Biology subjects

Wang, R.

Publications and source records attributed to Wang, R..

At least 127 records · Page 7Linked to original sources

bioRxiv2025

Detection of prostate cancer in 3D pathology datasets via generative immunolabeling

Recent advancements in nondestructive 3D pathology offer a complement to standard histology by enabling comprehensive volumetric analyses of intact clinical specimens (e.g. biopsies). Prior studies have demonstrated the added prognostic value of 3D pathology for prostate cancer risk stratification by correlating 3D microarchitectural features with long-term patient outcomes. However, these analyses relied on coarse manual annotations of cancer-enriched regions for downstream analysis without fine-grained delineation between often-intermixed cancerous and benign glands. To address these limitations, we have developed a 3D computational pipeline: Synthetic Immunolabeling for Generative Heatmaps of Tumor (SIGHT). SIGHT relies on deep learning-based 3D image translation models, trained in a fully supervised fashion, to convert H&E-analog 3D pathology datasets into multiplexed 3D immunofluorescence datasets that facilitate tumor detection. Our implementation of SIGHT synthetically labels two cytokeratin markers that are differentially expressed in cancerous and benign prostate glands, which are used to generate explainable 3D heatmaps of cancer-enriched regions in prostate tissues. Validation of SIGHT against ground-truth annotations from a panel of genitourinary pathologists yields an average F1 score of 0.88 which is comparable to the average inter-pathologist agreement F1 score of 0.90. To demonstrate the value of SIGHT, we developed machine classifiers of recurrence risk based on 3D glandular histomorphometric features from 75 patients. Volumetric glandular analysis in SIGHT-identified cancer-enriched regions vs. all tissue regions yields an average Kaplan-Meier hazard ratio of 3.57 (1.6 - 7.9 CI) vs. 0.92 (0.45 - 1.89 CI).

pathology↗

bioRxiv2025

A minimal model for the role of Rim4 in regulating meiotic exit in budding yeast

Meiosis ensures formation of haploid gametes through two rounds of chromosome segregation after one round of DNA replication. How this complex cell cycle process is restricted to two and only two divisions is poorly understood. In budding yeast, RNA-binding protein Rim4 binds various mRNAs to prevent their translation. At the onset of meiosis II, phosphorylation and degradation of Rim4, along with the concomitant release of sequestered mRNA, has an important role in ensuring meiotic exit. Building on previous work, we developed a parsimonious mathematical model of meiotic termination that elucidates the role of Rim4-mRNA release and translation of AMA1 mRNA in the fidelity of meiotic exit. Central to our model is the accumulation of Ama1 protein, a meiosis-specific activator of APC/C. Our mathematical model predicted further outcomes, which we tested experimentally. We found that either slowing Rim4 degradation or disrupting APC/CAma1 activity delayed meiosis II. In some cells, this disruption prevented meiotic exit entirely, leading them to re-enter cell cycle oscillations after meiosis II. These findings demonstrate that the timely activation of this regulatory network is crucial for ensuring irreversible meiotic exit.

cell biology↗

bioRxiv2025

Quantifying spatiotemporal decoupling of GDGT-temperature relationships in a deep alpine lake

Glycerol dialkyl glycerol tetraethers (GDGTs), membrane lipids produced by archaea and some bacteria, are widely used in paleoclimate reconstructions due to their empirical relationship with temperature. However, their application in lakes is complicated by uncertainties in source attribution and environmental controls on their distribution. To address these constraints, we analyzed both branched and isoprenoid GDGTs (brGDGTs and isoGDGTs) in settling particles collected over 19 months using sediment traps at four depths (10, 15, 25 and 35 m) in Lake Lugu, a deep, stratified alpine lake in southwestern China. GDGT fluxes showed synchronous spatiotemporal variations across depths, with higher values during winter mixing than summer stratification, suggesting in situ production enhanced by nutrient upwelling during lake overturn. Correlations between GDGT distributions and high-resolution water temperature profiles revealed strong temperature sensitivity in isoGDGTs, particularly the Ring Index (RI), with peak correlations linked to mean temperatures [~]20 days prior to trap recovery, indicating a clear temporal lag in GDGT-temperature relationship. Moreover, stronger correlations with temperatures at overlying depths, implying vertical transport of isoGDGTs and a dominant autochthonous origin from the upper water column. In contrast, brGDGTs displayed weak or non-significant temperature dependence, likely reflecting distinct microbial sources or other controlling factors. These findings underscore the utility of isoGDGT-based proxies, particularly RI, while highlighting the importance of accounting for spatiotemporal offsets in GDGTs production when reconstructing paleotemperatures in deep, stratified lakes.

ecology↗

bioRxiv2025

Deep-learning triage of 3D pathology datasets for comprehensive and efficient pathologist assessments

Standard-of-care slide-based 2D histopathology severely undersamples spatially heterogeneous tissue specimens, with each thin 2D section representing <1% of the entire tissue volume (in the case of a biopsy). Recent advances in non-destructive 3D pathology, such as open-top light-sheet microscopy (OTLS), enable comprehensive high-resolution imaging of large clinical specimens. While fully automated computational analyses of such 3D pathology datasets are being explored, a potential low-risk route for accelerated clinical adoption would be to continue to rely upon pathologists to provide final diagnoses. Since manual review of these massive and complex 3D datasets is infeasible for routine clinical practice, we present CARP3D, a deep learning triage framework that identifies high-risk 2D cross sections within large 3D pathology datasets to enable time-efficient pathologist evaluation. CARP3D assigns risk scores to all 2D levels within a tissue volume by leveraging context from a subset of neighboring depth levels, outperforming models in which predictions are based on isolated 2D levels. In two use cases - risk stratification based on prostate cancer biopsies and screening for dysplasia/cancer in endoscopic biopsies of Barretts esophagus - AI-triaged 3D pathology, enabled by CARP3D, demonstrates the potential to improve the detection of high-risk diseases in comparison to slide-based 2D histopathology while optimizing pathologist workloads.

pathology↗

bioRxiv2025

Emotion Regulation in the Gradient Framework: Large-Scale Brain Organization Shapes Individual Differences in Reappraisal Success

Emotion regulation is essential for well-being and mental health, yet individuals vary widely in their emotion regulation success. Why? Traditional neuroimaging studies of emotion regulation often focus on localized neural activity or isolated networks, overlooking how large-scale brain organization may shape the integration of distributed systems and sub-processes supporting regulatory success. Here, we applied a novel system-level framework based on spatial gradients of macroscale brain organization to study variance in emotion regulation success. Using two large fMRI datasets (n=358, n=263), we projected global activation patterns from a laboratory emotion regulation task onto principal gradients derived from independent resting-state fMRI data from the Human Connectome Project. These gradients capture low-dimensional patterns of neural variation, providing a topographical framework within which complex mental phenomena, such as emotion regulation, emerge. In both datasets, individual differences in regulation success were predicted by systematic reconfiguration along Gradient 1--a principal axis differentiating unimodal and heteromodal brain areas. This gradient-based neural reconfiguration also predicted lower negative affect in daily life, as measured via smartphone-based experience sampling in a subset of participants (n=55). Meta-analytic decoding via Neurosynth revealed that Gradient 1 and regulation success align with multiple psychological processes, including social cognition, memory, attention, and negative emotion, suggesting this gradient reflects diverse, integrative demands during effective emotion regulation. These findings advance a network-level account of regulatory success, offering a biologically grounded, ecologically valid framework for understanding adaptive emotional functioning. Such gradient-based dynamics may serve as predictive biomarkers of regulatory success and inform targeted interventions in clinical populations. Significance StatementWhy are some people better than others at regulating their emotional experiences? This study employs a novel systems neuroscience framework to demonstrate that successful emotion regulation relies not only on localized brain activity, but also on how large-scale neural activation patterns systematically shift along principal axes of the brains intrinsic, large-scale spatial architecture. We demonstrate that variance in emotion regulation success--both in the laboratory (assessed using an established emotion regulation task) and in daily life (captured using phone-based experience sampling)--is associated with dynamic transitions along a principal neural gradient from sensory to transmodal association regions in the brain. This insight may lead to the development of new brain-based markers for predicting emotional resilience and tailoring mental health interventions.

neuroscience↗

bioRxiv2025

MuSHIN: A multi-way SMILES-based hypergraph inference network for metabolic model reconstruction

Genome-scale metabolic models (GEMs) are indispensable tools for probing cellular metabolism, enabling predictions of metabolic fluxes, guiding strain optimization, and advancing biomedical research. However, their predictive capacity is often compromised by incomplete reaction networks, stemming from gaps in biochemical knowledge, annotation inaccuracies, and insufficient experimental validations. Here we present MuSHIN (Multi-way SMILES-based Hypergraph Interface Network), a novel deep hypergraph learning method that integrates network topology with biochemical domain knowledge to predict missing reactions in GEMs. Evaluated on 926 high- and intermediate-quality GEMs with artificially removed reactions, MuSHIN significantly outperforms state-of-the-art methods, achieving up to a 17% improvement across multiple metrics and maintaining robust recovery even under severe network sparsity. Furthermore, MuSHIN substantially enhances phenotypic predictions in 24 draft GEMs associated with fermentation by resolving critical metabolic gaps, as validated against experimental measurements. Together, these findings highlight MuSHINs potential to advance GEM reconstruction and accelerate discoveries in systems biology, metabolic engineering, and precision medicine.

bioinformatics↗

bioRxiv2025

DREAM-GNN: Dual-route embedding-aware graph neural networks for drug repositioning

Drug repositioning presents a compelling strategy to accelerate therapeutic development by uncovering new indications for existing compounds. However, current computational methods are often limited in their ability to integrate heterogeneous biomedical data and model the intricate, multi-scale relationships underlying drug-disease associations, while large-scale experimental validation remains prohibitively resource-intensive. Here we present DREAM-GNN (Dual-Route Embedding-Aware Model for Graph Neural Networks), a multi-view deep graph learning framework that incorporates biomedical domain knowledge with two complementary graphs capturing both topological structure and feature similarity to enable accurate and biologically meaningful prediction of drug-disease associations. Extensive experiments on benchmark datasets demonstrate that DREAM-GNN significantly outperforms current state-of-the-art methods in recovering artificially removed repositioning candidates. These results establish DREAM-GNN as a robust and generalizable computational framework with broad potential to streamline drug discovery and advance precision medicine.

bioinformatics↗

bioRxiv2025

ZCCHC4 Promotes Translation of Replication-dependent Histone mRNAs by Recruiting Cytoplasmic eIF3 complex

Chromatin instability is a major driving force in tumorigenesis, so the correct chromatin structure is essential for maintaining genome stability during the various stages of the cell cycle and DNA template processes. However, there is limited research on how histones are regulated and whether insufficient histone supply leads to chromatin instability, thereby affecting the process of tumor generation. Our study demonstrates that ZCCHC4 exhibits dual subcellular localization in both the nucleus and cytoplasm. Notably, in the cytoplasm, ZCCHC4 facilitates the translation of replication-dependent (RD) histone mRNAs, potentially through direct interaction with components of the translation initiation machinery. Depletion of ZCCHC4 results in a global reduction in protein translation, particularly affecting the translation of replication-dependent histones. This translational impairment prolongs the S phase of the cell cycle, leading to insufficient histone supply, chromatin relaxation, and genomic instability. Moreover, ZCCHC4 knockout markedly inhibits cell proliferation, colony-forming ability, and the growth of xenograft tumors. In conclusion, this study demonstrates that ZCCHC4 plays a pivotal role in regulating global protein translation by interacting with the eIF3 complex, which is essential for efficient mRNA translation in cancer cells and for sustaining the supply of replication-dependent histones during the S phase.

cancer biology↗

bioRxiv2025

Disassociating cerebral vasomotion from low frequency spontaneous neurovascular coupling

Vasomotion, vascular oscillations at [~]0.1 Hz, may serve as a biomarker and therapeutic target for neurodegenerative diseases, but its origins, structure across brain vasculature, and correlation with neural activity remain unclear. This study examined the spatiotemporal characteristics of cerebral vasomotion and its relationship to neural activity in anaesthetised Hooded Lister rats using simultaneous recordings of neuronal activity and haemodynamics in motor and whisker barrel cortices. In a subset of rats, tissue oxygen was also measured. Blood pressure was pharmacologically modulated to alter vascular oscillations. We found that vasomotion was driven by the arterial tree. Two prominent activity patterns emerged: global vasomotion across the entire hemisphere and phasic vasomotion seen as a travelling wave running through the surface arteries. Moreover, vasomotion was associated with low tissue oxygen and was largely independent of spontaneous neural activity and therefore not a product of neurovascular coupling.

neuroscience↗

bioRxiv2025

Plasma Membrane Damage by Environmental Materials Enhances Cell-Cell Fusion and Impairs Immune Functions of Macrophages

Macrophages are the most abundant phagocytes and play an essential role in host defense. Previous studies have shown that many environmental materials can activate macrophages and trigger inflammatory responses. However, whether these exposures alter macrophage function in host defense remains unclear. This study found that many environmental materials (such as carbon nanotubes, tungsten carbide [WC], and detergents) can damage the plasma membranes of macrophages. This damage leads to decreased reactive oxygen species (ROS) production and phagocytosis but elevated cell-cell fusion. In vivo, airway exposure to laundry detergent impaired the recruitment of macrophages and other myeloid cells to the lung and dramatically dampened protective TH1 and TH17 cell responses, leading to increased susceptibility to Candida infection in mice. Overall, our data indicate that exposure to environmental materials compromises macrophage membrane integrity and impairs host defense. These findings may aid in the development of effective preventive and therapeutic strategies.

immunology↗

bioRxiv2025

Ultrastructural analysis of synapses after induction of spike-timing-dependent plasticity

Repeated sequential activation of connected neurons causes lasting changes in synaptic strength, a process known as spike-timing-dependent plasticity (STDP). Recently, sequential spike patterns have been induced without electrodes, using two spectrally separated channelrhodopsins. However, due to the difficulty of labeling and localizing the few connecting synapses between the stimulated pre and postsynaptic neurons ([~]1-5 per neuron pair), ultrastructural analysis after STDP has not been reported. Here, we optogenetically induce STDP at CA3-CA1 hippocampal synapses and identify stimulated boutons and spines in CA1 using transmission electron microscopy (TEM). Presynaptic CA3 neurons express vesicle-targeted horseradish peroxidase, cre recombinase and cre-dependent ChrimsonR, a red light activatable channelrhodopsin. Postsynaptic neurons express violet light activatable CheRiff and dAPEX2, an enhanced ascorbate peroxidase. In transmission electron microscopy, presynaptic boutons and postsynaptic spines are readily identifiable with well-preserved ultrastructural features. Our labeling strategy allows ultrastructural analysis of optogenetically manipulated neurons and their synapses.

neuroscience↗

bioRxiv2025

Volume Control for a Cortical Network

Conventional implantable neuromodulation relies on brief electrical or optical pulses that directly evoke action potentials in targeted neurons. While powerful, such approaches primarily impose activity rather than modulate how circuits process ongoing signals. Here, we introduce ionic direct current (iDC) delivered through non-penetrating, electrolyte-filled microcatheters on the cortical surface as an alternative strategy for neuromodulation that adjusts cortical gain without directly driving spikes. In rat primary somatosensory cortex, laminar recordings showed that cathodic iDC attenuated and anodic iDC amplified spontaneous and sensory-evoked activity while preserving the temporal structure of population responses. Computational modeling implicated polarization at the axon initial segment as the mechanism underlying bidirectional gain control. In awake animals, iDC modulation altered tactile sensitivity, demonstrating behavioral relevance. These findings establish iDC as a rapidly reversible, spatially precise method for modulating cortical processing, offering a fundamentally different mode of neuromodulation that bridges circuit physiology and behavior without overriding native neural dynamics. TEASERIonic direct current modulates cortical gain via subthreshold polarization, preserving native neural dynamics.

neuroscience↗

bioRxiv2025

Versatile Vasculature Chips for Ultrasound Localization Microscopy

Ultrasound localization microscopy (ULM) has revolutionized microvasculature imaging by surpassing the diffraction limit via microbubbles. ULM demonstrates exceptional potential to resolve micrometer-scale vascular structures in both preclinical and clinical studies. However, its performance evaluation remains challenging primarily due to the lack of reference microvascular phantoms featuring micrometer-scale, hierarchical branches, and realistic vascular structures. Inspired by microfluidic chip techniques, we present an organ-on-a-chip protocol for fabricating agarose-based micro-vessel network phantoms with ground truth. The vasculature pattern offers design versatility, enabling on-demand customization. We experimentally demonstrated the feasibility of the vasculature phantom using two adapted patterns. The first was a leaf pattern, which exhibited intrinsic quasi-two-dimensional venation network with hierarchical and branching channels similar to animal vasculature. The second was a kidney pattern, which was based on a two-dimensional projection of real human vasculature obtained from micro computed tomography. The microbubble solution was perfused into the phantoms by capillary force and gravity. The ULM-reconstructed vasculature maps agreed well with the ground truth. ULM achieved a high sensitivity of 0.97 and 0.95, but a low precision of 0.37 and 0.60, for the leaf and kidney phantom, respectively. The results indicated the capability of ULM to reconstruct vessel structures while making many false positive predictions. The proposed protocol holds significant promise for the development and optimization of ultrasound microvascular imaging techniques.

bioengineering↗

bioRxiv2025

DiffusionST: A deep generative diffusion model-based framework for enhancing spatial transcriptomics data quality and identifying spatial domains

Recent advancements in spatial transcriptomics technology have generated substantial volumes of spatial transcriptome data. However, the quality of this data is often compromised due to the limitations of current sequencing technologies. To address this issue, DiffusionST proposes a method for imputing spatial transcriptomics data and clustering the imputed data. The method employs a graph convolutional network (GCN) model combined with a newly designed loss function, denoising data using the zero-inflated negative binomial (ZINB) distribution, and data enhancement through a diffusion model to improve clustering accuracy. DiffusionST demonstrates superior clustering accuracy compared to six of the most popular spatial transcriptomics clustering algorithms. DiffusionST also excels in data imputation when compared to five single-cell RNA sequencing (scRNA-seq) imputation algorithms. Additionally, DiffusionSTs robustness against noise is quantitatively validated by manually introducing random dropout noise into the dataset, where our model significantly enhances the quality of spatial transcriptomic data. Moreover, DiffusionST is well-suited for high-resolution spatial transcriptomics data and has been demonstrated, through survival analysis and cell-cell communication studies, to dissect spatial domains within breast cancer tissues. These findings provide strong evidence of DiffusionSTs efficacy in handling spatial transcriptomic data especially with strong noise, making it a valuable tool in this field.

bioinformatics↗

bioRxiv2025

Inhibiting RhoA Activation via GDP-state Stabilization to Relieve Heart Failure

High rates of heart failure (HF) morbidity and mortality have made targeting myocardial remodeling--particularly hypertrophy and fibrosis--a key therapeutic focus. RhoA, which regulates cytoskeletal reorganization and cell migration, plays a role in this process. However, RhoA has long been considered "undruggable", due to its strong binding to its endogenous substrates, GDP/GTP, and the lack of well-defined pockets for drug targeting. Here, we discovered a cryptic pocket proximate to GDP within RhoA and identified a natural product, AH001, binds here and interacts with GDP, stabilizing RhoAs interaction with its endogenous inhibitor, RhoGDI. AH001 reduced the downstream MRTFA nuclear translocation and downregulated fibrosis/hypertrophy proteins. Consequently, AH001 mitigated myocardial remodeling in multiple HF animal models, and in the 3D myocardial tissue model. Our findings highlight the therapeutic potential of inhibiting RhoA activation in myocardial remodeling, ultimately targeting HF, and offer a promising avenue for developing reversible inhibitors against undruggable GTPases.

pharmacology and toxicology↗

bioRxiv2025

METTL5 Blockade Enhances Anti-Tumor Immune Response via Inducing Neoantigen Generation

Tumor neoantigens play a pivotal role in eliciting tumor-specific immune responses and holds the promise for personalized immunotherapy. However, previous studies mainly focused on the tumor-specific neoantigens derived from genomic mutation and aberrant RNA splicing, limiting the repertoire of targetable neoantigens. Here, we demonstrate that inhibition of rRNA methyltransferase METTL5 translationally increases neoantigen production and enhances anti-tumor immunity. Mechanistically, METTL5-mediated m6A modification at the decoding center of small ribosomal subunit maintains the proper function of ribosome during mRNA translation. METTL5-deficiency decreases translation fidelity and increases production of tumor cell-specific antigens derived from non-canonical translation. Furthermore, we found that Mettl5-depletion increased CD8T cell infiltration density and T cell receptor (TCR) repertoire diversity in murine tumor models. Importantly, this immunostimulatory effect strictly depended on intact antigen presentation pathways, suggesting that Mettl5 knockout exerts its effects primarily through neoantigen generation. Together, this study uncovers the intrinsic mechanisms sustaining mRNA translation accuracy, elucidates a novel source of tumor neoantigen generation, and proposes a new strategy to enhance immunotherapy through targeting mRNA translation.

cancer biology↗

bioRxiv2025

Central nervous system atlas of larval zebrafish constructed using the morphology of single excitatory and inhibitory neurons

Single-neuron morphology mapping is fundamental for deciphering brain architecture and function, yet in vertebrates it remains limited in spatial coverage and cell-type information. Here we present a nervous system-wide single-neuron morphology atlas of larval zebrafish with annotated excitatory/inhibitory identity and dendrite-axon polarity. The atlas comprises >20,000 reconstructed neurons from >13,400 animals, spanning the central and peripheral nervous system and representing [~]24.5% of the total excitatory and inhibitory neurons in the brain, all integrated into a common physical space with neuroanatomical parcellations. We define >500 excitatory and inhibitory neuronal morphotypes and construct putative inter-cell-type and interregional connectomes. Projection and morphotype analyses reveal distinct laterality and directionality biases and modular innervation patterns in excitatory and inhibitory neurons, while network analyses uncover fundamental connectivity motifs, hubs, and sensorimotor pathways. Accessible through custom-built interactive platform ZExplorer, this atlas provides a foundational resource for multimodal data integration, circuit analysis, computational modeling, and hypothesis-driven research.

neuroscience↗

bioRxiv2025

Contrastive Learning-Enhanced Drug Metabolite Prediction Drives the Discovery of an Orally Bioavailable RSK4 Inhibitor for Esophageal Cancer

Drug metabolism liabilities are a major bottleneck in drug development, necessitating accurate metabolite prediction tools. While AI-driven methods have advanced, challenges remain in achieving high-precision, robustness, broad reaction coverage and seamless integration into the drug development workflow. To address this, we first constructed the most comprehensive human-specific drug metabolism database to date, encompassing 11,665 human metabolic reactions and 2,497 high-quality rules. Leveraging this resource, we developed ConMeter (Contrastive learning and feature interaction based Metabolic Reaction prediction), a novel model integrating chemical feature interaction and contrastive learning. ConMeter demonstrates significant improvements, achieving [~]80% accuracy in identifying at least one metabolite within the top 5 predictions and showing an average 25% relative enhancement in ranking true positives compared to state-of-the-art methods. The comprehensive database also expands the range of predictable metabolic reactions. To demonstrate the utility of ConMeter, we integrated it into a metabolite prediction-guided drug design strategy aimed at addressing the low oral bioavailability of a metabolically vulnerable RSK4 lead compound -- a pervasive issue among known RSK inhibitors. This strategy successfully yielded R636, a novel RSK4 inhibitor exhibiting remarkably improved bioavailability (a 63-fold increase, reaching 63% absolute bioavailability) while maintaining its potent activity. Notably, R636 showed favorable safety profile and significant anti-tumor efficacy against esophageal squamous cell carcinoma (ESCC) in vitro and in two distinct patient-derived xenograft (PDX) mouse models. This work introduces ConMeter as a powerful and practical tool for high-accuracy metabolite prediction, demonstrating its potential to accelerate lead optimization, exemplified by the discovery of R636 as a promising orally bioavailable candidate for ESCC.

cancer biology↗