Biology subjects

Zheng, J.

Publications and source records attributed to Zheng, J..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Development of an Application Ontology for Beta Cell Genomics Based On the Ontology for Biomedical Investigations

The development process for the beta cell genomics application ontology (BCGO) is described. This process should be generally applicable and consists of integration of a subset of reference ontologies. A key element is use of the Ontology for Biomedical Investigation (OBI) as an ontology framework. Another element is enriching ontologies using existing patterns when needed. The ontology is validated in three aspects based on our needs including data annotation, queries and automated classification. The BCGO is available on: http://purl.obolibrary.org/obo/bcgo.owl.

bioinformatics↗

bioRxiv2025

VO: The Vaccine Ontology

With the widespread use of vaccines in research and clinical settings, there is an urgent need to standardize vaccine representation, integrate information across diverse vaccine types, and support computer-assisted reasoning. Accordingly, we have since 2007 developed the community-based Vaccine Ontology (VO), which aligns with the Basic Formal Ontology and adheres to OBO Foundry principles. VO models ontologically vaccines, vaccine components, vaccine immune responses, vaccine investigation studies and other vaccine-related topics. VO represents more than 10,000 vaccines targeting 289 infectious pathogens and cancers in humans and over 30 nonhuman animal species. VO provides mappings to external resources such as RxNorm, CVX, FDA, and USDA. Various VO use cases exist. VO facilitates vaccine standardization in resources such as the VIOLIN vaccine database, ImmPort, and the Vaccine Adjuvant Compendium (VAC). Semantic queries can be made to query VO. VO has been shown to enhance experimental and clinical vaccine data analysis and vaccine literature mining. Overall, VO standardizes vaccine modeling and representation and greatly supports vaccine AI research in the Semantic Web era.

bioinformatics↗

bioRxiv2025

StCEL-based platform enables detection and sequencing of native sialoglycoRNA

Glycosylated RNAs (glycoRNAs) are an emerging class of biomolecules characterized by glycans attached to RNA molecules. Currently, glycoRNA detection remains largely confined to cellular models, substantially impeding its functional characterization and clinical translation. Here, we developed sialyltransferase-mediated chemical enzymatic labeling (StCEL), in which azido-modified sialic acid was transferred to the natural sialic acid on sialoglycoRNA, enabling the detection of native sialoglycoRNA across diverse biological samples with high specificity and sensitivity. Using StCEL, we found that sialoglycoRNA was significantly upregulated in multiple cancer types. StCEL combined with a microplate reader was applied to detect sialoglycoRNA in large-scale samples, revealing that elevated sialoglycoRNA levels correlate positively with tumor malignancy and poor patient prognosis. Furthermore, StCEL-based sialoglycoRNA enrichment and sequencing identified dysregulated sialoglycoRNAs in tumor tissues. Collectively, StCEL establishes a multifunctional platform that enables sensitive and specific detection of sialoglycoRNA, alongside sequencing analysis, demonstrating significant potential for clinical translation.

molecular biology↗

bioRxiv2025

Modulators of epithelial-mesenchymal transitions in prostate cancer: potential for novel therapeutics development

Research in the area of hallmarks of cancer has opened the possibility of designing novel therapeutic interventions based on modulating cancer properties. Interestingly, the epithelial-mesenchymal transition (EMT), important in both tumour growth and metastasis, has not been targeted in prostate cancer (PCa). Previously, in a repositioning screen, three new chemicals that modulate EMT in PCa (named LLSOs) have been found. This study aims to investigate how they work mechanistically as well as the effect of these LLSOs on the properties of PCa cells both in vitro and in vivo. Although LLSOs exhibited varying effects on different cancer cell lines, all of them modulated EMT by inhibiting the migration of PCa cells. LLSO3 also exhibited a significant inhibitory effect on tumour growth in vivo, concomitant with an increase in the expression levels of E-cadherin. Further investigation was conducted to obtain mechanistic insights into the LLSO compounds mode of action. In conclusion, our work strongly supports that LLSO compounds may become important candidates for targeted therapeutics in prostate cancer.

cancer biology↗

bioRxiv2025

A trade-off mechanism underpins the evolution of a young two-gene sex-determining system in plants

Sexual systems in animals and plants are remarkably diverse, with dioecy having evolved independently in numerous lineages. In plants, dioecy often evolved more recently than in the best-studied animal systems, making plants especially important for understanding how separate sexes evolved independently from functionally hermaphrodite ancestors. Despite long-standing theories of developmental trade-offs in sex allocation, the underlying genetic mechanisms remain elusive. Here, we show that the XY sex determination system in the dioecious plant species Eurycorymbus cavaleriei in Sapindaceae involves two Y-linked mutations that act jointly within the developmental male-female trade-off: YUN{Delta}, a truncated allele that lowers the dosage of the D-class MADS-box gene YUN, and SUNMAO, a novel sRNA locus that silences the X-linked SUN allele. In females, SUN stabilizes the HD-ZIP transcription factor KUN, which is a known sex determinant in another dioecious plant, thereby promoting femaleness by increasing YUN expression; loss of SUN expression, together with the effect of YUN{Delta}, shifts development toward males. Two interlocking regulatory loops in this "SKY" module (SUN-KUN-YUN) fine-tunes YUN dosage. This dioecious system in E. cavaleriei likely evolved by sequential mutations in genes acting in the predicted male-female trade-off system, with their close linkage reflecting a translocation, and later recombination-suppressing inversions.

plant biology↗

bioRxiv2025

Immune Biomarkers, Profiles, and Responses: A Vaccine Ontology Perspective

BackgroundVaccines have the ability to induce a range of immune responses under different conditions, for example stimulating the production of neutralizing antibodies to block pathogen entry or activating cytotoxic T-cells to eliminate infected cells. Many such immune responses have not been thoroughly examined and classified. The Vaccine Ontology (VO) is a community-based ontology in the domain of vaccinology. We here describe how VO is used to represent the variety of immune responses associated with vaccines, together with associated biomarkers and profiles. ResultsThe VO differentiates vaccination and vaccine immunization. The former is a process of administering a vaccine in vivo; the latter is the outcome of vaccine induction of immune response. This distinction is critical for understanding both the procedure of vaccination and the resulting immune effects. VO also models and represents various vaccine-induced responses at multiple biological levels, including population, organism, organ/tissue, cell, and gene/protein levels. Such an approach captures the complexity of vaccine-induced immunity, from population-wide trend (for example: herd immunity) to molecular mechanisms. VO defines immune biomarkers as material entities such as neutralizing antibodies that signify humoral immune response, and IFN-gamma that is indicative of cell-mediated responses. Such biomarkers provide measurable indicators of the immune systems functional state post vaccination, enabling robust evaluation of vaccine efficacy. VO classifies immune response profile and correlated profile (or correlate) of immune protection as process profiles, a class in the Basic Formal Ontology (BFO 2.0). Immune response profiles, such as Th1 (or Th2)- biased profile, can be induced by various vaccines and vaccine adjuvants. Different types of correlated profile of immune protection are also identified, such as mechanistic and non-mechanistic correlates of immune protection. Such distinctions help us to quickly identify biomarkers and associated prediction and measurement of different kinds of vaccine protection. ConclusionThe important immune-related terms for immune biomarkers, profiles, and responses are modeled ontologically in VO together with their interrelations. The results support enhanced classification and analysis of vaccine-induced immune responses and related biomarkers and immune profiles, leading to further understanding of the vaccine immune mechanisms and enhanced vaccine research and development.

immunology↗

bioRxiv2025

VaxKG: Integrating The Vaccine Ontology And VIOLIN For Advanced Vaccine Queries And LLM-Powered Chat Systems

Vaccine research faces challenges in integrating diverse biomedical datasets. While the Vaccine Investigation and Online Information Network (VIOLIN) provides comprehensive vaccine data, implemented in traditional relational models limit complex analysis. Similarly, the Vaccine Ontology (VO) offers standardized semantic frameworks but lacks comprehensive empirical data. This study addresses these limitations by developing the Vaccine Knowledge Graph (VaxKG) that integrates VIOLINs dataset with VOs standardized terminology. Using Neo4j, we transformed 12 core VIOLIN tables into a graph structure enriched with VO concepts. The resulting knowledge graph comprises 28,123 VIOLIN data nodes and 101,282 VO resource nodes, connected by 412,865 relationships. Our comparative analysis of Brucella and Influenza vaccines demonstrates VaxKGs ability to enable complex semantic queries and reveal insights unavailable from either resource alone. We further demonstrate VaxKGs utility through VaxChat, a large language model application that leverages the VaxKG as Retrieval-Augmented Generation (RAG) for intuitive vaccine information access.

bioinformatics↗

bioRxiv2025

Mapping and Harmonization of CVX vaccine terms tothe Vaccine Ontology

BackgroundWith many vaccines developed and used, it is critical to standardize vaccine information. The OHDSI OMOP Common Data Model (CDM), widely used to support EHR data integration and analysis, leverages CVX, RxNorm, and RxNorm Extension codes to standardize vaccine-related records. However, these terminologies lack robust semantic relations, making the vaccine classification ineffective in OMOP CDM. To address this issue, our OHDSI Vaccine Vocabulary Working Group proposes to use the Vaccine Ontology (VO) to map these standards and build up its own semantic relations. As a first study of the work, we performed the mapping and alignment of the Vaccine Administered (CVX) codes with the VO using a combination of semi-automatic and manual mapping methods. ResultsA total of 273 CVX terms were first collected and classified. A high-level VO design pattern and an exact one-to-one mapping strategy were developed to guide the CVX-to-VO term mapping. To facilitate the manual mapping and harmonization process, we also developed and evaluated three semi-automated mapping approaches utilizing lexical and semantic information of vaccine concepts to map CVX to VO. These approaches suggested candidate VO mappings for CVX terms and also indicated CVX terms that were unmappable to VO and required new term additions to VO. The application of the best approach to the 2022-10-05 release of VO achieved an accuracy of 85.55% for its suggestions. The suggestions made by the semi-automated approaches were taken into account to further enhance the mappings, which led to our eventual mapping of all CVX terms to the latest version of VO. We innovatively proposed the inclusion of the passive vaccine branch in VO, which includes 24 immunoglobulins and antitoxins from CVX as passive vaccines. A specific CVX-VO OWL file was developed and added to the VO GitHub. Use case queries were developed to demonstrate its support for computer-assisted queries of vaccine groups based on CVX-VO hierarchies. ConclusionAll CVX terms were mapped to the VO using our combined semi-automatic and manual mapping methods. The mapped results enhanced semantic vaccine classification, providing a basis for further OMOP vaccine classification and EHR data analysis.

bioinformatics↗

bioRxiv2025

Self-Reactive B Cells in Artery Tertiary Lymphoid Organs Encode Pathogenic High Affinity Autoantibody in Atherosclerosis

Artery tertiary lymphoid organs (ATLOs) emerge in atherosclerosis which is a chronic inflammatory artery disease with an autoimmune component. However, whether disease-relevant autoimmune B cells emerge in ATLOs and their impacts remains unknown. To map atherosclerosis-specific humoral autoimmunity and define its roles, we isolated germinal-center (GC) B cells from ATLOs and lymph-nodes, expression-cloned 60 autoantibodies and screened them for arterial wall reactivity. ATLO-derived autoantibodies markedly skewed to atherosclerosis-relevant autoantigens versus those of lymph-nodes. One ATLO GC-derived autoantibody bound to histone 2B (H2B) with high-affinity ([~]25 nM). Moreover, vaccination with H2B or adoptive transfer of its cognate autoantibody markedly accelerated atherosclerosis suggesting that ATLOs fail to delete pathogenic high-affinity self-reactive B cells. In a human cohort, total circulating anti H2B antibody titers positively correlated with aortic and coronary artery calcification. We conclude that ATLOs harbor a dysregulated immune tolerance environment permissive for autoreactive B cells that express pathogenic autoantibodies driving atherosclerosis.

immunology↗

bioRxiv2025

PromptBio: A Multi-Agent AI Platform for Bioinformatics Data Analysis

PromptBio is a modular AI platform for scalable, reproducible, and user-adaptable bioinformatics analysis, powered by generative AI and natural language interaction. It supports three complementary modes of analysis designed to meet diverse research needs. PromptGenie is a multi-agent system that enables stepwise, human-in-the-loop workflows using prevalidated domain-standard tools. Within PromptGenie, specialized agents--including DataAgent, OmicsAgent, AnalysisAgent, and QAgent--collaborate to manage tasks such as data ingestion, pipeline execution, statistical analysis, and interactive summarization. DiscoverFlow provides integrated, automated workflows for large-scale multi-omics analysis, offering end-to-end execution and streamlined orchestration. ToolsGenie complements these modes by dynamically generating executable bioinformatics code for custom, user-defined analyses, enabling flexibility beyond standardized workflows. PromptGenie and DiscoverFlow leverage a suite of domain-specific tools, including Omics Tools for standardized omics pipelines, Analysis Tools for downstream statistical interpretation, and MLGenie for machine learning and multi-omics modeling. We present the design, capabilities, and validation of these components, highlight their integration into automated and customizable workflows, and discuss extensibility, monitoring, and compliance. PromptBio aims to democratize high-throughput bioinformatics through a large language model-powered, natural language understanding, workflow generation and agent orchestration.

bioinformatics↗

bioRxiv2025

MFPLI: A Computational Framework for Assessing Biological Authenticity of Protein-Ligand Interactions Using Molecular Fingerprints and Structural Features

Traditional computational drug discovery approaches struggle to accurately evaluate the biological authenticity of protein-ligand binding conformations due to inherent limitations in empirical scoring functions and force field approximations. This study proposes MFPLI - a deep learning framework integrating multimodal physicochemical features to systematically assess the biological authenticity alignment between molecular docking poses and true co-crystal structures. By establishing a continuous surface characterization system for protein-ligand interfaces, we concurrently incorporate geometric curvature features (radius, shape index) and chemical interaction fields (electrostatic potential, hydrogen-bond networks, hydrophobicity gradients). A contrastive learning architecture based on Siamese equivariant graph neural networks was developed to enable discriminative analysis between co-crystal conformations and parameter-perturbed pseudo-conformations generated through inverse docking. The five-channel fusion model demonstrates robust performance on the time-split PoseBuster validation set (AUC=0.91), with predicted Euclidean distance deviation ({Delta}E) effectively distinguishing native co-crystal conformations from aberrant docking poses in 80% of samples. Notably, 71% of {Delta}E-negative samples concentrate within the [-0.3, 0] interval, reflecting physical consistency between model predictions and conformational transition processes. This framework establishes a novel paradigm for biological authenticity assessment in virtual screening for computer-aided drug discovery through synergistic modeling of surface topology and interaction chemistry.

molecular biology↗

bioRxiv2025

RubyACRs Enable Red-Shifted Optogenetic Inhibition in Freely Behaving Drosophila

Optogenetic activators with red-shifted excitation spectra, such as Chrimson, have significantly advanced Drosophila neuroscience. However, until recently, available optogenetic inhibitors required shorter activation wavelengths, which dont penetrate tissue as effectively and are stronger visual stimuli to the animal, potentially confounding behavioral results. Here, we assess the efficacy of two newly identified anion-conducting channelrhodopsins with spectral sensitivities similar to Chrimson: A1ACR and HfACR (RubyACRs). Electrophysiology and functional imaging confirmed that RubyACRs effectively hyperpolarize neurons, with stronger and faster effects than the widely used inhibitor GtACR1. Activation of RubyACRs led to circuit-specific behavioral changes in three different neuronal groups. In glutamatergic motor neurons, activating RubyACRs suppressed adult locomotor activity. In PPL1-{gamma}1pedc dopaminergic neurons, pairing odors with RubyACR activation during learning produced odor responses consistent with synaptic silencing. Finally, activation of RubyACRs in the pIP10 neuron suppressed pulse song during courtship. Together, these results demonstrate that RubyACRs are effective and reliable tools for neuronal inhibition in Drosophila, expanding the optogenetic toolkit for circuit dissection in freely behaving animals.

neuroscience↗

bioRxiv2025

Effects of diffusion MRI spatial resolution on human brain short-range association fiber reconstruction and structural connectivity estimation

Short-range association fibers (SAFs) are critical for cortical communications but are often underestimated in conventional resolution diffusion magnetic resonance imaging (dMRI) since they locate within a [~]1.5mm thin layer of superficial white matter. With the emergence of high-resolution diffusion imaging techniques, this study timely evaluated the effects of image spatial resolution on SAF reconstruction using simulation data and multi-resolution (2, 1.5, and 0.96 mm iso.) empirical data acquired on the same 20 healthy subjects using the gSlider sequence and 20 widely used tractography approaches. Resolution effects were qualitatively assessed through model fitting and tractography results and quantitatively evaluated using global and regional short-range connectivity strength (SCS). It is found that lower resolution systematically reduces SCS across all methods in a spatially varying manner. Moreover, tractography methods exhibit significant differences in resolution sensitivity, with diffusion tensor imaging (DTI) based single-tissue single-fiber tractography showing greater vulnerability than constrained spherical deconvolution (CSD)-based multi-tissue multi-fiber tractography. Probabilistic tracking with anatomical constraints (ACT) and filtering (SIFT) improves robustness. Finally, up-sampling data to nominally higher resolution partially mitigates resolution-induced degradation and improves SAF reconstruction accuracy, particularly for DTI tractography. Based on these findings, higher resolution and multi-shell imaging is recommended if possible. For a given dataset, data up-sampling and DTI-based probabilistic tracking with ACT is recommended for single-shell low b-value data. CSD-based probabilistic tracking with ACT and SIFT is recommended for single-shell higher b-value data and multi-shell data. In summary, this study systematically and quantitatively evaluated resolution effects on SAF reconstruction and structural connectivity estimation and provided practical guidelines for more accurate mapping of SAFs. These advances hold promises to improve the characterization of healthy and diseased human brains in a wide range of neuroscientific and clinical applications.

neuroscience↗

bioRxiv2025

A Cryptic Interfacial Pocket Uncovered in Full CRL4CRBN-IKZF3 Ubiquitylation Complex Enhances IMiD Efficacy

Immunomodulatory imide drugs (IMiDs) redirect the CUL4-RBX1-DDB1-CRBN (CRL4CRBN) ligase to ubiquitylate and degrade disease-linked proteins, but the full picture of how IMiDs glue the neosubstrate within the whole CRL4CRBN complex remains unknown. We determined cryo-electron microscopy structures of eight approved or clinical IMiDs in full CRL4CRBN ubiquitylation complexes at resolutions up to 3.4 [A], and revealed how these structurally distinct IMiDs exploit the structural plasticity of CRL4CRBN to organize conformationally conserved and compact active ubiquitylation assemblies with the neosubstrate IKZF3. Four "next-generation" IMiDs were found to additionally engage a cryptic gluing-driven interfacial (GDI) pocket at the non-degron zinc finger 3 (ZF3) of IKZF3, which contributes to their enhanced efficacy and neosubstrate specificity. The identification of cryptic gluing-driven pockets formed only in full ubiquitylation complexes provides new structure-directed design opportunities for IMiDs to improve therapeutic efficacy.

biochemistry↗

bioRxiv2025

CCN1 forms a complex with GPC4 and heparin to fine-tune signaling activities for embryonic cortical neural stem cell maintenance

Radial glial cells (RGCs) are the neural stem cells (NSCs) in the developing brain, capable of self-renewing and generating neurons and glia in order. The developmental behaviors of NSCs are determined by extrinsic factors from the NSC niche, together with intrinsic factors. Here, we show that Cellular Communication Network 1 (CCN1), a secreted matricellular protein, is an autocrine factor derived from RGCs in the embryonic telencephalon. Loss of Ccn1 in RGCs leads to accelerated lineage progression of RGCs, and causes premature cell cycle exit and neuronal differentiation, resulting in a reduction in NSC pool size, and in turn, at the expense of the perinatal gliogenesis. Mechanistically, CCN1 interacts with an RGC-expressed membrane protein GPC4, one of the heparan sulfate proteoglycans (HSPGs), and is required for GPC4 to maintain NSCs through the Sonic Hedgehog (Shh) signaling pathway, depending on the binding of heparin to CCN1. Our work provides a potential mechanism for the dynamic and sophisticated interconnections of niche components that meet the needs of context-dependent maintenance, differentiation, and fate determination of NSCs.

developmental biology↗

bioRxiv2025

The interplay of membrane tension and FtsZ filament condensation on the initiation and progression of cell division in B. subtilis

The first step of cell division is deforming the planar cell membrane inward towards the cytoplasm. As deforming membranes is energetically costly, biology has developed various protein systems to accomplish this task. The mechanisms providing the force to deform bacterial membranes to initiate division remain unknown. In vivo studies have shown the condensation of FtsZ filaments into a sharp ring is required to initiate cell division, an observation mirrored in vitro with FtsZ filaments encapsulated inside liposomes. Similarly, the force for membrane deformation in many eukaryotic deforming systems arises from the local crowding of proteins on the membrane surface. As any membrane deforming system works against the membrane tension, here we modulated the amount of lipid synthesis and thus membrane tension in Bacillus subtilis to examine: 1) if the condensation of FtsZ filaments by FtsZ bundling proteins serves to overcome the cellular membrane tension to deform the membrane inward and 2) how changes to the membrane tension affect the subsequent invagination of the septum. First, we developed methods to simultaneously measure and modulate membrane tension in live cells. Next, we determined how altering the membrane tension affected the cells ability to initiate division with reduced levels of FtsZ bundling proteins. While cells depleted of 2 FtsZ bundling proteins were unable to divide, reducing membrane tension to a given threshold restored their ability to initiate division. Likewise, cells with intermediate levels of FtsZ bundling proteins required a lesser decrease in membrane tension to initiate division. We also found that reductions in membrane tension increase the rate of Z ring constriction, with the constriction rate scaling linearly with the membrane tension. Interestingly, while the constriction rate in wild-type B. subtilis is limited by FtsZ treadmilling, the rate of constriction becomes independent of FtsZs treadmilling rate when membrane tension is reduced. These experiments give two major insights: First, the filament condensation caused by FtsZ bundling proteins works to overcome membrane tension and deform the membrane inward to initiate division. Second, the rate of septal constriction is limited by membrane tension, suggesting that membrane fluctuations at the tip of the growing septa limit the rate of cell wall synthesis. Finally, our measurements allow the estimation of several physical values of cell division, such as the force required to bend the membrane, but also that the cell membrane provides only 0.1%, a small amount of surface tension relative to the entire cell envelope, indicating 99.9% of the pressure drop occurs across the cell wall. These calculations also indicate that cell division occurs via comparatively very small membrane tension fluctuations relative to the high turgor pressure that exists across the entire cell envelope.

microbiology↗

bioRxiv2025

TMEDs Mediate Versatile Cargo Transport in Vesicle-dependent Unconventional Secretion

Unconventional protein secretion (UcPS) exports diverse signal peptide-lacking cargoes, yet its cargo selectivity remains poorly understood. Here, we identify TMED proteins as key regulators of vesicle-dependent UcPS, mediating selective cargo release via translocation into secretory carriers. TMED proteins act as translocators, facilitating cargo passage across lipid bilayers with assistance from HSP90 chaperones and partial cargo unfolding. Selectivity arises during translocation, where TMED cytoplasmic tails bind specific cargoes. The ER-Golgi intermediate compartment (ERGIC) is essential for TMED-mediated translocation and release. TMED homo-oligomerization enhances translocation, while hetero-tetramerization inhibits it. ERGIC localization promotes homo-oligomerization, which is further stabilized by cargo binding, forming a feed-forward mechanism to enhance translocation. These findings establish TMED proteins as central regulators of cargo diversity in UcPS, with their oligomerization and subcellular localization modulating translocation efficiency.

cell biology↗

bioRxiv2025

PhysDock: A Physics-Guided All-Atom Diffusion Model for Protein-Ligand Complex Prediction

Accurate prediction of protein-ligand complexes remains a central challenge in structural biology. Traditional methods are computationally inefficient and prone to local minima, whereas deep learning approaches struggle to capture structural flexibility and physical plausibility. We introduce PhysDock, a physics-guided diffusion model that uniquely integrates (i) all-atom diffusion to model ligand flexibility and protein precision-flexibility (i.e., subtle conformational adjustments); (ii) physical priors as diffusion conditioning, alongside two-phase physics guidance during the denoising diffusion to ensure physical plausibility. PhysDock demonstrates state-of-the-art performance in redocking benchmarks and excels in the more challenging cross-docking assessments. For practical utility, PhysDock (i) resolves cannabinoid receptor selectivity across diverse molecules, achieving accuracy comparable to experiments; (ii) distinguishes most drug candidates from weak binders in virtual screening of NTRK3 kinase, while uncovering novel candidates with structural insights. PhysDock serves as a versatile tool for protein-ligand complex prediction, with substantial potential to accelerate structure-based drug discovery.

bioinformatics↗