Biology subjects

Zhang, W.

Publications and source records attributed to Zhang, W..

At least 235 records · Page 13Linked to original sources

bioRxiv2025

FlyTomo: A streamlined software for on-the-fly cryo-ET data processing and diagnosis

Cryo-electron tomography (Cryo-ET) combined with subtomogram averaging (STA) enables the structural elucidation of macromolecular assemblies in their native environments. However, the widespread adoption of cryo-ET has been limited by its labor-intensive, expertise-dependent data processing workflow. Here we present FlyTomo, a software that streamlines data processing from frame alignment to STA with high-throughput for authentic cryo-ET scenarios. During data acquisition, FlyTomo performs real-time diagnosis, enabling prompt feedback on sample quality, microscope performance and structural features. After acquisition, it aggregates diagnostic metrics into an interactive overview, guiding users through data review and refinement. FlyTomo also curates raw and processed data into structured directories to simplify data management and archiving. We validated FlyTomo across a diverse set of authentic cryo-ET samples, including purified enveloped viruses and cryo-lamellae, on multiple microscopes and cameras, achieving structures at resolutions ranging from 3.4 to 5.7 [A]. Collectively, by integrating accuracy, scalability and usability, FlyTomo reduces the technical barrier for in situ structural biology using cryo-ET.

biophysics↗

bioRxiv2025

Engineering light robustness: Adaptive evolution uncovers new genetic determinants of HL tolerance in Synechocystis

Excess light absorption causes severe photo-oxidative damage and limits photosynthetic efficiency. Enhancing high-light (HL) tolerance and energy utilization in photosynthetic cyanobacteria holds great potential for sustainable biomanufacturing. Here, using Synechocystis sp. PCC 6803 (Syn6803) as a model, we generated eight independently evolved strains tolerant to hyper-HL (2000 mol photons/m2/s) through approximately two years of natural adaptive laboratory evolution (ALE). Remarkably, these strains circumvented common evolutionary trade-offs; all demonstrated markedly enhanced quantum yields and photosynthetic performance, with four strains exhibiting a 121.71%-168.36% increase in biomass accumulation under HL. When equipped with the heterologous sucrose transporter CscB, sucrose productivity under HL increased by up to 208.33%. Whole-genome resequencing unveiled a total of 77 mutations across the eight lineages, uncovering a diverse polygenic landscape for HL adaptation. Reverse genetics combined with structural modeling highlighted pivotal roles for regulatory and structural loci, including slr0758 (KaiC1) and slr1513 (SbtB), in modulating circadian rhythmicity and inorganic carbon uptake to prevent photo-oxidative overload. Overall, our findings uncover diverse evolutionary routes toward high-light acclimation and provide superior cyanobacterial chassis for photosynthetic biotechnology.

microbiology↗

bioRxiv2025

Palmitoylation-dependent activation of NADK promotes NADP+ synthesis and tumorigenesis

NAD kinase (NADK) is the sole cytosolic enzyme that catalyzes the synthesis of nicotinamide adenine dinucleotide phosphate (NADP+) from NAD+. NADP+ is essential for anabolic reactions and redox balance. Here, we show that palmitate facilitates NADP+ synthesis by enhancing NADK palmitoylation and activity. NADK is post-translationally S-palmitoylated on three cysteine residues (Cys22, Cys23, and Cys26) within the amino-terminal domain by the protein-acyl transferase ZDHHC5, which stimulates NADK activity. Fatty acids activate NADK by enhancing its palmitoylation and relief of an autoinhibitory function inherent to its amino terminus. Furthermore, ZDHHC5-/- mice showed defect in NADK palmitoylation and NADP+ production. Clinically, elevated expression of ZDHHC5 in pancreatic cancer patients was associated with increased NADK palmitoylation and correlated with poor prognoses for patients. These data reveal that fatty-acid and ZDHHC5-mediated palmitoylation has a critical role in NADP+ synthesis and cancers.

cancer biology↗

bioRxiv2025

Towards a mental programming neural circuit: Insights from working memory sequence manipulation

Cognitive reasoning, also known as mental programming, is fundamental to our intelligence. A central function in mental programming is working memory (WM), involving both temporarily maintaining the information and manipulating it based on rules. While the neural mechanisms of WM maintenance have been extensively studied, those governing WM manipulation remain largely unknown. To bridge this gap, the present study focuses on an elementary operation in WM manipulation: re-ordering items in WM sequences. We propose a functional, biologically plausible neural circuit model that consists of two interconnected modules: a memory module composed of continuous attractor-based memory slots that store item features, and a control module sending gain-modulating commands to orchestrate specific operations in the memory module. The model successfully implements two-item swapping in a WM sequence, generating neuronal responses similar to recent primate experiments of WM sequence manipulation. By incorporating principles from the algebraic permutation group, we generalize the circuit model to accommodate more complex sequence manipulations. This math foundation reveals how arbitrary permutations can be decomposed into sequences of elementary swapping operations, which can be generated by a hierarchical tree-structured control circuit module. And the mutual inhibition within the control tree ensures that only one program is being executed at the same time. Our study establishes overarching connections among mental programming neural circuit models, neuro-science experiments, and abstract algebraic structure. These insights enhance our understanding of the neural underpinnings of cognitive reasoning and inspire the design of artificial cognitive systems.

neuroscience↗

bioRxiv2025

Mild subcortical stroke induces widespread astrogliosis independent of microglia and age

Ischemic stroke induces a plethora of pathophysiological changes, including neuroinflammation and chronic cerebrovascular dysfunction. In humans, even small, silent strokes can trigger these pathologies, which can spread to brain regions far beyond the infarct and persist chronically, ultimately worsening prognosis and increasing the risk for vascular dementia and Alzheimers disease. The cause of this extensive pathology is unknown, but reactive astrocytes and microglia are likely contributors. Here, we describe an optimized short-duration middle cerebral artery occlusion model that produces a clinically relevant small stroke confined to subcortical regions, similar to most silent strokes in humans. We termed this model the mild subcortical infarct (MSCI). We then mapped the spatiotemporal extent of reactive astrocytes and microglia during the sub-acute period (1, 3, and 7 days) following MSCI. We observed that reactive astrogliosis develops more rapidly and spreads more extensively, permeating the entire middle cerebral artery territory, compared to the reactive microglial response following this small infarct. Microglial depletion resulted in larger infarct sizes but did not prevent the reactive astrocytes, suggesting that ischemia-driven astrogliosis is largely microglia-independent. Lastly, we show that aging mice exposed to MSCI exhibit a comparably strong response of reactive astrocytes and microglia as young mice. We propose that MSCI is a novel and valuable model for examining the subtle yet highly important chronic effects of stroke. It may be especially useful for investigating the influence of reactive astrogliosis on pathologies like neuroinflammation and cerebrovascular dysfunction in regions distal from the primary injury site.

neuroscience↗

bioRxiv2025

ProtLoc-GRPO: Cell line-specific subcellular localization prediction using a graph-based model and reinforcement learning

Subcellular localization prediction is crucial for understanding protein functions and cellular processes. Subcellular localization is dependent on tissue and cell lines derived from different cell types. Predicting cell line-specific subcellular localization using the information of protein-protein interactions (PPIs) offers deeper insights into dynamic cellular organization and molecular mechanisms. However, many existing PPI networks contain systematic errors that limit prediction accuracy. In this study, we propose a reinforcement learning approach, ProtLoc-GRPO, to enhance subcellular localization prediction by optimizing the structure of the underlying PPI network. ProtLoc-GRPO learns to rank and retain the most informative PPI edges to maximize the macro-F1 score for cell line-specific subcellular localization. Our approach yields a 7% improvement in macro-F1 score over the baseline. We further evaluate its robustness across various edge pruning rates and benchmark it against conventional pruning strategies. Results show that our proposed method consistently outperforms existing approaches. To our knowledge, this work represents the first study to predict cell line-specific protein subcellular localization and the first application of the Group Relative Policy Optimization (GRPO) framework to a graph-based model for bioinformatics tasks.

bioinformatics↗

bioRxiv2025

NTM-DB: A Comprehensive Non-Tuberculosis Mycobacteria Genomic Database

Non-tuberculous mycobacteria (NTM) are a major group of environmental bacteria, with approximately one-third of them causing serious human infections, particularly respiratory diseases. The global rise in the prevalence and severity of NTM infections has posed a major public health challenge. While high-throughput sequencing has generated vast genomic data on NTM, there remains a lack of comprehensive resources for cross-species genomic analysis. To address these limitations, we have developed a specialized database called NTM-DB (https://ngdc.cncb.ac.cn/ntmdb) tailored for NTM researchers and clinicians. NTM-DB offers the most comprehensive collection of NTM genomic and bioinformatic resources, including 16,469 genome assemblies (13,134 newly assembled genomes), 189 type/standard strain genomes representing 177 species and 12 subspecies, 705 MLST types, 33,240 resistance genes, and 74,315 virulence genes. A user-friendly interactive website has been constructed to enable efficient browsing, MLST profiling, searching, online analysis, and downloading of the aforementioned data. Notably, with online analysis tools, users can perform customized genotyping, cross-species phylogeny, pan-genome, and virulence and drug resistance gene annotation analyses using our data and/or their uploaded data. Overall, with its comprehensive data, intuitive interface, and powerful analysis tools, NTM-DB serves as an important resource and reference for NTM researchers and clinicians, improving diagnosis and treatment for various NTM-related diseases, and supporting both scientific discovery and clinical practice.

microbiology↗

bioRxiv2025

Clonal lineage tracing of innate immune cells in human cancer

Innate immune cells constitute the majority of the tumor microenvironment (TME), where they mediate both natural anti-tumor immunity and immunotherapy responses. While single-cell T- and B-cell receptor sequencing has provided fundamental insights into the clonal dynamics of human adaptive immunity, the lack of appropriate tools has precluded similar analysis of innate immune cells. Here, we describe a method that leverages somatic mitochondrial DNA (mtDNA) mutations to reconstruct clonal lineage relationships between single cells across cell types in native human tissues. We jointly sequenced single-cell transposase-accessible chromatin and mtDNA to profile n=124,958 cells from matched tumor, non-involved lung tissue (NILT), and peripheral blood of early-stage non-small cell lung cancer (NSCLC) patients, as well as n=93,757 cells from matched tumor and peripheral blood of ovarian cancer patients. Single-cell concomitant profiling of lineage and cell states of thousands of immune cells resolved clonality across cell types, tissue sites, and malignancies. Clonal tracing of innate immune cells demonstrates that TME-resident myeloid subsets, including macrophages and type 3 dendritic cells (DC3), are clonally linked to both circulating and tissue-infiltrating monocytes. Further, we identify distinct DC-biased and macrophage-biased myeloid clones, enriched in the tumor and NILT, respectively, and find that their circulating monocyte precursors exhibit distinct epigenetic profiles, suggesting that myeloid differentiation fate may be predetermined before TME infiltration. These results delineate the clonal pathways of intratumoral myeloid cell recruitment and differentiation in human cancer and suggest that remodeling of the tumor myeloid compartment may be peripherally programmed.

genomics↗

bioRxiv2025

MACGA: Multi-scale Adaptive Convolution with Graph Attention for LncRNA-Disease Association Prediction

Accurate prediction of lncRNA-disease associations (LDAs) is crucial for understanding complex disease mechanisms and advancing precision medicine. Existing computational methods based on graph neural networks (GNNs) often suffer from over-smoothing issues, limiting their predictive accuracy. To address this challenge, we propose MACGA, a novel neural network framework integrating Multi-scale Adaptive Convolution with Graph Attention networks. MACGA effectively captures both local neighborhood information and global high-order structural features by combining PCA-based dimensionality reduction, local node feature extraction via graph attention mechanisms, and multi-scale feature aggregation through adaptive convolution. Extensive experiments using five-fold cross-validation demonstrated that MACGA significantly outperformed four representative state-of-the-art methods (AUC=0.9463, AUPR=0.4119). Case studies on prostate and colon cancers further validated the reliability of MACGA, with top-ranked lncRNA predictions well-supported by existing experimental literature. Moreover, we extended the proposed framework to predict disease-drug associations via lncRNA-bridging strategy, highlighting its potential for drug repositioning. Overall, MACGA provides a powerful computational tool for accurately predicting lncRNA-disease associations and exploring novel therapeutic opportunities. The source code and dataset used in this paper are available at: https://github.com/huifeideyu518/MACGA.

bioinformatics↗

bioRxiv2025

Stimuli-driven cyclical content exchange in a composite synthetic cell

Engineering sophisticated behaviours in synthetic cells lacking complex biomolecular machinery remains a central challenge in synthetic biology. Here, we introduce a protein-free approach for dynamic content modulation in liposome-based synthetic cells using an internal gelation strategy. By crosslinking a polymer hydrogel within the lumen of giant vesicles and tethering it to the inner membrane leaflet, we create a composite architecture that enables controlled and reversible membrane permeabilisation via osmotic swelling and shrinking, facilitating externally gated material exchange without reconstituted protein pores or electroporation. Simultaneously, the hydrogel matrix affords control over membrane fluidity and the diffusion of cytoplasmic clients. We deploy the transport-regulation platform to construct a synthetic-cell bioreactor whereby reversible membrane permeabilisation enables content supplementation and fuels a biocatalytic reaction. The composite gel-GUV chassis provides an adaptive, robust and expandable solution for engineering increasingly modular and functional synthetic cellular systems. These findings may echo how primordial cells harnessed environmental fluctuations for content exchange through chemically distinct pathways.

synthetic biology↗

bioRxiv2025

Canonical recurrent neural circuits: A unified sampling machine for static and dynamic inference

AO_SCPLOWBSTRACTC_SCPLOWThe brain lives in an ever-changing world and needs to infer the dynamic evolution of latent states from noisy sensory inputs. Exploring how canonical recurrent neural circuits in the brain realize dynamic inference is a fundamental question in neuroscience. Nearly all existing studies on dynamic inference focus on deterministic algorithms, whereas cortical circuits are intrinsically stochastic, with accumulating evidence suggesting that they employ stochastic Bayesian sampling algorithms. Nevertheless, nearly all circuit sampling studies focused on static inference with fixed posterior over time instead of dynamic inference, leaving a gap between circuit sampling and dynamic inference. To bridge this gap, we study the sampling-based dynamic inference in a canonical recurrent circuit model with excitatory (E) neurons and two types of inhibitory interneurons: parvalbumin (PV) and somatostatin (SOM) neurons. We find that the canonical circuit unifies Langevin and Hamiltonian sampling to infer either static or dynamic latent states with various moving speeds. Remarkably, switching sampling algorithms and adjusting models internal latent moving speed can be realized by modulating the gain of SOM neurons without changing synaptic weights. Moreover, when the circuit employs Hamiltonian sampling, its sampling trajectories oscillate around the true latent moving state, resembling the decoded spatial trajectories from hippocampal theta sequences. Our work provides overarching connections between the canonical circuit with diverse interneurons and sampling-based dynamic inference, deepening our understanding of the circuit implementation of Bayesian sampling.

neuroscience↗

bioRxiv2025

Stage- and tissue-specific gene editing using 4-OHT inducible Cas9 in whole organism

Vertebrate genes usually function in specific tissues at specific stages, and thus their functional studies require conditional knockout or editing. In the zebrafish, spatiotemporally inducible genome editing, especially during early embryonic development, is still challenging. Here, we report inducible Cas9-based gene editing in specific cell types at desired stages in zebrafish. We show that the nCas9ERT2 fusion protein consisting of Cas9 and estrogen receptor flanked by two nuclear localization signals is normally located in the cytoplasm and efficiently translocated into the nucleus upon 4-hydroxytamoxifen (4-OHT) treatment in cultured cells or embryos. As a proof-of-concept, we demonstrate that target genes in primordial germ cells or germ cells of transgenic zebrafish embryos or adults with stable expression of nCas9ERT2 and gene-specific guide RNAs (gRNAs) can be induced to mutate by application of 4-OHT. This inducible nCas9ERT2 system also works in mouse early embryos. Thus, this inducible gene editing approach will be another choice for studying the temporospatial function of genes at the organismal level. Hence, the inducible nCas9ERT2 system expands the tissue- and stage-specific gene editing toolkit.

developmental biology↗

bioRxiv2025

Natural gradient Bayesian sampling: an innate algorithm emerges in canonical cortical circuits

AO_SCPLOWBSTRACTC_SCPLOWAccumulating evidence suggests the canonical cortical circuit, consisting of excitatory (E) and diverse classes of inhibitory (I) interneurons, implements Bayesian posterior sampling. However, most of the identified circuits sampling algorithms are simpler than the nonlinear circuit dynamics, suggesting complex circuits may implement more advanced algorithms. Through comprehensive theoretical analyses, we discover the canonical circuit innately implements natural gradient Bayesian sampling, which is an advanced sampling algorithm that adaptively adjusts the sampling step size based on the local geometry of stimulus posteriors measured by Fisher information. Specifically, the nonlinear circuit dynamics can implement natural gradient Langevin and Hamiltonian sampling of uni- and multi-variate stimulus posteriors, and these algorithms can be switched by interneurons. We also find that the non-equilibrium circuit dynamics when transitioning from the resting to evoked state can further accelerate natural gradient sampling, and analytically identify the neural circuits annealing strategy. Remarkably, we identify the approximated computational strategies employed in the circuit dynamics, which even resemble the ones widely used in machine learning. Our work provides an overarching connection between canonical circuit dynamics and advanced sampling algorithms, deepening our understanding of the circuit algorithms of Bayesian sampling.

neuroscience↗

bioRxiv2025

Multi-season analysis reveals hundreds of drought-responsive genes in sorghum

Persistent drought affects global crop production and is becoming more severe in many parts of the world in recent decades. Deciphering how plants respond to drought will facilitate the development of flexible mitigation strategies. Sorghum bicolor L. Moench (sorghum), a major cereal crop and an emerging bioenergy crop, exhibits remarkable resilience to drought. To better understand the molecular traits that underlie sorghums remarkable drought tolerance, we undertook a large-scale sorghum gene expression profiling effort, totaling nearly 1,500 transcriptome profiles, across a 3-year field study with replicated plots in Californias Central Valley. This study included time-resolved gene expression data from roots and leaves of two sorghum genotypes, BTx642 and RTx430, with different pre-flowering and post-flowering drought-tolerance adaptations under control and drought conditions. Quantification of genotype-specific drought tolerance effects was enabled by de novo sequencing, assembly, and annotation of both BTx642 and RTx430 genomes. These reference-quality genomes were used to construct a pan-gene set for characterizing conserved and genotype-specific expression. By integrating time-resolved transcriptomic responses to drought in the field across three consecutive years, we identified a set of drought-responsive genes that responded similarly in all three years of our field study. This expansive dataset represents a unique resource for sorghum and drought research communities and provides a methodological framework for the integration of multi-faceted time-resolved transcriptomic datasets.

genomics↗

bioRxiv2025

Functional genomics and tumor microenvironment analysis reveal prognostic biological subtypes in Mantle cell lymphoma

Mantle cell lymphoma (MCL) is a genetically and clinically heterogeneous B-cell malignancy. We studied two MCL cohorts with differing treatment patterns: one enriched for immunochemotherapy, the other for chemotherapy alone. TP53 alterations were consistently associated with poor prognosis, whereas ATM mutations correlated with improved outcomes following rituximab-based chemotherapy. Based on recurrent genetic events, six clusters were identified and refined into three prognostic groups: high-risk (TP53 mutations and deletions at 17p13.3, 13q14.2, and 19p13.3), intermediate-risk (ATM and epigenetic regulator mutations, or gains at 8q/17q/15q), and low-risk (lacking TP53 alterations, rare ATM mutations without 11q deletions, gains at 3q, deletions at 6q). Transcriptomic analysis revealed enrichment of proliferation, metabolism-promoting gene signatures in high-risk; angiogenesis and NOTCH signaling in intermediate-risk; and proinflammatory-related (i.e., IFN, TNF) in low-risk MCLs. Multi-proteomic spatial profiling using imaging mass cytometry (IMC) demonstrated enrichment of CD8 T cells with high expression of exhaustion markers and dominant population of myeloid cells skewed toward an M2-like phenotype. Compared to ATM-perturbed tumors, TP53-perturbed tumors exhibited enriched SOX11 tumor cells and enhanced tumor-immune cell interactions. Functional analysis revealed that p53 represses BCR signaling through PTPN6 activation. Collectively, these findings highlight distinct molecular and immune landscapes and reveal therapeutic vulnerabilities in high-risk TP53-altered MCL.

cancer biology↗

bioRxiv2025

Neurogranin Unlocks the L-type Ca2+ Channel and Directs Calmodulin Delivery

Neurogranin (Ng) is enriched in the postsynaptic density, with mounting evidence of its importance to neural pathophysiology. Ng has long been recognized as a dedicated calmodulin (CaM)- binding/buffering protein. This study demonstrates that Ng, through its CaM-binding IQ domain, directly binds the distal C-terminus (DCT) of the L-type Ca2+ (CaV1) channel, as a de novo mechanism regulating excitation-transcription coupling. The Ng/DCT interaction attenuates autonomous C- terminus-mediated inhibition and promotes specific CaM delivery to CaV1, cooperatively enhancing channel gating (activation and inactivation), Ca2+ influx and downstream neuronal signaling. Phosphorylation of Ng leads to the dissociation of the CaM C-lobe, while transiently forming the linkage with the CaM N-lobe. In contrast to neurodegenerative disease-related Ng mutants, WT Ng, within the above transient complex, enables the "targeted CaM transfer" to the proximal C-terminus of CaV1. Collective evidence from electrophysiology, binding assays, structural modeling and Ca2+ imaging supports a paradigm of dynamic intra- and inter-molecular interactions/facilitations underlying Ng and CaV1, both of which are representative CaM-binding proteins. This work proposes a mechanistic scheme potentially applicable to a broad scope of CaM-related physiology.

biophysics↗

bioRxiv2025

Fluoro-forest: A random forest workflow for cell type annotation in high-dimensional immunofluorescence imaging

Cyclic immunofluorescence (IF) techniques enable deep phenotyping of cells and help quantify tissue organization at high resolution. Due to its high dimensionality, workflows typically rely on unsupervised clustering, followed by cell type annotation at a cluster level for cell type assignment. Most of these methods use marker expression averages that lack a statistical evaluation of cell type annotations, which can result in misclassification. Here, we propose a strategy through an end-to-end pipeline using a semi-supervised, random forests approach to predict cell type annotations. Our method includes cluster-based sampling for training data, cell type prediction, and downstream visualization for interpretability of cell annotation that ultimately improves classification results. We show that our workflow can annotate cells more accurately with a training set <5% of the total number of cells tested. In addition, our pipeline outputs cell type annotation probabilities and model performance metrics for users to decide if it could boost their existing clustering-based workflow results for complex IF data. Availability and implementationFluoro-forest is freely available on github (https://github.com/Josh-Brand/Fluoro-forest). Data used within this manuscript is hosted on Dryad (DOI: 10.5061/dryad.hqbzkh1v1) Supplementary informationSupplemental figures and methods are included in the submission.

bioinformatics↗

bioRxiv2025

UniCure: A Foundation Model for Predicting Personalized Cancer Therapy Response

Predicting drug efficacy across diverse patient contexts remains a major challenge in oncology, as models trained on cancer cell lines often fail to capture patient-specific biology. Emerging biological foundation models and patient-derived technologies offer a promising solution. Here, we present UniCure, the first pre-trained foundation model integrating both omics and chemical foundation models (UCE and Uni-mol) to predict transcriptomic responses to drugs across diverse cellular and tissue contexts, enabling personalized cancer therapy and drug prioritization at the individual level. Rather than encoder/decoder used in traditional models, UniCure utilizes parameter-efficient fine-tuning (PEFT) techniques for optimizing the training process, a novel FlexPert module for modeling flexible drug-cell interactions, and a Maximum Mean Discrepancy (MMD) loss for learning unpaired data. Trained on over 1.8 million perturbation RNA-seq profiles over 22,000 compounds, 166 cell types, and 24 tissues, UniCure achieves high accuracy in predicting both dose-dependent responses and drug combination effects, demonstrating strong generalization across bulk and single-cell transcriptomic data. In particular, fine-tuning on our patient-derived tumor-like clusters and real-world data of 800 profiles enables UniCure to generate individualized therapeutic predictions on patients tissue samples. In addition, UniCure enables patient stratification based on the predicted drug responses, providing a new way for subtyping patients. UniCures drug prioritization was validated across over 1000 patients from pan-cancer cohorts and supported by experiments of candidate therapeutics. By enabling the potential to screen millions of compounds per patient at scale, UniCure represents a biologically grounded tool that could advance personalized precision oncology and accelerate drug discovery.

bioinformatics↗