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Hua, C.

Publications and source records attributed to Hua, C..

18 recordsLinked to original sources

Resolving Heterogeneous Mechanical Domains via Physics-Aware Deep Clustering of Single-Molecule Force Spectroscopy Data

Many biological processes rely on mechanical forces, with protein molecules acting as key mediators. Understanding how proteins respond to mechanical stress is essential for conditions including cardiomyopathy and muscular dystrophy. Natural proteins such as dystrophin and utrophin are composed of heterogeneous folding domains with distinct mechanical properties; deciphering domain-level behavior provides insights into disease mechanisms and informs therapeutic strategies. Single-molecule force spectroscopy (SMFS) enables probing the mechanical properties of entire proteins, yet current approaches struggle to identify heterogeneous folding domains, particularly without prior knowledge. Here, we present the first automated framework to identify heterogeneous folding domains in SMFS data, applying both existing clustering methods and a novel physics-aware deep clustering architecture, LatentUnfold. LatentUnfold learns complementary latent representations from force magnitude and the force-extension physical relationship through dual autoencoders, jointly optimized for clustering assignments. We apply our framework to experimental SMFS data collected from a synthetic two-domain protein (ddFLN4-Titin I27) as well as natural protein constructs of dystrophin and utrophin, with Monte Carlo simulated datasets serving as controlled validation. For the synthetic protein, we recover mechanical properties consistent with previously reported values for each domain. For the natural proteins, we uncover two mechanically distinct domain populations - corresponding to the N-terminal domain and spectrin-like repeats - with differences in both unfolding force and contour length increase, and reveal different unfolding order between them for the first time. This work enables domain-level biological inference, overcoming prior limitations that relied on averaging and overlooked heterogeneity, thus advancing the understanding of mechanical behavior in protein unfolding.

biophysics↗

Small Molecule Binding to EDS1/PAD4 in LRR-RP-Mediated Pattern-Triggered Immunity

Pattern-triggered immunity (PTI) is a key defense mechanism initiated when pathogen-associated molecular patterns are recognized by receptors, including leucine-rich repeat receptor proteins (LRR-RPs)1,2. EDS1 and PAD4 are crucial for LRR-RP-mediated PTI activation3. Small molecule (SM) phosphoribosyl-AMP/ADP, generated by TIR-domain NADase activity, promote the recruitment of helper NLRs into EDS1-PAD4 complex4,5. Here, we show that BAK1 recruitment to LRR-RP complexes is unaffected in eds1-2 or pad4-1 mutants, but is abolished in the sobir1-12 mutant, highlighting the essential role of SOBIR1 in surface immune receptor complex formation. Furthermore, SM-binding to EDS1 and PAD4 is critical for LRR-RP-mediated PTI, as SM-binding-deficient mutants fail to restore immune responses. De novo SM synthesis is required for late PTI responses, including PR1 expression and bacterial resistance, but not for early responses such as ROS and ethylene production. The TNL SADR1 functions only in late PTI responses, suggesting distinct roles of multiple TIR-domain proteins in PTI signaling.

plant biology↗

GerNA-Bind: Geometric-enhanced RNA-ligand Binding Specificity Prediction with Deep Learning

RNA molecules are essential regulators of biological processes and promising therapeutic targets for various diseases. Discovering small molecules that selectively bind to specific RNA conformations remains challenging due to RNAs structural complexity and the limited availability of high-resolution data. Herein, we introduce GerNA-Bind, a geometric deep learning framework to predict RNA-ligand binding specificity by integrating multi-state RNA-ligand representations and interactions. GerNA-Bind achieves state-of-the-art performance on multiple benchmark datasets and excels in predicting interactions for low-homology RNA-ligand pairs. It achieves a 20.8% improvement in precision for binding site prediction compared to AlphaFold3. Furthermore, it offers informative, well-calibrated predictions with built-in uncertainty quantification. In a large-scale virtual screening application, GerNA-Bind identified 18 structurally diverse compounds targeting the oncogenic MALAT1 RNA, with experimentally confirmed submicromolar affinities. Among them, one lead compound selectively binds the MALAT1 triple-helix, reduces its transcript levels, and inhibits cancer cell migration. These findings highlight GerNA-Binds potential as a powerful tool for RNA-focused drug discovery, offering both accuracy and biological insight.

bioinformatics↗

The architecture, assembly, and evolution of a complex flagellar motor

Bacterial flagella drive motility in many species, likely including the last bacterial common ancestor 1,2. Knowledge of flagellar assembly and function has mainly come from studies of Escherichia coli and Salmonella enterica, which have simple flagellar motors 3-7. However, most flagellated bacteria possess complex motors with unique, species-specific adaptations whose mechanisms and evolution remain largely unexplored 8-10. Here, we deploy a multidisciplinary approach to build a near-complete model of the flagellar motor in Campylobacter jejuni, revealing its remarkable complexity in architecture and composition. We identify an E-ring around the MS-ring, a periplasmic cage with two distinctive conformations, and an intricate interaction network between the E-ring and cage. These scaffolds play critical roles in stabilizing and regulating 17 torque-generating stator complexes for optimal motility. In-depth evolutionary analyses uncover the ancient origin and prevalence of the E-ring in flagellated species of the domain Bacteria as well as a unique exaptation of type IV pili components PilMNOPQF in the ancestral motor of the phylum Campylobacterota. Collectively, our studies reveal novel mechanisms of assembly and function in complex flagellar motors and shed light on the evolution of flagella and modern bacterial species.

microbiology↗

The receptor-like kinases BIR1 and BIR3 modulates antiviral resistance by different mechanisms

The BAK1-INTERACTING RECEPTOR-LIKE KINASE (BIR) proteins, including BIR1, are negative regulators of cell death and defense in Arabidopsis. Although BIR1 is upregulated during viral infections, its role in virus defense remains unclear. Here, we demonstrate a conserved role for the BIR family as modulators of antiviral resistance in Arabidopsis. During tobacco rattle virus (TRV) infection, BIR gene expression is regulated by antagonistic interactions between salicylic acid (SA) and jasmonic acid (JA) signaling pathways. Genetic evidence shows that BIR1, BIR2, and BIR3 negatively regulate TRV resistance via distinct mechanisms. The antiviral phenotype in bir1-1 mutants is unaffected by mutations in the receptor-like kinases (RLK) BAK1, SOBIR1, or in the lipase-like PAD4. While BIR1 induction partially suppresses PTI marker gene expression, it does not affect early PTI responses such as reactive oxygen species (ROS) production or ethylene accumulation. BIR1 also inhibits plasmodesmata (PD) callose deposition and facilitates virus spread. Our data suggest that BIR1 modulates antiviral defense through mechanisms that include PTI gene expression, PD callose deposition alongside yet unidentified pathways independent of ROS or BAK1, SOBIR1, or PAD4 signaling components. In contrast, BIR3 suppresses an antiviral resistance pathway against TRV that requires BAK1 and the lipases PAD4 and EDS1. Our results suggest that BIR3 controls an NLR-dependent ETI-like response that restricts virus proliferation without triggering a hypersensitive response. These findings highlight the distinct roles of BIR family members in regulating antiviral defense in Arabidopsis.

plant biology↗

EnzymeCAGE: A Geometric Foundation Model for Enzyme Retrieval with Evolutionary Insights

Enzyme catalysis is fundamental to life, driving the chemical transformations that sustain biological processes and support industrial applications. However, unraveling the intertwined relationships between enzymes and their catalytic reactions remains a significant challenge. Here, we present EnzymeCAGE, a catalytic-specific geometric foundation model trained on approximately 1 million structure-informed enzyme-reaction pairs, spanning over 2,000 species and encompassing an extensive diversity of genomic and metabolic information. EnzymeCAGE features a geometry-aware multi-modal architecture coupled with an evolutionary information integration module, enabling it to effectively model the nuanced relationships between enzyme structure, catalytic function, and reaction specificity. EnzymeCAGE supports both experimental and predicted enzyme structures and is applicable across diverse enzyme families, accommodating a broad range of metabolites and reaction types. Extensive evaluations demonstrate EnzymeCAGEs state-of-the-art performance in enzyme function prediction, reaction de-orphaning, catalytic site identification, and biosynthetic pathway reconstruction. These results highlight its potential as a transformative foundation model for understanding enzyme catalysis and accelerating the discovery of novel biocatalysts.

synthetic biology↗

Global transcription factors analyses reveal hierarchy and synergism of regulatory networks and master virulence regulators in Pseudomonas aeruginosa

The transcription factor (TF) regulatory network in Pseudomonas aeruginosa is complex and involves multiple regulators that respond to various environmental signals and physiological cues by regulating gene expression. However, the biological functions of at least half of its 373 putative TFs remain uncharacterised. Herein, chromatin immunoprecipitation sequencing (ChIP-seq) was used to investigate the binding sites of 172 TFs in the P. aeruginosa PAO1 strain. The results revealed 81,009 significant binding peaks in the genome, more than half of which were located in the promoter regions. To further decode the diverse regulatory relationships among TFs, a hierarchical network was assembled into three levels: top, middle, and bottom. Thirteen ternary regulatory motifs revealed flexible relationships among TFs in small hubs, and a comprehensive co-association atlas was established, showing the enrichment of seven core associated clusters. Twenty-four TFs were identified as the master regulators of virulence-related pathways. The pan-genome analysis revealed the conservation and evolution of TFs in P. aeruginosa complex and other species. A Web-based database combining existing and new data from ChIP-seq and the high-throughput systematic evolution of ligands by exponential enrichment was established for searching TF-binding sites. This study provides important insights into the pathogenic mechanisms of P. aeruginosa and related bacteria and is expected to contribute to the development of effective therapies for infectious diseases caused by this pathogen.

microbiology↗

ECloudGen: Access to Broader Chemical Space for Structure-based Molecule Generation

Structure-based molecule generation represents a significant advancement in AI-aided drug design (AIDD). However, progress in this domain is constrained by the scarcity of structural data on protein-ligand complexes, a challenge we term the Paradox of Sparse Chemical Space Generation. To address this limitation, we propose a novel latent variable approach that bridges the data gap between ligand-only and protein-ligand complexes, enabling the target-aware generative models to explore a broader chemical space and enhancing the quality of molecular generation. Drawing inspiration from quantum molecular simulations, we introduce ECloudGen, a generative model that leverages electron clouds as meaningful latent variables--an innovative integration of physical principles into deep learning frameworks. ECloudGen incorporates modern techniques, including latent diffusion models, Llama architectures, and a newly proposed contrastive learning task, which organizes the chemical space into a structured and highly interpretable latent representation. Benchmark studies demonstrate that ECloudGen outperforms state-of-the-art methods by generating more potent binders with superior physiochemical properties and by covering a significantly broader chemical space. The incorporation of electron clouds as latent variables not only improves generative performance but also introduces model-level interpretability, as illustrated in a case study designing V2R inhibitors. Furthermore, ECloudGens structurally ordered modeling of chemical space enables the development of a model-agnostic optimizer, extending its utility to molecular optimization tasks. This capability has been validated through a single-objective oracle benchmark and a complex multi-objective optimization scenario involving the redesign of endogenous BRD4 ligands. In conclusion, ECloudGen effectively addresses the Paradox of Sparse Chemical Space Generation through its integration of theoretical insights, advanced generative techniques, and real-world validation. The newly proposed technique of leveraging physical entities (such as electron clouds) as latent variables within a deep learning framework may prove useful for computational biology fields beyond AIDD.

bioinformatics↗

Two operational modes of atomic force microscopy reveal similar mechanical properties for homologous regions of dystrophin and utrophin

Duchenne muscular dystrophy (DMD) is a lethal muscle disease caused by the absence of the protein dystrophin. Dystrophin is hypothesized to work as a molecular shock absorber that limits myofiber membrane damage when undergoing reversible unfolding upon muscle stretching and contraction. Utrophin is a dystrophin homologue that is under investigation as a protein replacement therapy for DMD. However, it remains uncertain whether utrophin can mechanically substitute for dystrophin. Here, we compared the mechanical properties of homologous utrophin and dystrophin fragments encoding the N terminus through spectrin repeat 3 (UtrN-R3, DysN-R3) using two operational modes of atomic force microscopy (AFM), constant speed and constant force. Our comprehensive data, including the statistics of force magnitude at which the folded domains unfold in constant speed mode and the time of unfolding statistics in constant force mode, show consistent results. We recover parameters of the energy landscape of the domains and conducted Monte Carlo simulations which corroborate the conclusions drawn from experimental data. Our results confirm that UtrN-R3 expressed in bacteria exhibits significantly lower mechanical stiffness compared to insect UtrN-R3, while the mechanical stiffness of the homologous region of dystrophin (DysN-R3) is intermediate between bacterial and insect UtrN-R3, showing greater similarity to bacterial UtrN-R3. SignificanceDuchenne muscular dystrophy (DMD) is a severe muscle wasting disorder caused by mutations in DMD gene encoding dystrophin. Utrophin, a fetal homologue of dystrophin, is under active investigation as a dystrophin replacement therapy for DMD. However, it is still unknown if it can substitute dystrophin mechanically. Here, we report mechanical properties of both utrophin and dystrophin fragments encoding the N terminus through spectrin repeat 3 (UtrN-R3, DysN-R3) using atomic force microscope (AFM) through two operational modes, constant speed and constant force. Our data, consistent across both modes, confirm that UtrN-R3 expressed in bacteria exhibits significantly lower mechanical stiffness than insect UtrN-R3. Additionally, bacterial DysN-R3 lies between bacterial and insect UtrN-R3 in terms of mechanical properties, leaning closer to bacterial UtrN-R3.

biophysics↗

Arabidopsis WALL-ASSOCIATED KINASES are not required for oligogalacturonide-induced signaling and immunity

Carbohydrate-based cell wall signaling impacts plant growth, development, and stress responses; however, how cell wall signals are perceived and transduced remains poorly understood. Several cell wall breakdown products have been described as typical damage-associated molecular patterns (DAMPs) that activate plant immunity, including pectin-derived oligogalacturonides (OGs). Receptor kinases (RKs) of the WALL-ASSOCIATED KINASE (WAK) family have been shown to bind pectin and OGs, and were previously proposed as OG receptors. However, unambiguous genetic evidence for the role of WAKs in OG responses is lacking. Here, we investigated the role of Arabidopsis WAKs in OG perception using a novel deletion mutant of the clustered WAK family. Using a combination of immune assays for early and late pattern-triggered immunity (PTI), we show that WAKs are dispensable for OG-induced signaling and immunity, indicating that they are not bona fide OG receptors.

plant biology↗

DNA Methylome Regulates Virulence and Metabolism in Pseudomonas syringae

Bacterial pathogens employ epigenetic mechanisms, including DNA methylation, to adapt to environmental changes, and these mechanisms play important roles in various biological processes. Pseudomonas syringae is a model phytopathogenic bacterium, but its methylome is less well known than that of other species. In this study, we conducted single-molecule real-time sequencing to profile the DNA methylation landscape in three model pathovars of P. syringae. We identified one Type-I restriction-modification system (HsdMSR), including the conserved sequence motif associated with N6-methyladenine (6mA). About 25%-40% of the genes involved in DNA methylation were conserved in two or more of the strains, revealing the functional conservation of methylation in P. syringae. Subsequent transcriptomic analysis highlighted the involvement of HsdMSR in virulent and metabolic pathways, including the Type III secretion system, biofilm formation, and translational efficiency. The regulatory effect of HsdMSR on transcription was dependent on both strands being fully 6mA methylated. Overall, this work illustrated the methylation profile in P. syringae and the critical involvement of DNA methylation in regulating virulence and metabolism. Thus, this work contributes to a deeper understanding of epigenetic transcriptional control in P. syringae and related bacteria.

microbiology↗

A fungal pathogen manipulates phytocytokine signaling for plant infection

Phytocytokines are hormone-like plant peptides that modulate immune homeostasis and development. Phytosulfokine (PSK) mediates plant growth and attenuates activation of plant pattern-triggered immunity (PTI). We show that the small cysteine-containing effector VdSCP8 from Verticillium dahliae (Vd) is a virulence-promoting protein that suppresses PTI in Arabidopsis thaliana and Nicotiana benthamiana. Apoplastic SCP8 suppresses immune activation through leucine-rich repeat ectodomain pattern recognition receptors. SCP8 virulence and immunosuppressive activities require PHYTOSULFOKINE RECEPTOR 1 (PSKR1), which binds PSK and forms a complex with co-receptor BAK1 for PTI suppression. We find that PSK, like SCP8, suppresses PTI, SCP8 stimulates PSKR1-BAK1 complex formation, and that Vd requires PSK signaling for host infection. SCP8 interacts with an apoplastic subtilase, and co-expression of SCP8 and subtilase inhibitors reduces PTI suppression. Our findings suggest that a multi-host plant pathogen manipulates PTI by enhancing immunosuppressive PSK signaling, likely through plant subtilase activity.

plant biology↗

STOmicsDB: a database of Spatial Transcriptomic data

Recent technological development in spatial transcriptomics allows researchers to measure gene expression of cells and their spatial locations at the almost single-cell level, which generates detailed biological insight into biological processes. However, specialized spatial transcriptomics databases are rare. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a user-friendly database with multifunctions including search of relevant publications and tools, public dataset visualization, customized specialized databases, new data archive, and online analysis. The current version of STOmicsDB consists of 141 curated spatial transcript datasets covering 12 species, and includes 5,618 spatial multi-omics publications and 674 tools. STOmicsDB is freely accessible at https://db.cngb.org/stomics/.

genomics↗

CDCP: a visualization and analyzing platform for single-cell datasets

Advances in single-cell sequencing technology provide a unique approach to characterize the heterogeneity and distinctive functional states at single-cell resolution, leading to rapid accumulation of large-scale single-cell datasets. A big challenge undertaken by research community especially bench scientists is how to simplify the way of retrieving, processing and analyzing the huge number of datasets. Towards this end, we developed Cell-omics Data Coordinate Platform (CDCP), a platform that aims to share and integrate comprehensive single-cell datasets, and to provide a network analysis toolkit for personalized analysis. CDCP contains single-cell RNA-seq and ATAC-seq datasets of 474 572 cells from 6 459 samples in species covering humans, non-human primate models and other animals. It allows querying and visualization of interested datasets and the expression profile of distinct genes in different cell clusters and cell types. Besides, this platform provides an analysis pipeline for non-bioinformatician experimental scientists to address questions not focused by the submitters of the datasets. In summary, CDCP provides a user-friendly interface for researchers to explore, visualize, analyze, download and submit published single-cell datasets and it will be a valuable resource for investigators to explore the global transcriptome profiling at single-cell level.

bioinformatics↗

Convergent evolution of distinct immune sensor systems for fungal polygalacturonases in Brassicaceae

Plant pattern recognition receptors (PRRs) facilitate recognition of microbial surface patterns and mediate activation of plant immunity. Arabidopsis thaliana RLP42, a leucine-rich repeat (LRR) receptor protein (LRR-RP), senses fungal endopolygalacturonases (PGs) through a ternary complex comprising RLP42, the adapter kinase SOBIR1, and SERK proteins. Several fungal PGs harbor a conserved 9-amino acid fragment pg9(At), which is sufficient to activate RLP42-dependent plant immunity. Domain swap experiments using RLP42 and paralogous RLP40 sequences revealed a dominant role of the island domain (ID) for ligand binding and PRR complex assembly. Involvement of the ID in plant receptor function is reminiscent of plant phytosulfokine (PSK) perception through the receptor, PSKR, a LRR receptor kinase. Sensitivity to pg9(At), which is restricted to A. thaliana, exhibits notable accession specificity as active RLP42 alleles were found in only 16 of 52 accessions tested. Arabidopsis arenosa and Brassica rapa, two Brassicaceae species closely related to A. thaliana, perceive plant immunogenic PG fragments pg20(Aa) or pg36(Bra), which are distinct from pg9(At). Our study unveils unprecedented complexity and dynamics of PG pattern recognition receptor evolution within a single plant family. PG perception systems may have evolved rather independently as a result of convergent evolution even among closely related species.

plant biology↗

VirusDIP: Virus Data Integration Platform

MotivationThe Coronavirus Disease 2019 (COVID-19) pandemic poses a huge threat to human public health. Viral sequence data plays an important role in the scientific prevention and control of epidemics. A comprehensive virus database will be vital useful for virus data retrieval and deep analysis. To promote sharing of virus data, several virus databases and related analyzing tools have been created. ResultsTo facilitate virus research and promote the global sharing of virus data, we present here VirusDIP, a one-stop service platform for archive, integration, access, analysis of virus data. It accepts the submission of viral sequence data from all over the world and currently integrates data resources from the National GeneBank Database (CNGBdb), Global initiative on sharing all influenza data (GISAID), and National Center for Biotechnology Information (NCBI). Moreover, based on the comprehensive data resources, BLAST sequence alignment tool and multi-party security computing tools are deployed for multi-sequence alignment, phylogenetic tree building and global trusted sharing. VirusDIP is gradually establishing cooperation with more databases, and paving the way for the analysis of virus origin and evolution. All public data in VirusDIP are freely available for all researchers worldwide. Availabilityhttps://db.cngb.org/virus/ Contactweixiaofeng@cngb.org

bioinformatics↗

CNSA: a data repository for archiving omics data

With the application and development of high-throughput sequencing technology in life and health sciences, massive multi-dimensional biological data brings the problem of efficient management and utilization. Database development and biocuration are the prerequisites for the reuse of these big data. Here, relying on China National GeneBank (CNGB), we present CNGB Sequence Archive (CNSA) for archiving omics data, including raw sequencing data and its analytical data and related metadata which are organized into six objects, namely Project, Sample, Experiment, Run, Assembly, and Variation at present. Moreover, CNSA has created the correlation model of living samples, sample information, and analytical data on some projects, so that all data can be traced throughout the life cycle from the living sample to the sample information to the analytical data. Complying with the data standards commonly used in the life sciences, CNSA is committed to building a comprehensive and curated data repository for the storage, management and sharing of omics data, improving the data standards, and providing free access to open data resources for worldwide scientific communities to support academic research and the bio-industry. Database URL: https://db.cngb.org/cnsa/

genomics↗

Large-scale Quantification of Vertebrate Biodiversity in Ailaoshan Nature Reserve from Leech iDNA

1Protected areas are central to meeting biodiversity conservation goals, but measuring their effectiveness is challenging. We address this challenge by using DNA from leech-ingested bloodmeals to estimate vertebrate occupancies across the 677 km2 Ailaoshan reserve in Yunnan, China. 163 park rangers collected 30,468 leeches from 172 patrol areas. We identified 86 vertebrate species, including amphibians, mammals, birds, and squamates. Multi-species occupancy modelling showed that species richness increased with elevation and distance to reserve edge, including the distributions of most of the large mammals (e.g. sambar, black bear, serow, tufted deer). The exceptions were the three domestic mammal species (cows, sheep, goats) and muntjak deer, which were more common at lower elevations. eDNA-estimated vertebrate occupancies are Granular, Repeatable, Auditable, Direct, Efficient, and Simple-to-understand measures that can be used to assess conservation effectiveness and thus to improve the contributions that protected areas make to achieving global biodiversity goals.

ecology↗