. FM-GPT performs gene-guided dimension reduction of the phenotypes and reveals pleiotropic or phenotype-specific effects of the identified causal genes. When applied to the UK Biobank data, FM-GPT greatly narrowed down the set size of putatively causal genes compared to other methods. The tool identified genes with pleiotropic effects on regional cortical thickness that regulate neuronal morphology and cortical organization across the cerebral cortex. It also identified genes that influence multiple medical conditions spanning the circulatory, metabolic, digestive, respiratory, and genitourinary systems. Among these conditions, two major axes of variation emerged, revealing a potential trade-off in gene regulation between immune and metabolic functions. This work provides a clearer picture of shared biological mechanisms across traits and diseases, advancing translational research and the understanding of comorbidity.

bioinformatics↗
bioRxiv2026

Geometry-enhanced protein language modeling enables discovery of novel antibiotic resistance genes

The global antibiotic resistome remains largely unexplored, not because antibiotic resistance genes (ARGs) are rare in the environment, but because many are evolutionarily distant from known ARGs. Current computational approaches primarily rely on sequence homology, and thus miss distant homologues. We develop GeoARG, a geometry-enhanced framework that integrates structural features with protein language models through knowledge distillation, enabling efficient large-scale screening using sequence input alone. Across multiple benchmarks, GeoARG substantially improves the detection of remotely homologous ARGs, particularly under low sequence identity and fragmented conditions. Large-scale metagenomic analysis uncovers 1,485 high-confidence ARG candidates that are highly divergent from known ARGs, expanding the phylogenetic and functional landscape of the resistome. Structural analyses further show that these candidates preserve active-site geometry and maintain stable ligand-binding configurations consistent with known resistance mechanisms. These results demonstrate that geometric constraints enable systematic expansion of the resistome and facilitate the discovery of evolutionarily distant yet functionally conserved genes. A public web server is available at https://ycclab.cuhk.edu.cn/GeoARG/.

bioinformatics↗
bioRxiv2026

Metabolic Drivers of Disease Activity and Complications in Crohns Disease: A Retrospective Cross-Sectional Study

BackgroundThis study aims to examine the independent relationships between individual components of metabolic syndrome (MetS) and two key clinical outcomes in patients with Crohns disease (CD): disease activity, as quantified by the Crohns Disease Activity Index (CDAI), and the occurrence of complications. MethodsThis retrospective cross-sectional study included 376 adults with newly diagnosed Crohns disease. Multiple linear regression was used to examine associations between metabolic parameters and CDAI scores, while multivariate logistic regression assessed links to complications. Analyses were also based on clinical CDAI cut-offs. Predictive nomograms were developed and internally validated via bootstrap resampling. ResultsMultiple linear regression indicated that higher CDAI scores were independently associated with lower BMI (B = -5.866, P < 0.001), lower HDL-C levels (B = -81.770, P < 0.001), higher triglycerides (B = 15.618, P = 0.001), and lower ESR (B = -0.375, P = 0.03). Multivariate logistic regression established low HDL-C (OR = 0.042, P < 0.001), low BMI (OR = 0.915, P = 0.034), and high triglycerides (OR = 1.792, P = 0.007) as significant independent risk factors for complications. The developed nomograms demonstrated strong predictive performance, with an adjusted R2 of 0.207 for the CDAI model and an AUC of 0.765 for the complication model. For both predictive tasks, the model incorporating separate TG and HDL-C measurements significantly outperformed the TG/HDL-C ratio model. ConclusionMetabolic disturbances demonstrate a significant association with increased disease severity and a higher risk of complication development in Crohns disease. Core tipO_LIDual-outcome study reveals HDL-C and TG differentially link to CD inflammation and complications, pointing to distinct mechanisms. C_LIO_LILow HDL-C is the strongest independent predictor for CD complications, underscoring its protective role beyond cholesterol transport. C_LIO_LIIndividual TG and HDL-C metrics outperform their ratio in prediction, challenging its use and suggesting independent pathways in CD. C_LIO_LILow BMI independently associates with both adverse outcomes, refining the "obesity paradox" and highlighting malnutritions key role. C_LIO_LIA practical, validated nomogram (AUC=0.765) integrates HDL-C, TG, and BMI to stratify complication risk, aiding clinical decision-making. C_LI

immunology↗
bioRxiv2026

Targeting Protease-activated Receptor 4 (PAR4) Protects Against Acute Kidney Injury (AKI) in Ischemia Reperfusion Injury

Acute kidney injury (AKI) is a serious and common clinical syndrome that currently has no effective treatment. Emerging evidence links coagulation pathways to kidney injury, particularly through coagulation proteases. Protease-activated receptors (PARs) are a family of G-protein coupled receptors (GPCRs) that are activated by proteolytic cleavage of their N termini, exposing a tethered ligand that initiates receptor signaling. PARs have been shown to play a major role in inflammation, vascular regulation, and tissue injury. PARs play key roles in inflammation, vascular regulation, and tissue injury. Previous work from the Hamm laboratory demonstrated that PAR4 contributes to AKI progression, as PAR4 knockout mice were protected in both unilateral ureteral obstruction and ischemia-reperfusion-based models of kidney disease. In this study, we investigated the potential of a PAR4 antagonist, VU6073819, at mitigating AKI progression in an ischemia-reperfusion injury (IRI) mouse model. PAR4 antagonism not only alleviated kidney injury and inflammatory response, but it significantly improved the survival. These findings identify PAR4 as a promising therapeutic target for AKI.

pharmacology and toxicology↗
bioRxiv2026

ERFMTDA: Predicting tsRNA-disease associations using an enhanced rotative factorization machine

MotivationtRNA-derived small RNAs (tsRNAs) have emerged as a novel class of regulatory molecules implicated in the pathogenesis of many human diseases, making them as promising biomarkers and therapeutic targets. However, existing computational methods for tsRNA-disease association prediction often overlook explicit biological attributes and complex feature interactions, limiting their predictive performance. ResultsWe propose ERFMTDA, an enhanced rotative factorization machine framework for predicting potential tsRNA-disease associations. ERFMTDA explicitly models complex interactions among heterogeneous biological features while integrating latent structural representations derived from the global association matrix. In addition, a biologically informed negative sampling strategy based on motif-level sequence similarity is introduced to improve the reliability of negative samples. Extensive experiments demonstrate that ERFMTDA consistently outperforms eleven state-of-the-art methods. Case studies on diabetic retinopathy and hepatocellular carcinoma further confirm its ability to prioritize biologically meaningful tsRNA-disease associations. Availability and implementationThe source codes and datasets of ERFMTDA are available at https://github.com/lanbiolab/ERFMTDA.

bioinformatics↗
bioRxiv2026

Evolutionarily Conserved Decline of tRNA Mannosyl-Queuosine Links Translational Regulation to Aging and Is Reversed by Queuine

Aging arises from interconnected molecular defects, yet upstream regulatory mechanisms that coordinate these hallmarks remain incompletely defined. While epitranscriptomic regulation has emerged as a critical layer of gene control, the contribution of tRNA-specific modifications to aging remains largely unexplored. Here, we systematically profile tRNA modifications across multiple organs, species, and senescence models and identify mannosyl-queuosine (manQ), a wobble-position modification of tRNAAsp, as the first tRNA-specific modification that consistently declines with age. ManQ depletion is evolutionarily conserved and tightly correlates with functional deterioration. Mechanistically, loss of manQ impairs translational fidelity, leading to proteome imbalance, collapse of proteostasis, and aberrant expression of senescence-associated proteins, including GPNMB. These translational defects intersect with established aging hallmarks and accelerate cellular and organismal aging. We further demonstrate that circulating queuine, a microbiota-derived precursor required for manQ biosynthesis, declines with age in rodents and humans. Queuine deficiency promotes senescence, whereas supplementation restores manQ levels, improves translational accuracy, suppresses p16/p21-driven senescence programs, and re-establishes proteostatic balance. Across species, queuine supplementation extends lifespan and enhances healthspan. In Drosophila, it increases median lifespan by 47% and improves stress resistance and memory. In naturally aging mice, long-term oral administration extends lifespan by 15.3%, reduces DNA methylation age, improves cognitive and motor performance, strengthens antioxidant defenses, remodels the gut microbiota, and alleviates inflammation and metabolic dysfunction without detectable toxicity. Collectively, these findings establish tRNA epitranscriptomic remodeling as a previously unrecognized layer of aging regulation and identify restoration of manQ through queuine supplementation as a multi-system strategy to delay aging.

pharmacology and toxicology↗
bioRxiv2026

Minimal biophysical rules are sufficient for the emergence of computational intelligence at the neuronal scale

How intelligence emerges from the brains complex microscopic physical system is a central question for neuroscience and artificial intelligence. Constrained by the genomic bottleneck that precludes synapse-by-synapse specification, we propose and validate a microscopic structure-function concise-constraint sufficiency hypothesis. We develop the Neuro-Informed Generative Connectome (NIGC) framework, and show that connectomes generated under a concise set of biophysical constraints (geometric embedding, node propensity modulation, a global energy budget and maximum-entropy selection) closely match the structural statistics of a measured mouse V1 microcircuit (similarities, 0.997). In parallel, using the generated connectome as the fixed reservoir of an echo state network (ESN), training only a linear readout achieves 90% accuracy on an auditory multi-classification task. Moreover, multidimensional biologically consistent functional phenotypes, such as hierarchical transmission delays and low-dimensional spatiotemporal trajectories, are obtained without fitting functional matrices or time courses. Further, by combining single-constraint ablations, pathological perturbations and cross-modal validation, we clarify how specific structural constraints map onto functional consequences. Together, these results delineate sufficient conditions for computational intelligence emergence at the microscopic scale, and provide an auditable benchmark for first-principles understanding of brain construction.

neuroscience↗
bioRxiv2026

Arborist: Prioritizing Bulk DNA Inferred Tumor Phylogenies via Low-pass Single-cell DNA Sequencing Data

Cancer arises from an evolutionary process that can be reconstructed from DNA sequencing and modeled by tumor phylogenies. High coverage bulk DNA sequencing (bulk DNA-seq) is widely available, but tumor phylogeny inference requires deconvolution, often resulting in non-uniqueness in the solution space. Single-cell DNA sequencing (scDNA-seq) holds potential to yield higher resolution tumor phylogenies, but the sparsity of emerging low-pass sequencing technologies poses challenges for the study of single-nucleotide variants. Increasing availability of data sequenced with both modalities provides an opportunity to capitalize on the advantages of these technologies. While inference methods exist for bulk DNA-seq and for low-pass scDNA-seq, no joint inference methods currently exist. As a first step, we propose a method named ARBORIST that prioritizes tumor phylogenies inferred via bulk DNA-seq using low-pass scDNA-seq data. ARBORIST takes as input a candidate set of trees with corresponding SNV clustering, along with variant and total read count data from scDNA-seq and uses variational inference to approximate a lower bound on the marginal likelihood of each tree in the candidate set. On simulated data, matching characteristics of current scDNA-seq data, ARBORIST outperforms both bulk and low-pass single-cell reconstruction methods. On a biological dataset, ARBORIST conclusively resolves the evolutionary relationship between different SNV clusters on a malignant peripheral nerve sheath tumor, which is supported by orthogonal validation via a proxy for copy number. ARBORIST provides a principled framework for integrating bulk DNA-seq and low-pass scDNA-seq data, improving confidence in tumor phylogeny reconstruction. Availabilityhttps://github.com/VanLoo-lab/Arborist

bioinformatics↗
bioRxiv2026

SpaMOAL: A spatial multi-omics graph contrastive learning method for spatial domains identification

Recent advances in spatial multi-omics technologies have opened new avenues for characterizing tissue architecture and function in situ, by simultaneously providing multimodal and complementary information--such as spatially resolved transcriptomic, epigenomic, and proteomic features. Current computational approaches face substantial challenges such as effective integration of multi-omics molecular information with spatial information and corresponding high-resolution histology images. To address this challenge, we proposed SpaMOAL (Spatially Multi-Omics graph contrAstive Learning), a graph-based contrastive learning approach for spatial domain identification. SpaMOAL learns clustering-friendly representations from spatial multi-omics data by integrating spatial coordinates, histological image features and molecular profiles, enabling accurate delineation of spatial tissue domains. Benchmarking across multiple recent paired spatial multi-omics datasets demonstrated that SpaMOAL consistently outperforms existing methods. By enabling accurate spatial domain delineation, SpaMOAL provides a powerful framework for interpreting tissue organization and cellular microenvironments.

bioinformatics↗
bioRxiv2026

Magnetically activatable insect viral vectors promote anticancer immunity through spatially confined gene disruption

Precise control of genome editing activity in vivo remains a major barrier to the clinical translation of CRISPR-based therapeutics, as current approaches cannot reliably restrict editing activity to target tissues 1,2,3. Here we show that a magnetically gated baculoviral CRISPR platform enables spatial control of genome editing activity 4. By coupling magnetic activation of viral transduction at target sites with complement-mediated inactivation of circulating vectors, this system functionally confines genome editing activity to defined spatial contexts, effectively decoupling editing activity from biodistribution. We further show that spatially confined genome editing can be coupled with endogenous antiviral sensing to drive coordinated immune modulation within tumours. In a syngeneic murine tumour model, local administration combined with magnetic activation restricts genome editing to tumours without detectable editing in major organs, remodels dendritic cell and CD8{square} T cell states, suppresses tumour growth, and extends survival. Together, these findings establish a generalizable strategy for spatial control of genome editing and localized immunomodulation in cancer.

bioengineering↗
bioRxiv2026

The Extracellular Matrix Regulates Invasion in Fusion-Negative Rhabdomyosarcoma via YAP-PIEZO1 Signaling Axis

BackgroundFusion-negative rhabdomyosarcoma (FNRMS) represents the most prevalent subtype of rhabdomyosarcoma, the most common pediatric soft tissue sarcoma. Although its invasion is a leading cause of recurrence and poor prognosis, its underlying mechanism remains unclear. We investigated how extracellular matrix (ECM) density regulates FNRMS progression via mechano-transduction. MethodsWe used three-dimensional spheroid invasion assays with RD and SMS-CTR cells embedded in varying collagen concentrations. Mechanistic insights were gained through immunofluorescence, ChIP-seq re-analysis, Fluo-4 calcium live cell imaging, and pharmacological inhibition of the YAP-PIEZO1 axis. ResultsHigh ECM density significantly enhanced invasive spreading, correlating with increased YAP nuclear localization. YAP overexpression was sufficient to promote invasion, while its inhibition attenuated the matrix-enhanced phenotype. We identified PIEZO1 as a direct transcriptional target of the YAP. High ECM density stimulated PIEZO1-dependent calcium influx, which was required for invasion. Furthermore, elevated PIEZO1 expression was significantly associated with poorer overall survival in FNRMS patients. Targeting YAP or ROCK signaling effectively suppressed both calcium flux and invasion. ConclusionsOur findings establish a YAP-PIEZO1 axis linking ECM density to FNRMS invasion. This mechanosensitive pathway represents a potential therapeutic vulnerability in aggressive FNRMS. Simple SummaryFusion-negative rhabdomyosarcoma (FNRMS) is the prevalent subtype of rhabdomyosarcoma, the most common pediatric soft tissue sarcoma. Its invasion often leads to recurrence and poor prognosis. This study investigates how the density of the extracellular matrix (ECM), surrounding cancer cells in the tissue, influences FNRMS invasion. We found that high ECM density activates the protein YAP, which directly triggers the expression of the mechanical sensor PIEZO1. This sensor allows calcium to enter the cells, providing a signal that facilitates invasion. Pharmacological inhibition of this axis successfully reduced the invasive potential, highlighting a novel therapeutic vulnerability for FNRMS patients.

cancer biology↗
bioRxiv2025

Decoupling Efficacy from Toxicity: Engineering Spatial Control in AAV-Mediated Gene Therapy

Hepatotoxicity poses a critical safety challenge for AAV-mediated gene therapy. To mitigate this, we evaluated strategies to minimize off-target hepatic transduction using an antibody expression model. We compared (i) muscle-restricted wild-type AAV9 expression and (ii) a novel myotropic capsid variant, AAV.eM. In humanized B-NDG mice bearing Raji-Luc lymphomas, intravenous administration of AAV9-MHCK7 encoding an CD19CD3 bispecific T-cell engager failed to reduce tumor burden. Conversely AAV.eM-MHCK7-CD19CD3 substantially alleviated tumor burden and achieved lymphoma clearance. By leveraging tissue-specific microRNAs, precise restriction of AAV.eM-mediated transgene expression to skeletal or cardiac muscle was achieved. Incorporating a heart-specific miR-208a binding site into the transgenes 3UTR did not compromise therapeutic efficacy when delivered via AAV.eM-MHCK7. Intramuscular delivery of AAV9-MHCK7-CD19CD3 or AAV.eM-MHCK7-CD19CD3 both cleared Raji-Luc tumors at a dose of 5 x 1012 vg/kg, underscoring the advantage of localized and targeted rAAV delivery over systemic administration. Notably, only AAV.eM-MHCK7-CD19CD3 achieved tumor eradication at a tenfold lower intramuscular dose (5 x 1011 vg/kg), reducing manufacturing costs and risks of dose-dependent immunogenicity and toxicity. Our findings demonstrate that combining tissue-specific targeting--via engineered capsids or tissue-selective promoters--with local delivery robustly reduces off-target hepatic expression, providing a strategic framework for enhancing the safety of AAV-based gene therapies.

cancer biology↗
bioRxiv2025

A frontal motor circuit for economic decisions and actions

Flexible behaviour requires transforming abstract cognitive representations, such as value preferences, into concrete motor actions. During economic decision-making, individuals evaluate options to guide choices and then transform these choices into specific actions to obtain rewards. Understanding how neural circuits convert these abstract economic decisions into spatial actions remains challenging because decision formation and motor planning are typically intertwined. Here we introduce a mouse task that temporally dissociates value-guided decisions from spatial action planning, and show that a frontal motor network implements the transformation across decision stages through dynamic circuit reconfiguration. Using cortex-wide imaging and optogenetic perturbations, we identified a frontal motor circuit that was causally required for both abstract and motor stages of choice. During the abstract decision stage, neurons in this circuit encoded option values and economic choices independently of sensorimotor contingencies, and unilateral silencing impaired decisions without spatial bias. In contrast, during spatial planning, value and spatial signals were non-linearly integrated to guide action selection, and unilateral silencing produced an ipsilateral bias. A dynamical model captured this transformation, predicting a mode switch from cooperative interhemispheric maintenance of economic choice to competitive, lateralized control of actions, which we validated with simultaneous bilateral recordings. These findings demonstrate how frontal motor circuits reconfigure their interactions to bridge abstract cognition and concrete actions, providing a circuit-level mechanism for flexible, value-guided behaviour.

neuroscience↗
bioRxiv2025

Phylogenomics of Rhinogobius gobies reveal northern-southern divergence and trait evolution in China

The goby genus Rhinogobius represents a prominent radiation of freshwater fishes in East Asia, with China serving as its evolutionary hotspot harboring over 39 endemic species. Despite their ecological dominance in riverine ecosystems, phylogenetic relationships among mainland Chinese Rhinogobius species remain poorly resolved due to limited sampling and morphological conservatism, particularly in meristic traits traditionally used for classification. To address these knowledge gaps, we employed a nuclear gene enrichment approach to target 4,434 single-copy nuclear loci across 26 species spanning all major river basins in eastern and southern China. Our phylogenomic analyses based on sequence of 2,137 cleaned-up loci (477,822 bp) recovered a robust topology dividing Rhinogobius into three primary lineages: an early-diverging of R. similis sister to two major clades, the northern vs. southern clades exhibiting strong geographic structuring. Fossil-calibrated molecular dating placed the crown age of Rhinogobius at [~]9.33 million years ago (Ma) during the late Miocene, with a pivotal north-south divergence at [~]5.96 Ma coinciding with major paleo-drainage reorganizations in East Asia. Ancestral state reconstruction revealed that the southern clade uniquely evolved unstable first dorsal fin formula and near-universal presence of predorsal scales. By integrating the most extensive geographic and taxonomic sampling to date with phylogenomic data, this study establishes: (1) a revised evolutionary timeline for Rhinogobius diversification linked to East Asian geomorphological history, (2) diagnostic morphological characters for major clades, and (3) a robust phylogenetic framework to guide future taxonomic revisions and conservation efforts for this ecologically significant group. HighlightsO_LIPhylogenomics resolves north/south split in Chinese Rhinogobius gobies. C_LIO_LIFossil-dated 9.3 MYA origin of Rhinogobius and rapid divergence at 5.96 MYA. C_LIO_LISouthern lineages exhibit more predorsal scales and unstable first dorsal fin formula. C_LIO_LIFirst genomic backbone for species revision and adaptation study. C_LI

zoology↗
bioRxiv2025

Acetylcholine reflects uncertainty during hidden state inference

To act adaptively, animals must infer features of the environment that cannot be observed directly, such as which option is currently rewarding, or which context they are in. These internal estimates, known as hidden states, guide behaviour but are inherently uncertain. Theoretical models propose that efficient inference requires tracking both the most likely state and the uncertainty of that estimate. While neural representations of state identity have been described in cortical and hippocampal circuits, the origin of uncertainty signals remains unclear. Here we show that acetylcholine (ACh) reflects this uncertainty during hidden state inference. Using fibre photometry in mice performing a probabilistic two-armed bandit task, we found that ACh release in medial prefrontal cortex and ventral hippocampus tracked recent omission history and predicted whether animals would switch choice on the next trial. Blocking muscarinic receptors with scopolamine selectively impaired loss-driven switching without affecting stable performance. A hidden-state inference model in which ACh modulated how uncertainty from past experience shaped future beliefs reproduced both the physiological and behavioural effects, whereas alternative models did not. These results identify ACh as a neuromodulatory signal reflecting uncertainty during hidden-state inference, linking theoretical models of belief updating to their biological implementation in the mammalian brain.

neuroscience↗