Biology subjects

Tang, J.

Publications and source records attributed to Tang, J..

At least 55 records · Page 3Linked to original sources

bioRxiv2025

Temporal single-cell profiling uncovers age-associated delays in immune resolution to respiratory viral infection

Aging is a major risk factor for increased morbidity and mortality following acute respiratory virus infections. To elucidate the immune determinants underlying viral pathogenesis and delayed lung repair in the aged lung, a comprehensive time-course study was conducted. Single-cell RNA sequencing (scRNAseq) and high-dimensional flow cytometry were utilized to compare lungs from young and aged mice infected with influenza A virus (IAV). Aged hosts displayed diminished alveolar macrophage (AM) and dendritic cell (DC) but elevated monocyte-derived macrophage (MoM) and interstitial macrophage (IM) presence following infection. Additionally, enhanced accumulation of adaptive immune cells, including CD4+ tissue-resident helper (TRH) cells, CD8+ tissue-resident memory (TRM) cells, and a B cell subset resembling age-associated B cells, was observed in the memory phase. Pathway analysis revealed that elevated type I and II interferon (IFN/{gamma}) signaling, especially in MoM/IM subsets, distinguished the aged hosts from the young. Inhibition of IFN/{gamma} signaling after viral clearance improved long-term respiratory outcomes and reduced both IM and TRH populations in aged mice. These findings highlight the pivotal role of IFN/{gamma} signaling, likely within MoM/IM subsets, in driving the exuberant persistence of adaptive immune cells and chronic immunopathology in the aged lung following acute viral infection.

immunology↗

bioRxiv2025

Visual Semantic Encoding and Identification of Naturalistic Movies via High-Density Diffuse Optical Tomography

Understanding how the brain represents meaning in real-world contexts is essential for both fundamental neuroscience and clinical applications. Brain encoding and decoding models from naturalistic stimuli provide a powerful window into semantic representations. Yet, existing approaches rely on a constrained scanning environment, or on conventional fNIRS, which has been limited to sparse sampling and/or block-design paradigms. Here, we tested whether high-density diffuse optical tomography (HD-DOT), an advanced high-density tomographic optical imaging method, can support semantic encoding and decoding using naturalistic movies. We collected 3.5 hours of naturalistic movie viewing data from six participants using stimuli labeled with 1,708 categories. Encoding models robustly predicted voxel-level responses, yielding single semantic category maps consistent with prior fMRI studies. In complementary decoding analyses, we showed that DOT responses captured sufficient semantic content to identify which clips participants viewed. To assess organization across individuals, we identified a shared low-dimensional semantic space that captures common semantic dimensions. Finally, clustering analyses revealed interpretable higher-order semantic dimensions like social and animate agents, objects vs natural organisms, and textural scenes, consistently mapped across the cortex. These findings demonstrate that DOT can recover distributed, high-dimensional semantic representations from naturalistic movies, bridging fMRI-level semantic mapping with the accessibility of optical imaging.

neuroscience↗

bioRxiv2025

TissueNarrator: Generative Modeling of Spatial Transcriptomics with Large Language Models

The intricate spatial organization and molecular communication among cells are fundamental to multicellular systems. Spatial transcriptomics (ST) enables gene expression profiling while preserving spatial context, providing rich data for studying cellular interactions and tissue dynamics. However, most existing computational approaches focus on embedding-based tasks and provide limited generative capacity for simulating cell behavior in situ. Moreover, accurately interpreting spatial interactions requires extensive biological knowledge, which current models do not incorporate. Here, we introduce TO_SCPLOWISSUEC_SCPLOWNO_SCPLOWARRATORC_SCPLOW, a framework that reformulates spatial omics analysis as a language modeling problem. By representing tissue sections as spatial sentences - rank-based gene lists augmented with spatial coordinates and metadata - TO_SCPLOWISSUEC_SCPLOWNO_SCPLOWARRATORC_SCPLOW leverages pretrained large language models (LLMs) to learn spatially conditioned gene expression patterns. The model generates realistic, context-aware cellular profiles, predicts intercellular interactions, and performs in silico perturbation analyses. Across multiple ST technologies (MERFISH, Perturb-FISH, and CosMx SMI), TO_SCPLOWISSUEC_SCPLOWNO_SCPLOWARRATORC_SCPLOW achieves superior quantitative performance and recovers biologically meaning-ful ligand-receptor and signaling pathways. Furthermore, a conversational inference mode enables natural-language querying of tissue organization. By integrating pretrained biological knowledge with spatial context, TO_SCPLOWISSUEC_SCPLOWNO_SCPLOWARRATORC_SCPLOW establishes a new, scalable generative paradigm for modeling, simulating, and reasoning about tissue systems.

bioinformatics↗

bioRxiv2025

From Tasks to Topology: Dorsal and Ventral Streams Emerge in Optimized Neural Networks

The primate visual system is organized into dorsal and ventral pathways, classically linked to visuomotor control and perception. A long-standing question is whether this division reflects intrinsic architectural priors or emerges from task demands. We trained a single convolutional network to perform classification and grasp prediction of 3D objects, without imposing modular structure. Dual-stream topology - functionally distinct visuomotor and perceptual pathways - emerged spontaneously with rich cross-communication. Shapley value analyses revealed that action- and perception-selective features developed progressively across depth, reflecting task-driven hierarchical specialization. Time-resolved EEG showed that model activity mapped onto dissociable temporal components in human cortex: ventral-aligned signals emerged early and late, where dorsal- and ventral-aligned responses coincided in the intervening interval. These results demonstrate that task optimization alone can explain core features of dorsal-ventral organization, and that distinct temporal roles for perception and action arise naturally atop a shared feedforward scaffold, without requiring architectural hard-coding or recurrence.

neuroscience↗

bioRxiv2025

Xanthomonas type III effector XopN targets scaffold protein OsRACK1B to suppress rice immunity

Bacterial leaf streak (BLS), caused by Xanthomonas oryzae pv. oryzicola (Xoc), is a major bacterial disease that poses a substantial threat to rice production. Although Xoc deploys a variety of effectors to subvert host defenses, the mechanistic basis of how these effectors modulate plant immune signaling remains largely unclear. Here, we show that XopN suppresses rice immune responses and physically interacts with the rice scaffold protein OsRACK1B. Notably, OsRACK1B expression is induced following Xoc infection in a type III secretion system (T3SS)-independent manner. Functional analyses indicate that OsRACK1B overexpression enhances Xoc resistance, whereas loss-of-function mutants exhibit heightened susceptibility. This increased susceptibility is characterized by compromised immunity, including reduced callose deposition, diminished reactive oxygen species (ROS) accumulation, and attenuated expression of defense-related genes such as OsPBZ1 and OsPAL1. Furthermore, we demonstrate that XopN competes with OsRap2.6 for binding to OsRACK1B, thereby disrupting the formation of the OsRACK1B-OsRap2.6 immune complex. These findings reveal a novel virulence strategy in Xoc and identify promising gene targets for developing disease-resistant rice varieties.

plant biology↗

bioRxiv2025

Structure and function of the nairovirus cap-snatching endonuclease

Nairoviruses include several human pathogens such as Crimean-Congo hemorrhagic fever virus (CCHFV) and Kasokero virus (KASV). The cap-snatching endonuclease (EN) domain of the viral polymerase is essential for transcription and represents a promising antiviral target. However, the structural and functional mechanisms of nairovirus ENs remain poorly understood. Here, we describe biochemical and structural studies of the ENs from CCHFV and KASV. Biochemical assays demonstrate that the RNA endonuclease activity of both ENs is activated by manganese ions and exhibits a preference for uridine-rich RNA substrates. This activity is inhibited by three metal-chelating inhibitors (DPBA, L-742,001, and BXA), with BXA displaying the highest binding affinity and inhibitory potency. We further determine nine crystal structures of CCHFV and KASV ENs in apo, metal ion-bound, and inhibitor-bound states. Comparative structural analysis uncovers a two-metal-ion binding mode unique to nairovirus ENs, in which conserved residues coordinate two manganese ions via bridging water molecules. In the inhibitor-bound structures of KASV EN, BXA forms additional stabilizing interactions with the enzyme, explaining its superior inhibitory effect. Functional assays further confirm that the two-metal-ion mechanism is critical for viral transcription. These findings provide a structural foundation for the rational design of antivirals against CCHFV and related pathogens.

microbiology↗

bioRxiv2025

Rapid De Novo Antibody Design with GeoFlow-V3

Recent years have witnessed striking advances in miniprotein design, yet de novo antibody discovery remains challenging, marked by low binding rates and the need for extensive, labor-intensive experimental screening of millions of candidates. This technical report introduces GeoFlow-V3, a unified atomic generative model for structure prediction and protein design. GeoFlow-V3 delivers improved accuracy on antibody-antigen complex structure prediction relative to our previous version, and its performance is further enhanced when experimental constraints or prior knowledge are provided, enabling precise control over both folding and design. The model also demonstrates reliable ability to discriminate binders from non-binders based on its confidence scores. Leveraging this capability, we build a GeoFlow-V3 in silico pipeline to design no more than 50 nanobodies per therapeutically relevant target de novo, completing a single round of wet-lab characterization in under three weeks. GeoFlow-V3 identifies at least one binder for 8 tested epitopes and achieves an average hit rate of 15.5%, representing a two-orders-of-magnitude improvement over prior computational pipelines. These results position GeoFlow-V3 as an appealing platform for rapid, AI-driven therapeutic antibody discovery, significantly reducing experimental screening demands and offering a powerful avenue to tackle previously undruggable targets. A demo of GeoFlow-V3 can be accessed via prot.design for non-commercial use.

bioinformatics↗

bioRxiv2025

A newly identified Duck orbivirus as the etiological agent of egg-production decline in Chinese breeder ducks

The genus Orbivirus comprises double-stranded RNA viruses, many of which are transmitted by arthropods and cause febrile, hemorrhagic, or reproductive diseases in animals. In recent years, unexplained outbreaks characterized by marked egg-production decline have occurred in breeder duck flocks in China. To identify the causative agent, samples were collected from affected farms in multiple regions across China between 2022 and 2025. Representative samples were analyzed by high-throughput sequencing, followed by phylogenetic analysis, transmission electron microscopy, host cell infection assays, and animal challenge experiments. A previously unreported Orbivirus, designated Duck orbivirus (DORV), was identified. Epidemiological investigation indicated that outbreaks mainly affected breeder ducks during the peak laying period (21-55 weeks of age), with higher incidence in summer and autumn. The DORV genome contains ten double-stranded RNA segments encoding seven structural and five nonstructural proteins. Phylogenetic analysis showed that DORV is closely related to Parrys Lagoon virus (PLV) and Corriparta virus (CORV). Electron microscopy revealed icosahedral particles 30-40 nm in diameter. DORV replicated efficiently in DEF, DEL and C6/36 cells but not in MDCK and LMH cells, suggesting a restricted host range with potential arthropod-borne features. Infection of breeder ducks with DORV-SD01 reduced egg production by 30-40% and caused lesions in the ovary, liver, and spleen. This study describes a novel duck-origin Orbivirus linked to egg-production decline, offering new etiological insights and extending the host spectrum of Orbivirus. The results provide a foundation for future surveillance and prevention of emerging waterfowl diseases.

microbiology↗

bioRxiv2025

scLinguist: A pre-trained hyena-based foundation model for cross-modality translation in single-cell multi-omics

Single-cell multi-omics provides complementary insights into cellular states, heterogeneity, and regulatory programmes. However, paired assays remain costly, low-throughput, and technically challenging, whereas large-scale single-modality data such as scRNA-seq are abundant but do not capture protein-level biology. Here we present scLinguist, a foundation model for cross-modality translation introduces a three-stage framework: self-supervised pretraining on large-scale unimodal datasets to learn modality-specific representations, post-pretraining on limited paired data to capture cross-modality relationships, and inference to predict missing modalities (e.g., protein from RNA) in fine-tuning or zero-shot settings. Systematic benchmarking shows that scLinguist consistently outperforms state-of-the-art methods in predicting protein abundance from RNA across diverse biological contexts. It achieves high predictive performance while preserving cellular heterogeneity and further enables mechanistic and generalizable inference under simulated gene perturbations. Furthermore, scLinguist exhibits strong transferability across health states and datasets. By leveraging abundant unimodal data and minimizing dependence on paired assays, scLinguist establishes a scalable and versatile framework for cross-modality translation in single-cell analysis.

cell biology↗

bioRxiv2025

Mechanisms of superior respiratory IgA responses against SARS-CoV-2 after mucosal vaccination

Mucosal immunization and respiratory IgA offer significant promise in protecting against airborne pathogens, including SARS-CoV-2. However, the conditions and mechanisms that lead to the robust induction of respiratory IgA responses following mucosal vaccination remain poorly understood. It is also currently debatable whether mucosal vaccination is still warranted given that most individuals in developed countries have established a hybrid immunity from vaccination and infection. Here we characterized respiratory mucosal immune responses after SARS-CoV-2 infection, vaccination or both in humans. We found that hybrid immunity resulted in moderately increased respiratory IgA and neutralizing antibody responses compared to infection or vaccination alone. However, a direct comparison of hybrid immunity and a mucosal adenovirus-based booster vaccination in animal models revealed that respiratory booster immunization elicited markedly stronger and more durable respiratory IgA, T cell response, and protective immunity against SARS-CoV-2, supporting the promise of respiratory mucosal vaccination. Mechanistically, we found that mucosal booster immunization induced local IgA- secreting cells in the respiratory mucosa, aided by pulmonary CD4+ T cells in situ. Strikingly, local IL-21-producing Blimp-1+ Th1 effector cells were critical in mediating the CD4+ T cell help for respiratory IgA production. Furthermore, lung macrophages were important for this respiratory IgA response via the production of TGF-{beta}. Consequently, we demonstrated delivery of adenoviral booster to the lower airway was necessary to generate robust upper and lower airway IgA responses. Collectively, our results uncover a local cellular network supporting enhanced respiratory IgA responses, with implications for the development of optimal mucosal immunization strategies against SARS-CoV-2 and other respiratory pathogens.

immunology↗

bioRxiv2025

Proteome profiling reveals HES1-driven mitotic catastrophe in ovarian serous carcinoma

Ovarian high-grade serous cancer (HGSC) is an aggressive subtype of epithelial ovarian cancer. Here, we identify BX-912, a phosphoinositide-dependent kinase 1 (PDPK1) inhibitor, as a promising therapeutic agent for HGSC. BX-912 suppressed HGSC growth as a single agent and synergized with olaparib independently of BRCA status. Unexpectedly, BX-912 treatment induced multinucleation, a phenotype not observed with other PDPK1 inhibitors. Proteome Integral Solubility Alteration (PISA) profiling revealed the transcription factor HES1 as a functional target of BX-912. Structural modeling showed that BX-912 binds the Orange domain of HES1, while its WRPW motif mediates interactions with protein partners, including the AP2 endocytic protein complex, coordinating their nuclear accumulation that leads to a mitotic catastrophe. Furthermore, cell cycle analyses showed that BX-912 combined with olaparib synergistically enhanced DNA damage and G2-M arrest. Our study demonstrates the value of proteomics for revealing hidden drug activities. It also identifies potential inhibition strategies for HES1, which is commonly overexpressed in HGSC. Additionally, this study proposes a novel strategy of targeting consecutive cell cycle phases to enhance treatment efficacy in HGSC.

cancer biology↗

bioRxiv2025

HerbComb: an integrated database for the discovery of novel combinational therapies from herbal medicines

Drug combinations have gained increasing interest due to their potential for optimal efficacy and the ability to overcome drug resistance. Herbal medicine is a complex system with multiple ingredients and has illustrated apparent therapeutic effects through thousands of years of clinical experience, offering a rich resource for combinational therapies. However, the underlying mechanism of their synergistic therapeutic effects for disease treatment remains obscure for most herbal medicines. To fill this gap, we developed an integrated database named HerbComb (https://herbcomb.com/) to discover combinatorial therapies in herbal medicine, featuring three unique functions: drug prepositioning, customized combinatorial analysis, and biological and pharmacological features. Additionally, transcriptional gene signatures and ADMET properties were also interrogated as interactive functionalities to facilitate combinational analysis. Using Tongxinluo for Stroke as a case study, we identified and validated Oleanolic acid and Ferulic acid as synergistic ingredients. In summary, HerbComb is a versatile data exploration platform designed to characterize synergistic interactions among herbal medicines, enhancing the understanding of synergistic mechanisms and facilitating the discovery of effective drug combinations.

pharmacology and toxicology↗

bioRxiv2025

DANCE 2.0: Transforming single-cell analysis from black box to transparent workflow

Preprocessing is a critical step in single-cell data analysis, yet current practices remain largely a black-box, trial-and-error process driven by user intuition, legacy defaults, and ad hoc heuristics. The optimal combination of steps such as normalization, gene selection, and dimensionality reduction varies across tasks, model architectures, and dataset characteristics, hindering reproducibility and method development. We present DANCE 2.0, an automated and interpretable preprocessing platform featuring two key modules: the Method-Aware Preprocessing (MAP) module, which discovers optimal pipelines for task-specific methods via hierarchical search, and the Dataset-Aware Preprocessing (DAP) module, which recommends pipelines for new datasets via similarity-based matching to a reference atlas. Together, MAP and DAP execute over 325,000 pipeline searches across six major tasks - clustering, cell type annotation, imputation, joint embedding, spatial domain identification, and cell type deconvolution - yielding robust and generalizable recommendations. MAP-recommended pipelines consistently outperform original method defaults, with substantial gains across all tasks. Beyond automation, DANCE 2.0 reveals interpretable preprocessing patterns across tasks, methods, and datasets, transforming preprocessing into a transparent, data-driven process. All resources are openly available at https://github.com/OmicsML/dance to support broad community adoption and future methodological advances.

bioinformatics↗

bioRxiv2025

Genome-Wide Investigation of Transcription Factor Occupancy and Dynamics Using cFOOT-seq

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences. Notably, cFOOT-seq, combined with FootTrack analysis, predicts TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.

molecular biology↗

bioRxiv2025

Decoding functional genes across species with annotation-independent machine learning

Dissecting the genetic basis of complex traits across species is a challenge for traditional low-throughput, species-specific methods. To overcome these limitations, we introduce a computational framework that integrates cross-species comparative genomics and machine learning to identify functional genes for shared phenotypes, independent of prior annotation. Our method links orthologous gene group (OG) profiles to specific traits, enabling the powerful discovery of functional genes across diverse evolutionary clades. For plant-arbuscular mycorrhizal symbiosis, a notoriously difficult system, our model pinpointed a regulatory network by identifying 27 core plant-AM symbiosis genes among its top 50 candidate OGs, including the critical receptor SYMRK. The approach also proved highly effective for identifying functional genes related to the tested vertebrate skeletal development and multiple bacterial traits. Most notably, for bacterial motility, our model not only identified 63 known motility genes from the top 100 candidate OGs (of which 78 are present in Escherichia coli) but also guided the experimental validation of three novel essential genes. This annotation-independent strategy represents a paradigm shift in functional genomics, offering a scalable and universal engine to decode the genetic architecture of complex traits and illuminate the vast functional dark matter across the tree of life.

genomics↗

bioRxiv2025

Modular Inter-brain Synchrony Network Associated with Social Difficulty in Autism Spectrum Disorder: a Graph Neural Network-Driven Hyperscanning Study

Understanding social difficulties in Autism Spectrum Disorder (ASD) remains challenging due to its neurobiological heterogeneity and the limited ecological validity of conventional neuroimaging methods in capturing dynamic social interactions. Hyperscanning analysis based on functional near-infrared spectroscopy (fNIRS), which measures inter-brain synchrony (IBS) during dyadic interaction, offers a novel avenue to address these challenges. However, prior studies on ASD have reported inconsistent findings, primarily focusing on intra-regional synchronization while overlooking cross-regional network dynamics. To bridge this gap, we proposed an interpretable graph neural network (GNN) model to systematically identify ASD-specific IBS modular network between child-caregiver dyads during naturalistic cooperative puzzle-solving and free-talking tasks. We identified distinctive key IBS sub-networks for the cooperative puzzle-solving task and free-talking task, with the frontal eye field (FEF) of caregivers, the dorsal lateral prefrontal cortex (DLPFC) and the motor region of children highlighted. Furthermore, the key IBS sub-networks were found to be able to predict multiple domains of the core ASD symptoms. By integrating hyperscanning with GNN-driven analysis, this work uncovers task-dependent inter-brain neural mechanisms underlying social difficulties in ASD. These findings advance the field by proposing a data-driven framework to identify IBS biomarkers tied to clinical profiles, paving the way for personalized interventions that integrate computational neuroscience with clinical practice.

neuroscience↗

bioRxiv2025

Multi-omics profiling reveals atypical sugar utilization and identifies a key membrane composition regulator in Streptococcus pneumoniae

The human body comprises many different microenvironments, each with their own challenges for microorganisms to overcome in order to survive, and possibly cause infection. The human pathogen Streptococcus pneumoniae is notoriously flexible in this regard, and can adapt to a wide range of host niches, including the nasopharynx, lungs, and cerebrospinal fluid. However, the molecular and genetic underpinnings of this ability remain largely obscure. In this work, using infection-mimicking growth conditions we demonstrate that niche adaptation imposes genome-wide changes on multiple levels, including gene essentiality, expression and membrane lipid composition. In general, we show that gene expression and fitness profiling couple orthogonal sets of genes to environmental stimuli. For instance, N-acetylglucosamine (GlcNAc) import (manLMN) and catabolism (nagAB) genes were required for growth on this sugar, but not differentially expressed in its presence, whereas other amino sugar metabolism pathways were upregulated, but not essential. Surprisingly, we found that pneumococci do not necessarily prefer glucose over GlcNAc and that uptake of GlcNAc in absence of subsequent catabolism was toxic. Moreover, we identified a previously overlooked fatty acid saturation regulator, FasR, controlling membrane composition, rendering it important during heat stress. A fundamental understanding of how genes contribute to bacterial niche adaptation, including nutrient availability or temperature fluctuations, is crucial for understanding successful antibiotic therapy and vaccination strategies and the development of novel anti-infectives.

microbiology↗

bioRxiv2025

The abscisic acid could reverse the cuproptosis in sepsis-associated acute kidney injury through the inhibition of ferredoxin-1

Sepsis-associated acute kidney injury (S-AKI) is a grave clinical condition with a poor prognosis. Prior studies have observed a potential correlation between copper (Cu) and S-AKI, though the underlying mechanisms remain to be fully elucidated. The present study aims to investigate the underlying mechanism between Cu and renal dysfunction under sepsis conditions. Utilizing a cecal ligation and puncture (CLP) murine model, we observed a substantial upregulation of inflammatory factors and deteriorated renal function (p<0.05), concomitant with an augmented mortality rate (p<0..05). Concurrently, the renal tissue exhibited a series of pathological manifestations, including fibrosis and cell death. Concurrently, the expression of the cuproptosis-regulating protein FDX-1 (p<0.05) was found to be elevated in CLP mice. Collectively, these data indicated the activation of cuproptosis in CLP-induced S-AKI. Intriguingly, the application of abscisic acid (ABA), a FDX-1 inhibitor, led to a substantial alleviation of mortality rate and inflammatory factor level (p<0.05). Furthermore, a significant alleviation was observed in renal pathology and biofunction (p<0.05). Collectively, these observations suggest that ABA exerts a protective effect on renal function during cuproptosis promoted S-AKI. Consequently, it can be concluded that ABA has the potential to reverse cuproptosis in S-AKI by inhibiting FDX-1.

molecular biology↗