Biology subjects

Wang, J.

Publications and source records attributed to Wang, J..

At least 235 records · Page 13Linked to original sources

bioRxiv2026

Intrinsically disordered N-terminal regions suppress cotranslational protein degradation

Intrinsically disordered regions (IDRs) of proteins are thought to be inherently sensitive to proteolysis and considered one of the key components constituting an efficient degron. Here we report that IDRs can also suppress protein degradation. Our recent study showed that yeast ribosomal proteins, while posttranslationally stable, are subject to cotranslational protein degradation (CTPD). In mapping the degron responsible for CTPD of ribosomal protein Rpl8A, we found that its N-terminal IDR suppresses CTPD, whereas the adjacent structured domain acts as a degron. We further assessed the N-terminal IDRs of 9 other yeast proteins and found that they all inhibit CTPD. These results suggest that suppression of CTPD is likely a generic function of N-terminal IDRs. Moreover, we showed that the N-terminal IDR of human ribosomal protein hRpl7A also functions as a stabilizer against CTPD in human cells. When transplanted to the N-terminus of cystic fibrosis transmembrane conductance regulator (CFTR), the N-terminal IDR of hRpl7A reduces CTPD of CFTR by more than 80%. Thus, the stabilizer function of N-terminal IDRs is conserved from yeast to human. Using mass spectrometry, we demonstrated that HSP70 chaperone proteins Ssa and Ssb bind to the N-terminal IDR of Rpl8A. These data suggest that N-terminal IDRs may inhibit CTPD through recruiting HSP70 chaperone proteins to nascent chains, thereby facilitating cotranslational folding. Our study unveils a new role for IDRs in suppressing CTPD.

biochemistry↗

bioRxiv2026

Tau Disaggregation by a CNS-Permeable Small Molecule Reduces Fibril and Oligomer Burden and Preserves Proteostasis and Behavior

Pathological tau aggregates drive neuronal dysfunction in Alzheimer's disease (AD) and related tauopathies, yet no approved therapy eliminates existing tau neurofibrillary tangles. Here, we report the development of a coumarin-based small-molecule series that disaggregates tau fibrils and oligomers through a stacking-driven co-assembly mechanism. Structure-activity relationships identified PT-13 as a lead compound that inhibits tau seeding by AD brain-derived matter and reduces aggregate burden measured across both fibrillar and oligomeric tau species. Mechanistic studies demonstrate that disaggregation does not generate soluble oligomeric intermediates, addressing a central question in the field. PT-13 is brain-penetrant and well tolerated in vivo. In a tauopathy mouse model, PT-13 treatment reduces tau pathology while preserving behavioral function, proteasome capacity, and synaptic integrity. These findings establish small-molecule tau disaggregation as a viable therapeutic strategy and provide a molecular framework for the design of aggregate-directed therapeutics in neurodegeneration.

neuroscience↗

bioRxiv2026

A quantitative coordinate system for developmental dynamics

Quantitative comparison of morphogenesis across individuals remains a fundamental challenge, as developing embryos vary in shape, orientation and developmental tempo. Moreover, real-time three-dimensional imaging generates large, heterogeneous four-dimensional datasets that are difficult to directly align. As a result, developmental variability is typically described qualitatively rather than measured. Here we introduce STERN, a quantitative framework that learns continuous spatiotemporal representations of morphogenesis directly from in vivo 4D imaging data. By embedding embryos into a shared spatiotemporal space, STERN defines a quantitative developmental coordinate system that enables direct comparison of developmental trajectories across individuals without requiring explicit registration or staging. Applied to mouse embryogenesis, STERN reveals that embryos follow conserved developmental trajectories while progressing at distinct temporal rates, providing a quantitative measure of developmental heterochrony. Extending this framework to zebrafish neural crest light-sheet timelapse imaging, we further show that developmental order is preserved across distinct imaging views even with altered anatomical coverage, supporting the generality of the learned representation across vertebrate imaging contexts. Finally, in developing mouse hearts, where morphogenesis proceeds through subtle and continuously evolving structural changes, STERN resolves fine-scale developmental dynamics at minute-scale temporal resolution that are difficult to localize reproducibly using human experts or general-purpose multimodal AI. Together, these results establish a shared quantitative coordinate system for morphogenesis, in which developmental trajectories become directly comparable across individuals and developmental variability becomes a measurable property.

bioinformatics↗

bioRxiv2026

DyMoTree decodes early cell state transitions and drivers from single-cell transcriptomes using a tree-structured neural network

Inferring early cell fate from single-cell RNA-sequencing data is essential for identifying cellular origins and fate plasticity in development and disease. However, existing methods often fail to exploit tree-structured lineage trajectories, limiting the accuracy and interpretability of fate mapping. Here we present DyMoTree, a computational framework that models cell fate decisions as nonlinear mappings between progenitor and terminal cell states under explicit lineage constraints. By integrating lineage graphs with a tree-structured neural architecture, DyMoTree learns lineage-resolved cell-state transition maps from single-cell transcriptomes, enabling robust inference of early fate bias and identification of fate-specific progenitor substates and driver genes. Across simulations, lineage-tracing experiments, and in vivo systems, DyMoTree outperformed existing methods in resolving early fate biases. Applications to mouse embryogenesis, lung adenocarcinoma progression, and CAR-T immunotherapy revealed regulatory programs underlying developmental and disease-associated transitions. DyMoTree provides a general framework for modeling lineage-resolved cell-state dynamics underlying development and disease progression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/731114v2_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@11e715dorg.highwire.dtl.DTLVardef@1a4c695org.highwire.dtl.DTLVardef@e98805org.highwire.dtl.DTLVardef@1e14054_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

4 Phenylbutyrate Plus Gene augmentation: A dual therapy To Rescue of SLC6A1 Variant Associated Developmental And Epileptic Encephalopathy

BackgroundPathogenic variants in SLC6A1, which encodes the {gamma}-aminobutyric acid (GABA) transporter GAT-1, cause developmental and epileptic encephalopathies (DEEs) through reduced GABA uptake, impaired transporter trafficking, and functional haploinsufficiency. 4-Phenylbutyrate (PBA) is a clinically available small molecule with chemical-chaperone and histone-deacetylase-inhibitor activities that can rescue misfolded GABAergic proteins, but variant-level rescue data are needed to guide precision treatment. MethodsWe report a newly identified de novo SLC6A1 missense variant, p.Ala305Val (A305V), in a patient with myoclonic-atonic epilepsy and a developmental and epileptic encephalopathy phenotype. A305V was compared with the residue-matched comparator p.Ala305Thr (A305T). Variant effects were evaluated by (i) protein-structure prediction across nine stability-prediction algorithms using the cryo-EM-derived human GAT-1 template (PDB 7Y7W); (ii) 3H-GABA uptake assays in HEK293T cells and in human iPSC-derived astrocytes and cortical neurons; (iii) live-cell confocal microscopy of ER colocalization; (iv) pharmacologic rescue with PBA, TUDCA and salubrinal (v) and GAT-1 cDNA gene-augmentation, alone and in combination with PBA. ResultsAI-based stability predictors uniformly indicated destabilization of GAT-1(A305V) and GAT-1(A305T). A305V reduced 3H-GABA uptake across HEK293T, astrocyte, and neurons. The mutant transporter accumulated within the endoplasmic reticulum (ER), with ER colocalization rising from approximately 30% in wildtype to [~]80% in A305V; PBA reduced ER retention to approximately [~]40% and restored total GAT-1 fluorescence toward wildtype levels. Pharmacochaperones (PBA, TUDCA) restored GABA uptake for the mutant transporters. Wildtype GAT-1 gene augmentation improved mutant GAT-1 uptake and combined PBA-plus-augmentation produced rescue greater than either intervention alone in the available dose-response ranges. ConclusionsSLC6A1 A305V is a trafficking-impaired, loss-of-function GAT-1 variant whose dysfunction is tractable to two convergent therapeutic axes: pharmacologic correction of folding and trafficking, and augmentation of functional transporter dose. These findings support a two-pronged precision-medicine framework for SLC6A1-related DEEs in which PBA increased the transporter function augmented by increased gene therapy. Significance of the studyThis work links the patient-derived SLC6A1 A305V variant to a defined molecular mechanism--GAT-1 destabilization, ER retention, and reduced GABA uptake--and demonstrates that the deficit is reversible by two independent interventions that converge at the same downstream endpoint of functional surface transporter. Because PBA is already clinically deployable and GAT-1 cDNA augmentation models a future viral or non-viral gene therapy, the combined-rescue logic provides a falsifiable path for precision medicine in SLC6A1-related DEEs: chemical chaperoning corrects the folding bottleneck while transporter augmentation increases the pool available for rescue.

neuroscience↗

bioRxiv2026

Uncovering Pseudotime-Varying Genetic Causal Effects Along Single-Cell Trajectories for Pulmonary Disease Trait

With the increasing accessibility of single-cell RNA sequencing (scRNA-seq) data, cell-type-specific gene expression can be linked to complex traits through pseudo-bulk method, which considered aggregated gene expression from multiple cells of the same annotated cell type per individual and clearly shows the limitation of ignoring intra-individual cell-to-cell variability. Concurrently, pseudotime trajectory inference has gained popularity for its ability to capture continuous biological processes such as cell differentiation and lineage development, instead of individual discrete stages. It is natural to consider whether genetic effects for complex traits, such as individual level disease status, show a dynamic pattern along the inferred trajectories. In this study, we introduce a novel framework that models gene expression as a function of pseudotime along the inferred trajectories. We mapped expression quantitative trait loci (eQTL) effects in the cis-region as functional parameters, which we called "dynamic eQTLs", showing regulatory effects exerted by genetic variants change continuously along the cellular trajectory. For eQTLs of constant effects across pseudotime we leveraged external bulk-eQTL information to enhance the power. Furthermore, we employed significant, variable dynamic eQTLs as instrumental variables to infer causal relationships between gene expression and complex traits. To address challenges inherent to scRNA-seq data--such as sparsity and high variability--we incorporate an empirical likelihood-based inference method, which is non-parametric and self-normalized. Besides, genes associated with trajectory branchpoints may bring confounding, and we also proposed a causal mediation analysis framework to determine whether a gene plays a causal role for the disease directly and indirectly through driving cell fates. Applying our method to scRNA-seq data from human lung tissue of 114 samples (66 pulmonary fibrosis cases and 48 controls), along with meta-analyzed GWAS summary statistics for IPF from 3 studies, we identified pseudotime-dependent causal effects for IPF from genes implicated in the trajectory AT2 - translational AT2 - AT1, which is crucial in lung tissue repair and regeneration. We also found that 30 genes have a mediated effect through cell fates.

genetics↗

bioRxiv2026

Investigation of In Vivo Silk Scaffold Degradation by Decoupling Tissue Ingrowth Using a GPR-Driven Digital Twin Framework

Pelvic organ prolapse (POP) reconstruction is increasingly performed utilizing knitted silk meshes (KSM), yet tracking in vivo degradation kinetics remains challenging due to complex host tissue integration. This study developed an AI-driven semi-empirical framework utilizing Gaussian Process Regression (GPR) to bridge the kinetic mismatch between in vitro and in vivo environments. KSM scaffolds underwent 32 weeks of accelerated in vitro enzymatic degradation, with morphology (SEM), molecular conformation (FTIR), and mass loss being coupled with mechanical decay to train the GPR model. In vitro results revealed a multi-stage physical disintegration via a topochemical erosion pathway that preserved crystalline {beta}-sheet structures despite macro-scale mass and mechanical loss. When validated in a rat abdominal wall defect model, traditional tracking metrics encountered severe bottlenecks. Heterogeneous dye labeling caused premature fluorescence quenching by Week 16, while extensive tissue ingrowth masked gravimetric and SEM signatures. Intriguingly, a bi-phasic in vivo mechanical trajectory was identified, where initial degradation-led failure was followed by a secondary mechanical recovery driven by biomechanical synergy with neo-muscular tissue. Importantly, despite premature quenching, this work presents the first optical imaging approach to visually mapping the complete chronological breakdown of the scaffolds peripheral boundary layer in vivo, proving that outer functionalized layers eroded prior to internal silk cores. Furthermore, our GPR framework elegantly resolved the perennial technical barrier of tissue-mesh overlapping. By mathematically decoupling intrinsic polymer degradation from confounding tissue ingrowth, the model successfully achieved a first-of-its-kind prediction of the bare scaffolds long-term structural fate in a non-adhered state, providing a robust digital twin methodology for lifetime predictions of degradable biomaterials.

bioengineering↗

bioRxiv2026

Loss of Arginase 2 Promotes Lung Metastasis in immune-competent hosts via Nitric Oxide Synthase 2-Dependent Th17 Response

Distant metastasis is the leading cause of mortality in many cancers. Although metabolic reprogramming is recognized as a hallmark of cancer, how tumor-intrinsic metabolic enzymes regulate tumor-immune crosstalk during metastatic progression remains poorly understood. Here, using a high-throughput functional CRISPR-Cas9 screen targeting metabolic genes in an orthotopic 4T1 murine mammary carcinoma model of spontaneous lung metastasis, we identify a selective enrichment of arginase 2 (ARG2)-deficient tumor cells in metastatic lungs of immunocompetent but not RAG1-deficient mice, indicating a lymphocyte-dependent mechanism. Loss of ARG2 enhances spontaneous lung metastasis without affecting primary tumor growth. Further, metastatic outgrowth in the lung is not affected when tumor cells are injected intravenously, indicating that ARG2 regulates an early stage of the metastatic cascade. Mechanistically, ARG2 deficiency upregulates nitric oxide synthase 2 (NOS2), resulting in increased nitric oxide production, accumulation of cytosolic DNA, and activation of the cGAS-STING-NF-{kappa}B pathway, leading to upregulation of inflammatory cytokines. ARG2-deficient tumors exhibit an immunosuppressive tumor microenvironment characterized by enrichment of Th17 cells and reduced anti-tumor immune populations. Functionally, Th17 cells enhance tumor cell migration in vitro and promote spontaneous lung metastasis in vivo. Genetic deletion of NOS2 attenuates cytosolic DNA accumulation, reduces STING-NF-{kappa}B activation, restores anti-tumor immunity, and suppresses ARG2 deficiency-driven metastatic burden in vivo. Collectively, these findings define a tumor cell-intrinsic ARG2-NOS2 axis that regulates inflammatory signaling and the tumor microenvironment to promote metastasis, highlighting a targetable vulnerability in metastatic breast cancer.

cancer biology↗

bioRxiv2026

Lack of ancestral SARS-CoV-2 imprinting promotes BA.3.2.2 infection in children

The ongoing evolution of SARS-CoV-2 is heavily constrained by population-level immune imprinting. Recent genomic surveillance reveals an unexpected demographic shift: the highly mutated BA.3.2.2 sublineage is significantly enriched in paediatric populations globally, unlike concurrent variants XFG and NB.1.8.1. Here, the evidence demonstrates that this paediatric susceptibility arises from the absence of ancestral Wuhan-strain immune imprinting. Serological analyses reveal a profound failure to neutralise BA.3.2.2 in young children with no ancestral strain exposure. Concurrently, single-B-cell sequencing analyses demonstrate that BA.3.2.2 completely evades Omicron-specific Class 1/4 neutralising antibodies, encoded by IGHV2-5 and IGHV5-51, which dominate the repertoires of weakly imprinted individuals such as young children. Conversely, BA.3.2.2 remains uniquely susceptible to broadly cross-reactive Class 1 antibodies, encoded by IGHV3-53/66, typically enriched in adults with strong ancestral imprinting, such as mRNA vaccine recipients. Critically, sustained transmission of BA.3.2.2 in paediatric populations may catalyse the emergence of secondary variants that combine the paediatric-evading features of BA.3.2.2 with adult-evading Class 1 mutations. This could allow the lineage to breach the imprinted immunity of the adult population, potentially driving widespread global transmission. Together, these findings show that legacy immune imprinting paradoxically provides superior protection against a highly divergent saltation variant, directly explaining the age-biased transmission of BA.3.2.2, and informing future paediatric vaccination strategies.

immunology↗

bioRxiv2026

Minimal essential requirements for neural tube self-organisation

The reliable generation of diverse cell types in precise proportions is essential for the formation of functional tissues during embryonic development. Three-dimensional organoid models derived from pluripotent stem cells (PSCs) provide powerful systems for identifying principles governing tissue self-organisation. Neural tube organoids (NTOs), initiated from single PSCs with a pulse of retinoic acid (RA), self-organise into structures containing floorplate cells that secrete SHH morphogen to pattern adjacent neural tissue. Yet, how the initial cellular diversity arises and how appropriate cell-type proportions are allocated has remained unclear. Here, using time-resolved single-cell transcriptomics, quantitative immunofluorescence, and dynamical systems modelling we show that RA triggers a transient co-expression state of the transcription factors PAX6 and FOXA2 from which cells asynchronously resolve into two opposing fates: PAX6 neural precursors and FOXA2 floorplate precursors. PAX6 and FOXA2 are both necessary and sufficient to reconstitute self-organisation, establishing these transcription factors as key determinants of emergent tissue pattern. Rather than operating as a simple feed-forward system in which cells are guided solely by RA, feedback between the alternative cell fates, mediated by BMP signalling from floorplate precursors, determines cell type proportions and ensures reproducible cell-type diversity in each NTO. This dual expression state was also identified in mouse embryos, demonstrating how in vitro models inform in vivo biology. These findings establish a general design strategy - symmetry breaking through opposing fate determinants coupled to proportioning via signal feedback control - that may operate broadly across developmental contexts to generate tissues with predictable cellular compositions.

developmental biology↗

bioRxiv2026

Multimodal physical evidence uncovers interpretable gene regulatory networks for perturbation prediction

Gene regulatory networks govern cell fate transitions through dynamic causal mechanisms1. Since exhaustively mapping this vast perturbation space experimentally is prohibitive2, scalable computational models are essential. Yet, current frameworks fall short because they infer statistical co-expression rather than physical mechanisms, remain blind to non-canonical regulators lacking classical DNA-binding motifs, and fail to generalize across unseen perturbation factors or cell lines. Here we show that a multimodal biophysical framework, VitaGRN, overcomes these barriers by constructing a biophysical regulatory scaffold from multimodal evidence and propagating interactions to capture non-canonical regulators. By leveraging structurally aligned protein embeddings, VitaGRN predicts zero-shot perturbation responses and uncovers non-canonical translational control programs. Notably, VitaGRN demonstrates robust generalization across unseen factors, cell lines, and developmental transitions. Ultimately, VitaGRN generates a confidence-calibrated virtual perturbation atlas spanning over a thousand factors. This resource reframes gene regulatory networks from static correlation graphs into dynamically generalizable and mechanistically trans-parent models, streamlining wet-lab candidate prioritization.

bioinformatics↗

bioRxiv2026

A Self-Priming Neural Chain Links Sequential Behaviors Across Timescales

Behavioral sequences are essential for survival, yet the neural mechanisms that link one action to the next remain incompletely understood. In classical chain models, sequential behaviors arise through feedforward propagation of activity across distinct neuronal populations or network modules. Here, we identify a distinct form of neural chain mechanism in which neurons active during a first behavior modulate themselves into a persistent state of elevated tonic firing that subsequently drives the second behavior from within the first circuit module. We term this process self-priming, reflecting the role of activity-dependent auto-modulation in enabling behavioral sequencing. We investigated this mechanism in the escape swim-crawl sequence of the marine mollusk Tritonia diomedea, in which rhythmic swimming is consistently followed by tens of minutes of rapid crawling. Serotonergic dorsal swim interneurons (DSIs), components of the swim central pattern generator, were previously known to exhibit elevated firing for tens of minutes after a swim motor program (SMP) and to drive crawling. We show here that their sustained post-SMP activity arises from self-induced increases in DSI excitability: their bursting during the SMP produces long-lasting depolarization and enhanced excitability within the DSI population. This persistent state drives prolonged elevated tonic firing which, in turn, drives escape crawling. Our findings demonstrate a self-priming neural chain mechanism in which activity during one behavior generates the internal drive for the next. Unlike previously described feedforward chain mechanisms, sequencing in this system does not depend on sequential recruitment of distinct neural substrates. Instead, the same neurons participate continuously across both behaviors while their activity-dependent change in state links the two actions into a coordinated sequence. This mechanism provides an elegant and generalizable solution for linking sequential actions across timescales.

neuroscience↗

bioRxiv2026

TBC1D23-AAVR Interaction Drives Endosome-to-TGN Trafficking Required for rAAV Transduction

The adeno-associated virus receptor (AAVR; also known as KIAA0319L) is the proteinaceous receptor required for the transduction of multi-serotype adeno-associated viruses (AAVs). While the extracellular polycystic kidney disease (PKD) domains of AAVR directly bind AAV capsids, the function of its C-terminal cytosolic domain (AAVR-C) in governing AAV internalization and intracellular trafficking remains undefined. Using targeted pulldown of AAVR-interacting host proteins with a glutathione S-Transferase (GST)-fused AAVR-C recombinant protein (GST-AAVR-C) as a bait, we identified that TBC1 domain family member 23 (TBC1D23), a specialized intracellular trafficking adaptor and bridging factor, binds AAVR via the AAVR-C and mediates AAVs endosome-to-trans-Golgi network (TGN) transport. Biolayer interferometry (BLI) demonstrates nanomolar-affinity binding between AAVR-C and the C-terminal scaffold domain of TBC1D23 (TBC1D23-C). CRISPR-mediated knockout of TBC1D23 severely impairs rAAV transduction across multiple human cell types, including polarized human airway epithelium (HAE). Loss of TBC1D23 disrupts convergence of internalized AAV capsids at the TGN, leading to diminished nuclear import of AAV vectors. Mutational analyses of AAVR-C reveal that its acidic residue cluster is required for TBC1D23 binding and AAV retrograde transport from the endosome to the TGN. Together, these findings define TBC1D23 as a receptor-proximal trafficking module that couples AAV-AAVR engagement to vesicle transport, revealing a missing core regulatory step in AAV retrograde transport that is essential for productive rAAV transduction. SignificanceRecombinant adeno-associated viruses (rAAVs) are widely used gene delivery vectors, yet the intracellular trafficking steps that enable productive transduction remain incompletely defined. We identify TBC1D23 as a critical host adaptor that directly binds the cytosolic tail of the AAV receptor (AAVR) and mediates retrograde transport of AAV vectors from endosomes to the trans-Golgi network (TGN). Disruption of the AAVR acidic residue cluster abolishes TBC1D23 binding, blocks TGN trafficking, impairs nuclear import, and severely reduces transduction across multiple serotypes and in polarized human airway epithelium. These findings reveal a receptor-proximal trafficking checkpoint essential for rAAV gene delivery and define a host-vector interface with potential for rational vector engineering and therapeutic modulation.

microbiology↗

bioRxiv2026

Equitable Health Intelligence: An Open Benchmark of Multi-Population Machine Learning for Omics-Based Cancer Prognosis

PurposeMachine learning (ML) models for omics-based cancer prognosis are often trained on data from predominantly European-ancestry populations, producing biased predictions for other populations and undermining equitable genomic medicine. Existing fairness benchmarks mainly focus on outcome parity rather than predictive performance parity across populations. Public benchmark resources are needed for systematically detecting and mitigating such performance disparities in multi-population cancer prognosis. MethodsWe developed Equitable Health Intelligence (EHI, https://ehiportal.org), an open-source benchmark of multi-population ML for omics-based cancer prognosis. EHI contains 1,475 ML tasks across 40 cancer/pan-cancer types, 4 omics feature sets, 4 clinical endpoints, 5 event-time thresholds, and 3 data-disadvantaged population (DDP) groups relative to a majority European Ancestry population group. Deep neural network models are trained under three multi-population ML schemes (Mixture, Independent, and Transfer Learning), with Naive Transfer included as a no-adaptation control, comprising a total of 10,325 ML experiments. ResultsThe EHI platform provides an interactive environment with visualization and exploratory tools for users to inspect predictive performance disparities between the majority European-ancestry group and data-disadvantaged populations, evaluate the extent to which transfer learning mitigates these disparities, and examine the impact of feature engineering methods across cancer types, omics features, and clinical endpoints. ConclusionEHI is an open, interactive, and extensible benchmark for identifying and addressing performance disparities in multi-population ML for omics-based cancer prognosis. It provides a foundation for a growing ecosystem of methods targeting ML performance disparities arising from biomedical data inequality and population-level distribution shifts, thereby advancing equitable AI in precision oncology.

bioinformatics↗

bioRxiv2026

Enhanced glymphatic CSF tracer influx during α2-adrenergic agonist anesthesia is independent of tracer injection duration

The glymphatic system mediates brain-wide cerebrospinal fluid (CSF) transport and is highly sensitive to brain state. Experimental studies show that 2-adrenergic agonist- based anesthesia enhances glymphatic CSF influx, whereas isoflurane markedly suppresses it. However, it has been suggested that the reduced tracer influx observed during isoflurane anesthesia may reflect rapid clearance of tracer from the basal cisterns rather than genuine inhibition of glymphatic transport. To address this question, we compared conventional short-duration cisterna magna tracer injections with prolonged low-rate infusion while maintaining identical total tracer dose and anesthesia duration. Across both paradigms, ketamine/dexmedetomidine anesthesia consistently produced substantially greater perivascular CSF influx than isoflurane. In contrast, tracer accumulation in blood and cervical lymph nodes remained largely unchanged between conditions. These findings demonstrate that suppression of glymphatic influx during isoflurane anesthesia is independent of tracer injection duration and support the conclusion that 2-adrenergic agonist-based anesthesia promotes glymphatic transport through mechanisms linked to sleep-like brain states.

neuroscience↗

bioRxiv2026

Efficient neural encoding of time intervals in speech and complex sound sequences

Encoding time intervals in complex sound faces dual challenges: it must be precise and cover a broad dynamic range. In speech, for example, a ten millisecond lengthening of a syllable can signal stress or phrasal boundaries, yet the syllable duration distribution is long tailed beyond 500 ms and has variable statistics across speakers. Here, we propose that the auditory cortex employs efficient coding to represent time intervals. When listeners heard syllable sequences drawn from different duration distributions, the magnetoencephalographic (MEG) response from the temporal cortex scaled with syllable duration, which is characterized using the interval-response function. Crucially, this interval-response function met predictions of efficient coding. Its intercept and slope adapted to the mean and variance of syllable duration, respectively, and it consistently exhibited a compressive nonlinearity that reduced response skewness, consistent with a maximum-entropy code. A computational model that constantly updates the inference of duration distribution provided an algorithmic account of this efficient coding, and intracranial electroencephalogram (iEEG) data confirmed the same principles during natural speech comprehension. Together, our findings reveal an efficient neural mechanism that supports precise encoding of highly variable time intervals in complex sound sequences.

neuroscience↗

bioRxiv2026

Hepatic Acetate is an Essential Fuel for the Heart during Energy-Deprived States

Energy supply is fundamental to cardiac performance, yet the mechanisms by which the heart adapts to nutrient scarcity remain incompletely understood. Here, we identify acetate as a pivotal metabolic substrate that sustains cardiac contractile function during fasting-induced energy deficiency. Fasted mice reveal significant elevation of circulating acetate derived from hepatic fatty acid catabolism. Physiological concentrations of acetate alone can maintain ex vivo beating and electrical stability in Langendorff-perfused hearts. Primary mouse cardiomyocytes preferentially utilize acetate under energy-restricted conditions, which is abolished by knockdown of mitochondrial Acss1, the enzyme responsible for converting acetate to acetyl-CoA. Isotopic tracing with 13C-acetate demonstrated selective cardiac uptake during fasting. In vivo, acetate supplementation preserved heart rate and systolic function in fasted wild-type mice but not in Acss1-deficient hearts. These findings reveal an Acss1-dependent metabolic pathway enabling the heart to harness hepatic acetate as an endogenous fuel, representing a nutrient stress-responsive adaptation based on inter-organ crosstalk and implying therapeutic potential of acetate to energy-deficient heart diseases.

biochemistry↗

bioRxiv2026

Decoding Hierarchical Cell-Cell Communication in Spatial Multi-Omics with CellSTIC

Cell-cell communication coordinates tissue development, homeostasis, and immunity, yet defining signaling interactions within intact tissues remains challenging. Single-cell transcriptomics enables systematic ligand- receptor inference, but tissue dissociation removes spatial context and obscures local and region-specific signaling. Spatial transcriptomics and spatial multi-omics can recover communication in situ, although existing methods often incompletely integrate heterogeneous data or produce poorly interpretable ligand-receptor lists. Here we present CellSTIC, a framework that resolves cell-cell communication in spatial multi-omics as structured programs grounded in tissue architecture. CellSTIC integrates multimodal evidence from local neighborhoods and organizes interactions into a hierarchical semantic representation that remains traceable to underlying molecules. This enables analysis from individual ligand-receptor pairs to functional modules comparable across tissues, regions, and states. In simulations and diverse tissue datasets, CellSTIC recovered spatially coherent communication structures and domains, revealing immune, brain, developmental, and regenerative programs, and providing a general approach for generating mechanistic hypotheses in situ. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/728114v2_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@a04e82org.highwire.dtl.DTLVardef@826435org.highwire.dtl.DTLVardef@81092dorg.highwire.dtl.DTLVardef@1818e8b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗