Biology subjects

Yang, Y.

Publications and source records attributed to Yang, Y..

At least 217 records · Page 12Linked to original sources

bioRxiv2026

Focal Transcranial Magnetic Stimulation of the Rat Anterior Cingulate Cortex Inhibits Incubation of Opioid Craving after Voluntary Abstinence

Relapse remains a major challenge in opioid addiction treatment, underscoring the need for innovative therapies. Progress in neuromodulation therapies has been limited by insufficient mechanistic understanding of stimulation engagement and disease-related changes in the brain. We used a novel, focal transcranial magnetic stimulation (TMS) system to deliver high-density theta burst stimulation (hdTBS) combined with resting-state fMRI to test whether anterior cingulate cortex (ACC) stimulation reduces relapse-like behavior and alters functional circuitry in a rodent model of opiate dependence. The coil focality and stimulation parameters approximate human TMS protocols, and the targeted region represents a functional homolog of the human ACC. We trained rats to self-administer oxycodone intravenously for 14 days. We then introduced an electric barrier for 13 days, which caused cessation of drug self-administration. We assessed relapse to oxycodone seeking immediately after training (early abstinence) and after electric-barrier exposure (late abstinence). We administered daily hdTBS or sham stimulation for 7 days before the late-abstinence test. Sham-treated rats showed a time-dependent increase in oxycodone seeking during abstinence (incubation of oxycodone craving) and reduced ACC functional connectivity. In contrast, hdTBS prevented the incubation of oxycodone craving and restored ACC connectivity with the dorsal and ventral striatum. Tracer-based axonal-projection data further showed that stimulation-induced effects aligned with regions receiving dense projections from the stimulation site, suggesting that the projection architecture is critical to the propagation of focal stimulation across distributed networks. These findings identify ACC-centered circuits as mechanistically informed targets for TMS-based interventions that aim to reduce opioid relapse during abstinence. One sentence SummaryPrefrontal TMS stimulation reduced relapse-like behavior and restored corticostriatal circuits, highlighting translational targets for addiction treatment.

neuroscience↗

bioRxiv2026

Developmental Elimination of Electrical Synapses by UNC-51/UNC-76-Mediated Vesicular Transport

Neural circuit maturation requires precise elimination of transient synaptic connections, but how electrical synapses are eliminated remains unknown. Using C. elegans PLM mechanosensory neurons, we found that gap junctions form transiently during early development and are subsequently eliminated as the circuit matures. These transient electrical synapses drive high-frequency calcium oscillations, and optogenetic ablation of early gap junctions abolishes calcium dynamics and disrupts chemical synapse formation, demonstrating their critical developmental function. Through forward genetic screening, we identified the conserved kinase UNC-51/ULK as essential for elimination. UNC-51 phosphorylates the kinesin adaptor UNC-76/FEZ to initiate RAB-10-dependent retrograde trafficking, progressively depleting innexin proteins from the synaptic site. Disrupting this pathway prevents elimination, causing persistent electrical synapses and neuronal hyperactivity. These findings establish a conserved framework for developmental elimination of functionally essential transient electrical connectivity.

cell biology↗

bioRxiv2026

A normative reference for large-scale human brain dynamics across the lifespan

Human brain function emerges from dynamic reconfigurations of large-scale neural networks. While population-level reference charts have transformed the study of static brain structure and connectivity, an equivalent normative framework for intrinsic brain dynamics has been lacking. This gap has limited our ability to characterize individual variability, development, ageing, and mental health conditions at scale. Here, we establish a population-level normative reference for large-scale human brain dynamics using resting-state fMRI data from more than 10,000 individuals spanning the lifespan and 91 scanning sites. We derive a compact set of recurring brain-state configurations that are reproducible across scanners and acquisition paradigms and that generalize to previously unseen cohorts. Anchoring these dynamic states to normative lifespan models enables the quantification of individual deviations relative to population reference distributions. We show that intrinsic brain dynamics undergo systematic reorganization across development and ageing, with pronounced changes before early adulthood and more gradual modulation thereafter. Applying this framework across multiple mental health conditions reveals disorder-specific and highly heterogeneous deviations in brain dynamics that are not captured by static neuroimaging measures. Robust transfer to independent cohorts and longitudinal analyses demonstrate that normative brain dynamics can be reliably assessed out of distribution. These results delineate a population-scale dynamic architecture of the human brain and extend normative brain mapping from static phenotypes to the temporal domain, providing a reference framework for studying brain function across the lifespan in health and disease.

neuroscience↗

bioRxiv2026

Mitochondria Organize Gap Junctions to Enable Neural Circuit Scaling

Neural circuits must maintain function during dramatic growth-driven expansion. While chemical synapses can be independently reorganized in pre- and postsynaptic neurons, gap junctions physically couple partner cells, requiring coordinated spatial redistribution--a process whose mechanisms remain unknown. Using the C. elegans mechanosensory circuit, we show that gap junctions undergo stereotyped developmental reorganization, transitioning from clustered to dispersed configurations as partner neurites elongate. Forward genetic screening identified RIC-7, a mitochondrial adaptor protein, as essential for this redistribution. Loss of RIC-7 prevents gap junction dispersal, trapping partner neurites at clustered sites and severely impairing electrical transmission. Mechanistically, RIC-7 directs mitochondrial positioning to gap junctions, where mitochondria recruit the microtubule-organizing component PTRN-1 to locally enhance microtubule dynamics. Disrupting mitochondrial transport (miro-1;mtx-2 mutants) or PTRN-1 function phenocopies ric-7 defects, while metabolic dysfunction does not, establishing a non-metabolic role for mitochondria in organizing synaptic architecture. Remarkably, RIC-7 expression in one neuron alone suffices to restore gap junction and partner neurite distribution across both coupled cells. Structural modeling suggests mechanical adhesion between docked hemichannels enables this coordinated reorganization. These findings establish mitochondria as mobile organizers of synaptic spatial architecture, demonstrate that proper junction distribution is essential for electrical transmission efficacy, and reveal how cell-autonomous mechanisms can achieve bilateral structural coordination during circuit growth.

neuroscience↗

bioRxiv2026

Ligand-guided Sequence-structure Co-design of De Novo Functional Enzymes

Proteins underpin essential biological functions across all kingdoms of life. The capacity to design novel proteins with tailored activities holds transformative potential for biotechnology, medicine, and sustainability. However, since protein functions, particularly enzymatic activities, depend on precise interactions with small-molecule ligands, accurately modeling these interactions remains a formidable challenge in de novo protein design. Here, we introduce EnzyGen2, a protein foundation model designed for the simultaneous co-design of sequence and structure under ligand-guided functional targeting. Comprising 730 million parameters, EnzyGen2 is trained on 720,993 protein-ligand pairs using multi-task learning objectives that encompass joint prediction of sequence, structure, and protein-ligand interactions. In rigorous in silico benchmarks, EnzyGen2 consistently outperforms state-of-the-art baselines, including Inpainting, RFdiffusion/ProteinMPNN, RFdiffusion2/LigandMPNN, and RFdiffusion3/LigandMPNN, as measured by the enzyme-substrate prediction score, AlphaFold2 confidence metric, and structural fidelity, while it generates samples 400x faster than prior methods. We further experimentally validated EnzyGen2 across multiple enzyme families, including chloramphenicol acetyltransferase, aminoglycoside adenylyltransferase, and thiopurine S-methyltransferase. De novo enzymes generated by our family-specific EnzyGen2 exhibited catalytic activities comparable to or exceeding those of natural enzymes, while retaining substantial novelty with sequence identities as low as 51.6%. These results establish EnzyGen2 as a robust Artificial Intelligence-based tool for functional enzyme design, demonstrating the power of large protein foundation models to create high-performance, novel biocatalysts.

bioinformatics↗

bioRxiv2026

Stepwise GRN co-option in the evolution of a dipteran respiratory organ

The origin of morphological novelty lies at the heart of evolutionary biology, yet how ecological shifts drive its emergence through rewiring of gene regulatory networks (GRNs) remains poorly understood. Here, we dissect the roles of GRN co-option in the development and evolution of the prothoracic respiratory organ (PROD), a key innovation facilitating aquatic respiration in dipteran pupae. Ancestral-state reconstruction across over 200 species reveals that PROD originated [~]241 Ma in the common ancestor of Diptera, with a major increase in structural complexity in schizophoran flies [~]66-70 Ma. These transitions coincide with the Carnian Pluvial Episode and the K-Pg mass extinction, periods of profound climatic and ecological upheaval that likely imposed selection pressure for efficient pupal gas exchange in flooded or submerged habitats. Using comparative transcriptomics, scRNA-seq, and genetic manipulations in Drosophila melanogaster and Aedes albopictus, we demonstrate that PROD is a prothoracic wing serial homolog, specified by a cut-centered spiracle GRN and arising through heterochronic activation of an appendage GRN. In schizophoran flies, increase in complexity of PROD involved additional co-option of a breathless-centered tracheal GRN. Our findings reveal a stepwise model of hierarchical GRN co-option and provide potential links between geo-climatic events to the origin and escalation of morphological novelty. This eco-evo-devo framework provides a mechanistic and generalizable paradigm for how ecological challenges are translated into morphological innovations over macroevolutionary timescales.

evolutionary biology↗

bioRxiv2026

Somatic mutation in human cerebellum illustrates neuron type-specific patterns of age-related mutation

Human neurodegenerative disorders are characterized by exquisite specificity for neuronal types, but the basis of this is unknown. Here, we show that cerebellar granule neurons (GN)--the most abundant neuronal type in the human brain--accumulate somatic mutations in patterns highly distinct from cerebral cortical neurons, and more closely resembling oligodendroglia and other dividing cells. We find shared mutational signatures between normal aging GNs and medulloblastoma subtypes, suggesting the GN lineage as a tumor cell of origin. Whole-genome sequence of multiple single GNs from the same donor allowed analysis of specific times of neurogenesis, revealing a rich lineage tree that includes GNs that become postmitotic 2 years or more after birth, yet migrating postnatally to populate both the cerebellar vermis and the distant cerebellar hemisphere. Our results show that neuronal type-specific somatic mutation patterns enlighten normal development, cancer origins and potentially the cell type-specificity of neurodegeneration.

neuroscience↗

bioRxiv2026

MolX: A Geometric Foundation Model for Protein-Ligand Modelling

Understanding how small molecules interact with protein binding pockets is central to structure-based drug discovery. Accurately modelling these interactions requires capturing the 3D geometry and physicochemical complementarity of binding interfaces, yet existing computational approaches encode proteins and ligands separately or rely on simplified structural representations that do not explicitly model cross-entity spatial relationships. Such decoupled representations restrict their capacity to capture interface-level geometric constraints that arise from protein-ligand coorganisation. Here we present MolX, a Graph Transformer foundation model that jointly learns geometric and chemical representations of protein pockets and ligands from large-scale 3D structural data. Integrating over 3 million protein pockets and 5 million molecules, MolX represents both entities as E(3)-equivariant graphs to preserve spatial geometry and chemical context. The architecture employs dual E(3)-equivariant graph Transformer encoders to model pocket and ligand embeddings, ensuring representations remain invariant to rotation, translation, and reflection. MolX is pretrained using a hybrid learning paradigm that combines supervised biochemical objectives, logP and energy-gap regression, with self-supervised geometric objectives, coordinate re-construction, and atom-type prediction, fostering generalisable molecular understanding. Across eight downstream benchmarks, including antibody-drug conjugates (ADC), proteolysis-targeting chimeras (PROTAC), molecular glue, and PCBA activity prediction, as well as binding affinity and physicochemical property regression, MolX achieves consistent state-of-the-art performance and strong cross-domain generalisation. Furthermore, MolX incorporates a sparse autoencoder module to decompose latent representations into interpretable biological components, thereby revealing the pocket-ligand interactions that drive prediction outcomes. Together, MolX establishes a scalable and interpretable foundation model for molecular representation learning, providing a unified framework for predicting and interpreting complex small-molecule-protein interactions.

bioinformatics↗

bioRxiv2026

CsFDL1-CsFTL3 complex represses CsFTL3 via negative feedback to fine-tune flowering in Chrysanthemum seticuspe

In many flowering plants, the transition from vegetative growth to reproductive development is regulated by seasonal changes in photoperiod. Under inductive photoperiods, leaves produce the florigen FT (FLOWERING LOCUS T), which is transported to the shoot apex to promote flowering. The photoperiod is known to have a major effect on the flowering of chrysanthemum. In the perennial short-day (SD) plant Chrysanthemum seticuspe, the expression of CsFTL3 (FT-like gene) does not increase immediately after shifting from long-day (LD) to SD conditions but gradually accumulates under continuous SD conditions, peaking during inflorescence development. However, the underlying mechanism remains elusive. We show that CsFDL1 (an ortholog of FD) and CsFTL3 exhibit a significant inverse expression pattern in leaves during the initial stage of short-day inductions. Furthermore, the expression of CsFTL3 is upregulated in the leaves of CsFDL1-knockdown transgenic lines. CsFDL1 is expressed in leaves and forms a complex with CsFTL3 to recognize several TCGA- and ACGT-containing motifs in the CsFTL3 promoter. The CsFTL3-CsFDL1 complex downregulates CsFTL3 expression, thereby preventing its excessive induction by SD signals and inhibiting precocious floral transition. This study reveals that CsFDL1 acts as a key early repressor in the photoperiodic flowering pathway of chrysanthemum leaf, mediating negative feedback regulation by forming a complex with CsFTL3 to achieve precise temporal control of short-day-dependent flowering responses.

plant biology↗

bioRxiv2026

PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery

The convergence of large language model-powered autonomous agent systems and single-cell biology promises a paradigm shift in biomedical discovery. However, existing biological agent systems, building upon single-agent architectures, are narrowly specialized or overly general, limiting applications to routine analyses. We introduce PantheonOS (https://PantheonOS.stanford.edu), an evolvable, privacy-preserving multi-agent framework designed to reconcile generality with domain specificity. Critically, PantheonOS enables agentic code evolution, allowing evolving state-of-the-art batch correction and our reinforcement-learning augmented gene panel selection algorithms to achieve super-human performance. PantheonOS drives biological discoveries across systems: uncovering asymmetric paracrine Cer1-Nodal inhibition in proximal-distal axis formation of novel early mouse embryo 3D data; integrating human fetal heart multi-omics with whole-heart data to reveal molecular programs underpin heart diseases; and adaptively selecting virtual cell models to predict cardiac regulatory and perturbation effects. Together, PantheonOS points towards a future where scientific discoveries are increasingly driven by self-evolving AI systems across biology and beyond. Websitehttps://pantheonos.stanford.edu Ecosystemhttps://github.com/aristoteleo SummaryLarge language model-powered agent systems are driving a paradigm shift in scientific discovery by automating, scaling, and accelerating data analysis. This transformation is particularly profound in biology, where the rapid expansion of single-cell and spatial genomics has effectively reshaped the field into a data-intensive science. However, existing biological agent systems are typically constrained to single-agent designs, are narrowly specialized, or are overly general without sufficient domain expertise, limiting their applicability to routine or shallow analyses. Here, we introduce PantheonOS (https://pantheonos.stanford.edu), an evolvable, privacy-preserving, and general-purpose multi-agent framework designed to reconcile generality with deep domain specificity. PantheonOS provides an abstract, extensible architecture that enables customized agent composition and supports end-to-end single-cell and multi-omics analysis, spanning reinforcement-learning-augmented gene panel design, raw FASTQ processing, multimodal data integration, and three-dimensional spatial genomics reconstruction. Central to this framework, Pantheon-Evolve enables agentic code evolution, allowing the system to autonomously improve state-of-the-art batch-correction algorithms and new reinforcement-learning based gene panel design algorithms, achieving performance beyond manually designed baselines. We demonstrate the power of PantheonOS across multiple biological domains. In early mouse embryogenesis, PantheonOS automatically reconstructs three-dimensional spatial gene expression landscapes and resolves asymmetric Cer1 expression and paracrine Cer1-Nodal inhibition, revealing a robust proximal-distal axis at embryonic day six (E6.0). In human development, PantheonOS integrates fetal heart single-cell multi-omics with whole-heart 3D MERFISH+ data at post-conception week 12, uncovering spatially resolved molecular programs underlying heart disease ontogeny. Finally, an intelligent model-routing mechanism enables PantheonOS to adaptively select optimal virtual cell models across heterogeneous tasks, revealing minimal regulatory networks of cardiogenesis and predicting spatially resolved perturbation effects in the developing heart. Together, PantheonOS establishes a foundation for fully automated, evolvable, and domain-aware agentic analysis in genomics, and points toward a future in which scientific discovery is increasingly driven by self-improving AI systems across biology and beyond.

bioinformatics↗

bioRxiv2026

Combined Antibiotic and Herbicide Pollution Accelerates the Horizontal Transfer of Antibiotic Resistance Genes in Coastal Microbial Communities

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/708170v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@4c2513org.highwire.dtl.DTLVardef@116256corg.highwire.dtl.DTLVardef@f30846org.highwire.dtl.DTLVardef@1a44e00_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG The extensive use of herbicides and antibiotics in aquaculture has led to compounded pollution of coastal waters, marked by the co-occurrence of herbicides, antibiotic residues, and antibiotic resistance genes (ARGs). Plasmid-mediated conjugative transfer is a major driver of the dissemination and evolution of ARGs, yet the influence of herbicides alone or in combination with antibiotics on this process in aquatic bacterial communities remains unclear. In this study, we demonstrated that triazine herbicides, alone and in combination with a fluoroquinolone antibiotic, significantly promote the conjugative transfer of the broad-host-range RP4 plasmid. Using Escherichia coli DH5 as a donor, we observed increased plasmid transfer to multiple recipients, including E. coli HB101, Pseudomonas putida KT2440, and a natural coastal seawater microbial community. The enhanced transfer under co-exposure was associated with several interacting mechanisms, including elevated intracellular reactive oxygen species (ROS) and adenosine triphosphate (ATP) levels, increased cell membrane permeability, altered extracellular polymeric substance (EPS) composition, and upregulated expression of conjugation-related genes. Overall, this work underscores the potential role of combined herbicide and antibiotic contamination in shaping the microbial resistome of coastal ecosystems and provides insights to inform strategies for mitigating the spread of antibiotic resistance.

microbiology↗

bioRxiv2026

Neural Arming Niche in Tumor-Draining Lymph Nodes Programs CD8⁺T Cell Cytotoxicity via GZMB Norepinephrinylation

Lymph nodes are densely innervated neuro-immune hubs where naive T cells are primed and "armed" with cytotoxic machinery, yet how local neural cues in tumor-draining lymph nodes (tdLNs) set CD8 T cell effector reserves remains unclear. Here we show that tdLN-local sympathetic norepinephrine (NE) instructs CD8 T cell cytotoxic capacity by stabilizing the granzyme B (GZMB) pool through covalent norepinephrinylation. Exercise, used as a physiological perturbation, increases GZMB selectively in tdLN and tumor CD8 T cells without altering T cell abundance or cytokine output; tdLN sympathectomy abolishes these effects and reduces tdLN NE enrichment. Mechanistically, NE enters CD8 T cells and norepinephrinylates GZMB at Gln43, limiting UHRF1-mediated ubiquitination and proteasomal degradation. Genetic attenuation of norepinephrinylation destabilizes GZMB, impairs cytotoxicity, and accelerates tumor growth. Brief ex vivo NE conditioning increases GZMB reserves and improves adoptive tumor control.

cancer biology↗

bioRxiv2026

TRU-PE: A Universal, Trackable Prime Editor Toolkit for Robust Single- and Multi-Locus Genome Engineering

Prime editing (PE) holds immense promise for precise genome editing, but its wider adoption is constrained by large payloads and inefficient delivery in recalcitrant cell types. Here, we present TRU-PE (Trackable, Robust, and Universal Prime Editor), a toolkit that overcomes these barriers via a split-vector architecture coupled with fluorescence-guided enrichment. This design ensures stoichiometric component expression, yielding marked improvements in editing efficiency and accelerated workflows compatible with both plasmid and viral delivery. By integrating a polycistronic hCtRNA-sgRNA/pegRNA array, the derivative TRU-MPE system enables simultaneous editing of up to ten genomic loci. Furthermore, it enhances the performance of evolved PE editors, allows flexible vector stoichiometry and inspires novel editing reporters. Leveraging these capabilities, we successfully generated a combinatorial human iPSC-based platform for GATA2 deficiency, which accurately recapitulates genotype-specific hematopoietic defects. TRU-PE thus provides a user-friendly, scalable framework for robust single- and multi-locus genome engineering, accelerating functional genomics and therapeutic development.

genetics↗

bioRxiv2026

Redox-activated induction of Warburg-type metabolism in the adult heart

Proliferating cells rewire glucose metabolism away from oxidative ATP production towards glycolysis and anabolic branch pathways (the Warburg effect). In contrast, the contractile function of terminally differentiated cardiomyocytes in the heart critically depends upon mitochondrial ATP generation. Adult cardiomyocytes are largely non-proliferative but undergo hypertrophy in response to increased heart workload. A fundamental question in the field is how cardiac metabolism is modified to balance competing energetic and anabolic demands. We previously reported that the redox-signalling H2O2-generating enzyme, NADPH oxidase 4 (NOX4), facilitates compensated cardiac function in hearts undergoing hypertrophic remodelling. Here, we show that NOX4 induces Warburg-type reprogramming of glucose metabolism in the healthy heart, with increased flux into the pentose phosphate, serine and nucleotide biosynthetic pathways. Using an integrated multiomics approach, we uncover a NOX4-regulated network involving interplay between direct transcriptional activation of metabolic genes via NRF2 [aka NFE2L2] and ATF4 and a broader epigenetic regulation. This reprogramming of glucose intermediary metabolism along with previously reported NOX4-mediated enhancement of fatty acid oxidation may serve to optimally support the dual requirements of increased energy demand and remodelling in the heart. Our findings identify a novel paradigm for redox-regulated Warburg-type metabolic reprogramming in the terminally differentiated heart.

physiology↗

bioRxiv2026

Brain morphological pattern is associated with the presence, severity, and transition of transdiagnostic psychiatric disorders in preadolescents

Cognitive function, psychological processes, mental states, and behaviors are key dimensions of human subjective experience that separately relate to mental disorders across diagnostic categories. However, whether these dimensions are linked to common or distinct brain morphological patterns that convey risk or resilience for psychiatric disorders remains unclear. The current study is a longitudinal investigation on 11,875 youths from the Adolescent Brain Cognitive Development (ABCD) Study aged 9-10 years at baseline. A machine learning approach based on canonical correlation analysis was used to identify latent dimensional associations of cortical morphology (4 metrics: surface area, cortical and subcortical volume, cortical thickness, and sulcal/gyral depth) with multidomain behavioral assessments including cognitive scores and psychological measures indexing motivation, impulse control, mental states, and behaviors across a normative continuum from healthy to pathological. Across morphological measures, we identified a robust latent brain structural variate that correlated positively with cognitive performance and negatively with psychological measures indexing greater psychology. Notably, higher scores on this brain variate reflected larger cortical surface area and cortical volume--especially in the temporal gyri--together with a posterior-anterior gradient in cortical thickness, showing relatively greater thickness in occipital, parietal, and temporal cortices and lower thickness in cingulate and frontal regions. This brain variate and the related cognitive-psychological-behavioral variate remained stable at the 2-year follow-up, demonstrating temporal consistency. Importantly, the brain variate showed a dose-dependent relationship with the cumulative number of psychiatric diagnoses assessed concurrently and at 2-year follow-up, with lower brain variate scores being associated with higher numbers of comorbid diagnoses. In addition, the brain scores were associated with longitudinal transitions between healthy and diagnosed states over the 2-year study period, in which lower scores at baseline were associated with persistent psychiatric diagnoses whereas higher scores at baseline were associated with persistent healthy states, suggesting that the brain scores capture a vulnerability- resilience continuum for psychopathology. By revealing shared brain structural substrates across conventional diagnostic boundaries, these findings advance the neurodevelopmental understanding of psychiatric disorders and highlight the potential utility of morphology-informed approaches for early screening and intervention in youth.

developmental biology↗

bioRxiv2026

Brainwide dopamine dynamics across sleep-wake transitions

Dopaminergic signaling plays a critical role in regulating arousal and transitions between sleep states, yet brainwide dynamics underlying these transitions remain incompletely characterized. Here, we employed multi-site fiber photometry with the dopamine sensor GRAB-DA2m, combined with electroencephalogram (EEG)/electromyogram (EMG) recordings, to systematically assess regional dopamine (DA) dynamics across sleep-wake states in the medial prefrontal cortex (mPFC), striatal subregions, central amygdala (CeA), and midbrain nuclei in mice. We found that DA levels prominently increased in the mPFC, dorsolateral striatum (DLS), ventral tegmental area (VTA), substantia nigra pars compacta (SNc), and dorsal raphe nucleus (DRN) during transitions from non-rapid eye movement (NREM) sleep to wakefulness (WAKE), supporting their role in arousal. Conversely, DA decreased in the CeA and nucleus accumbens lateral parts (NAc-L) during these transitions. During transitions from NREM to rapid eye movement sleep (REM), DA elevations were observed in the CeA and middle accumbens subregion (NAc-M), rather than NAc-L, while other regions exhibited decreases. Cross-regional DA correlations revealed synchronized network activity across sleep-wake transitions. Optogenetic activation of VTA and DRN dopamine neurons induced robust DA release in cortical and subcortical regions, and chemogenetic activation promoted wakefulness selectively via VTA and DRN, but not SNc DA neurons. These results elucidate distinct brainwide DA dynamics across state transitions and highlight differential roles for DA signaling in modulating sleep-wake states.

neuroscience↗

bioRxiv2026

TACE reprograms RANKL-mediated differentiation of macrophages by activating the non-canonical pathway of IRF3

Inflammation is associated with an influx of inflammatory macrophages and increased activation and differentiation of osteoclasts. Receptor activator of NF-kB ligand (RANKL) is a key driver for osteoclast differentiation. However, the pathogenic mechanisms augmenting RANKL-induced osteoclast differentiation in inflammatory conditions are not fully elucidated. Here, we show that TNF- converting enzyme (TACE) plays a critical role in pathological bone erosion and enhances osteoclast differentiation in inflammatory conditions. Myeloid cell-specific TACE deletion in a murine arthritis model attenuates joint inflammation and bone destruction. TACE deficiency suppresses distal RANKL signaling in macrophages by increasing IRF3 activation, while enhancing proximal RANKL signaling, leading to the suppression of osteoclast differentiation. Mechanistically, IRF3 activation limits macrophage reprogramming by suppressing NFATc1 and HB-EGF in response to RANKL through a non-canonical pathway. HB-EGF, a TACE substrate, activates EGFR signaling and promotes osteoclastogenesis by inhibiting IRF3 activation. TACE regulates the reciprocal inhibition of the IRF3-HBEGF axis. Our study highlights a role for TACE as a rheostat, balancing both pro- and anti-osteoclastogenic signals.

cell biology↗

bioRxiv2026

Enhancing Pan-cancer Spatial Transcriptomics atSingle-cell Resolution with stPainter

Subcellular spatial transcriptomics technologies offer unprecedented views of tissue architecture but are fundamentally constrained by sparse gene panels and limited detection sensitivity. Current computational enhancement strategies typically rely on tissue-matched single-cell RNA sequencing (scRNA-seq) references and necessitate computationally intensive retraining for each dataset, impeding their scalability and clinical applicability. Here, we present STPAINTER, a conditional generative model that leverages a massive pretraining pan-cancer scRNA-seq atlas to universally enhance spatial transcriptomics data. Built upon a latent diffusion architecture with stochastic differential equation-guided generation, STPAINTER learns a universal manifold of cellular states to reconstruct genome-wide expression profiles from sparse spatial measurements. Uniquely, our pretraining paradigm enables zero-shot generalization and empowers downstream tasks by providing imputed transcriptomes and informative latent variables to enhance resolution at both the gene and cluster levels. Applied STPAINTER upon 6 spatial transcriptomics datasets of different cancer types, we demonstrate that our model empowers high-fidelity downstream analyses, including fine-grained subpopulation clustering and pathway enrichment. Furthermore, cross-validation with spatially resolved proteomics (CODEX) confirms the biological veracity of the imputed cellular landscapes. STPAINTER provides a robust, scalable framework for decoding complex tumor microenvironments without the need for auxiliary sequencing data.

bioinformatics↗