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PubMed2026

Moderate expression and activity of flocculins underlie the characteristic flocculation phenotype of Saccharomyces pastorianus.

Flocculation is a key technological trait in lager brewing, governing fermentation performance, yeast recovery, and beer quality. In the allo-aneuploid hybrid yeast Saccharomyces pastorianus, the genetic basis of flocculation remains poorly resolved due to its complex dual sub-genome architecture. Here, we systematically re-annotated and functionally characterized the complete FLO gene repertoire of the Group II strain CBS 1483. Thirteen FLO genes were identified, including allelic variants and a previously uncharacterized adhesin, Flo12, containing a Hyphal_reg_CWP domain instead of the canonical PA14 lectin-binding domain. Structural modeling revealed strong conservation of Ca²+-binding residues in PA14 domains, alongside repeat-region diversification likely contributing to functional variability. Using optogenetic expression in a FLO-null background, we demonstrated that SpcI-FLO9-1 and SpcI-FLO9-2_1 are the strongest drivers of flocculation, exhibiting NewFlo-like sugar sensitivity. Transcriptomic analysis during 17°P wort fermentation showed dynamic induction of these genes coinciding with flocculation onset. Surprisingly, deletion of both loci in CBS 1483 did not abolish but only delayed sedimentation in wort, accompanied by improved maltose utilization and attenuation. These findings reveal functional redundancy and compensatory mechanisms within the FLO network of lager yeast, highlighting the genetic complexity underlying flocculation, and providing a molecular framework to inform yeast selection, strain development, and optimization of the lager fermentation processes.IMPORTANCEFlocculation, the process by which yeast cells aggregate and settle, is essential for producing clear, high-quality lager beer, and for efficient yeast recovery during brewing. However, the genetic basis of this trait in lager yeast has remained poorly understood because these strains possess unusually complex hybrid genomes. In this study, we systematically identified and characterized the complete set of flocculation genes in the industrial lager yeast Saccharomyces pastorianus CBS 1483. We demonstrated that lager yeast flocculation is not controlled by a single dominant gene, but instead emerges from the combined action of several moderately active adhesion proteins that are expressed at low levels during fermentation. Surprisingly, deleting the two strongest candidate genes only delayed, rather than eliminated, sedimentation, revealing a robust compensatory network that preserves brewing performance. These findings refine the current understanding of yeast flocculation and provide a molecular framework for developing brewing strains with improved fermentation efficiency, product consistency, and flavor quality.

Saccharomyces pastorianus↗

PubMed2026

Whole-Genome Deep Learning Predicts Chemotherapy Response in Colorectal Cancer.

Chemotherapy response in colorectal cancer (CRC) exhibits significant heterogeneity, with current clinical predictors failing to capture complex genomic determinants of resistance. We developed a hybrid deep learning framework integrating convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) networks to analyze whole-genome somatic mutations, evolutionary conservation, chromatin accessibility, and 3D genome architecture in 2,546 TCGA patients. An attention mechanism identified predictive genomic regions. The model achieved an AUC of 0.92 (95% CI: 0.89-0.94) in cross-validation and 0.88 (95% CI: 0.85-0.91) in independent validation, outperforming clinical models (ΔAUC = +0.18, p < 0.001). Key predictors included non-coding variants in TP53, KRAS, and PIK3CA regulatory regions. Triple-positive patients (mutations in all 3 regions) had significantly worse progression-free survival (HR = 4.7, p < 0.001). Our framework enables accurate chemotherapy response prediction and reveals novel non-coding resistance mechanisms, advancing precision oncology in CRC.

Humans↗

PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗

PubMed2026

SGLF-Net:Staged Global-to-Local Cross-Scale Fusion Network for Colonoscopic Polyp Segmentation.

Polyp segmentation in colonoscopy images plays a pivotal role in computer-aided medical diagnosis and the early prevention of colorectal cancer. However, existing methods often suffer from performance degradation when confronted with extreme polyp scale variation and polyp boundary ambiguity. To address these challenges, we propose the Staged Global-to-Local Cross-Scale Fusion Network (SGLF-Net), which adopts a novel staged global-to-local learning paradigm to progressively refine segmentation from coarse global semantics to fine-grained local details. Specifically, the Global Semantic Perception Stage integrates a Swin Transformer Encoder and a Dynamic Attentive Decoder (DAD) to construct comprehensive multi-scale contextual representations. The Local Detail Refinement Stage employs an Edge-aware Dynamic Attentive Decoder (E-DAD) to enhance structural fidelity and boundary precision through explicit edge-guided supervision. Furthermore, we introduce the Cross Spatial-Scale Feature Aggregation and Reconstitution (CSSAR) module, equipped with hybrid attention mechanisms, to facilitate efficient semantic structural interaction between the two cascaded stages. Extensive experiments on five public benchmark datasets demonstrate that SGLF-Net consistently outperforms state-of-the-art methods in both segmentation accuracy and boundary preservation.

Journal Article↗

PubMed2026

Recent advances in electrode materials for electrochemical detection of zearalenone.

Zearalenone (ZEN) is an estrogenic mycotoxin commonly found in cereals, animal feed, and processed foods, making it an important concern for food safety and public health. Conventional chromatographic and immunological methods can detect ZEN; however, they often require expensive instruments, lengthy sample preparation, and skilled personnel, which restrict their use for rapid and on-site testing. Electrochemical sensors have attracted enormous interest of the scientific community because of their high sensitivity, rapid response, low cost, miniaturization potential, and compatibility with portable systems. The analytical performance of the electrochemical sensors is strongly influenced by electrode materials, morphology, conductivity, porosity, surface functionality, and the efficiency of bioreceptor immobilization. Despite several reviews on mycotoxin detection, a systematic assessment connecting electrode-material design, modification strategies, sensing mechanisms, and electroanalytical performance specifically for ZEN sensing remain limited. This review critically evaluates recent advances in metal oxides, carbon-based materials, metal-organic- and covalent organic frameworks, MXenes, polymers, and hybrid composites for electrochemical ZEN detection. Particular attention has been given to their roles in electron transfer, analyte enrichment, selectivity, and real-sample analysis. The review also compares the major limitations of current sensing systems, including complex fabrication, matrix interference, insufficient long-term stability, poor inter-electrode reproducibility, and limited scalability. Finally, future directions for developing robust, cost-effective, portable, and commercially viable ZEN sensors are discussed.

Journal Article↗

PubMed2026

Divergent evolutionary strategies in spider venoms: A comparative proteomic profiling of four sympatric species from Yunnan.

Spider venoms comprise complex cocktails of bioactive molecules evolved for predation and defense, representing a valuable resource for biological research and pharmaceutical discovery. In this study, we performed a systematic analysis of venom gland extracts from four common spider species indigenous to Yunnan, China: Agelena limbata, Hippasa lycosina, Lycosa grahami, and Sinopoda pengi. Using an integrated transcriptomic and proteomic targeted profiling approach, we successfully annotated 141 distinct toxins. Comparative analysis revealed significant interspecific heterogeneity, suggesting distinct evolutionary trajectories and "weapon system economics." Both A. limbata and L. grahami exhibited a "peptide-dominant" profile anchored by neurotoxic peptides and isomerases, optimized for rapid chemical paralysis. In contrast, S. pengi displayed a distinct "protein-dominant" signature enriched with high-molecular-weight enzymes and CAP superfamily proteins, likely functioning to facilitate tissue degradation and toxin diffusion. Occupying an intermediate position, H. lycosina demonstrated a hybrid composition. These findings suggest that although these species share the same geographical range, their venom systems have undergone divergent evolutionary adaptations driven by specific ecological niches and hunting strategies. This study represents the first systematic proteomic characterization of these venom components, providing a valuable reservoir of molecular candidates while highlighting the bioinformatic nuances of analyzing whole-gland homogenates.

Animals↗

PubMed2026

Engineering bubble structures as Cas12a activators for highly sensitive monitoring of WRN helicase function.

The Werner syndrome helicase (WRN) is a critical synthetic lethal target in microsatellite instability cancers, essential for resolving complex genomic structures like replication bubbles and R-loops. However, strategies to simultaneously discriminate WRN activity on DNA versus DNA-RNA substrates in living cells are lacking. Here, we developed a structure-specific CRISPR/Cas12a biosensing strategy to visualize WRN functional activity by engineering bubble-structure probes. These probes were rationally designed to structurally mimic DNA replication bubbles and R-loop associated DNA-RNA hybrids. Upon specific unwinding by WRN, the probes release a sequestered activator strand that triggers Cas12a trans-cleavage, effectively converting the unwinding event into an amplified fluorescent signal. This assay achieves low picomolar sensitivity (LODs: 5.6-6.0 pM) and exceptional selectivity against homologous RecQ helicases. Uniquely, this strategy enables the parallel quantification of WRN activity on both substrate types, providing insights into distinct WRN-mediated pathways for resolving genomic stress. We further demonstrated the strategy's utility by visualizing endogenous WRN dynamics in living cells and profiling the efficacy of small-molecule inhibitors. This work offers a powerful molecular toolkit for dissecting WRN biology and facilitating high-throughput drug screening in targeted cancer therapy.

Werner Syndrome Helicase↗

PubMed2026

Cre-loaded integrase-defective lentiviral vectors for targeted cassette exchange in CHO cells.

Genome-modifying enzymes, such as recombinases and CRISPR-associated nucleases, enable targeted gene insertion when delivered transiently to minimize off-target effects. Precise genome engineering requires controlled enzyme activity, as well as efficient donor DNA transfer. Integrase-defective lentiviral vectors (IDLVs) provide a promising platform for transient episomal DNA transfer; however, their integration efficiency depends on complementary genome-targeting strategies. Here, we engineered Cre-loaded IDLVs (Cre-IDLVs) that co-package lentiviral vector genomes together with bioactive Cre recombinase. Cre was inserted into the Gag region of an integrase-defective gag-pol construct, allowing for efficient encapsidation and protease-mediated release during virion maturation without compromising the viral titer. The resulting particles carried donor cassettes flanked by heterospecific loxP sites. When applied to CHO founder cells harboring compatible genomic loxP landing pads, Cre-IDLVs efficiently mediated recombination-mediated cassette exchange, producing the highest number of G418-resistant colonies among the plasmid ratios tested. Genomic PCR and sequencing confirmed precise locus-specific insertion without detectable random integration in the analyzed clones. These findings establish Cre-IDLVs as a streamlined dual-delivery platform that couples transient recombinase activity with episomal donor DNA transfer. This hybrid lentiviral strategy provides a programmable approach for controlled and site-specific genome modification in mammalian cells.

Integrases↗

PubMed2026

An RPA-assisted homogeneous electrochemical DNA sensor for on-site eDNA detection toward early warning of crown-of-thorns starfish outbreaks.

Crown-of-thorns starfish (COTS) outbreaks seriously threaten coral reef ecosystems, while conventional monitoring approaches are time-consuming and often lack sufficient sensitivity for early warning. Existing electrochemical DNA sensors usually require complex electrode-surface immobilization procedures, which can lead to uneven probe distribution, significant steric hindrance, and poor stability. Meanwhile, the low concentration of environmental DNA (eDNA) in marine environments further complicates detection. To overcome these challenges, this study developed a homogeneous electrochemical DNA sensor assisted by recombinase polymerase amplification (RPA) for COTS eDNA detection. Target DNA was first amplified by RPA, and the amplification products were then hybridized in solution with capture probe (CP)-modified magnetic beads (MB) and biotin-labeled signal probe (SP) to form sandwich-structured MB complexes. These complexes were subsequently magnetically enriched and immobilized on the electrode surface for electrochemical signal readout. Under optimized conditions, the sensor displayed a linear response to COTS genomic DNA from 3.77 fg/μL to 1 ng/μL, with an LOD of 2.02 fg/μL and an LOQ of 3.77 fg/μL. The sensor was applied to Xisha Islands samples, and the results agreed with droplet digital PCR (ddPCR) (P > 0.05), demonstrating its potential for sensitive and reliable on-site COTS eDNA detection.

Animals↗

PubMed2026

Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.

PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.

Humans↗

PubMed2026

Genome-wide SNP data support species boundaries in sympatric Polylepis Ruiz & Pav. (Rosaceae) species from Bolivia and Ecuador.

Species delimitation in the South American genus Polylepis is notoriously challenging due to high morphological similarity and phenotypic plasticity, likely driven by hybridization and gene flow. Previous phylogenetic studies suggested that genetic structure aligns more strongly with geography than with taxonomy, questioning existing species concepts and hampering conservation efforts. We used double-digest RAD sequencing (ddRADseq) to generate genome-wide SNP data for 11 Polylepis species sampled across multiple localities in Bolivia and Ecuador. Population genetic analyses, phylogenetic inference, and network approaches were combined to assess whether genetic structure aligns more closely with taxonomy or geography. Morphologically defined species formed largely cohesive genetic lineages across regions, with species identity explaining substantially more genetic variation than locality. While localized admixture and reticulation were detected among closely related taxa, widespread species showed strong genetic cohesion and clear separation from congeners. Our results indicate that the sampled Polylepis species from Bolivia and Ecuador maintain distinct genetic identities despite localized signals consistent with gene flow. This genome-wide support for current taxonomy highlights Polylepis as a valuable model for studying speciation under gene flow and indicates that multiple geographic sampling will be essential in reconstructing a robust phylogeny of the genus, with important implications for conservation planning in Andean montane forests.

Bolivia↗

PubMed2026

LINE-1 repeats are a defining feature of the Xce.

During early development, female mammals inactivate one X chromosome to balance their X-linked gene dosage with males. While allelic choice is random in inbred mouse populations, choice can be significantly skewed in interstrain hybrids. The genetic basis of skewing has long been attributed to the mysterious "X chromosome controlling element(s)" (Xce) with different strengths among species, subspecies, and strains. When two X-chromosomes with different Xce strengths are inherited by offspring, the X chromosome with the stronger Xce will have a higher probability of remaining active. Here, we provide evidence that L1Tf repeats-a subfamily of long interspersed nuclear elements 1-plays a role in determining Xce strength. L1Tf elements form a condensed core within the inactive X (Xi) territory. Mouse strains with varying Xce strengths differ in the L1Tf copy number on the X chromosome, with the strength of the Xce allele being inversely related to L1Tf copy number. L1Tf expression mediates the Xce effect. However, in contrast to a prior report, L1Tf RNA does not coat the Xi. Rather, L1Tf promotes condensation of the Xi core. Intriguingly, L1Tfs recruit and sequester YY1 from active genes, accelerating XCI in cis. Thus, L1Tf copy number, expression, and binding of YY1 are key defining features of the Xce. We propose a model in which the Xce influences the choice of Xist alleles by promoting YY1 binding to the nucleation site for the initiation of Xist spreading.

Animals↗

PubMed2026

Identification and functional analysis of MeJA-responsive bHLH family genes in Taraxacum kok-saghyz.

Taraxacum kok-saghyz (T. kok-saghyz) is considered a highly promising alternative source of natural rubber (NR), as its roots synthesize high-molecular-weight NR comparable to that produced by Hevea brasiliensis. The basic helix-loop-helix (bHLH) family of transcription factors (TFs) plays crucial roles in plant organogenesis, hormonal signal transduction, and the regulation of secondary metabolism. This study aimed to systematically identify TkbHLH family members and to elucidate their potential functions in responding to methyl jasmonate (MeJA) and regulating root development. Based on the T. kok-saghyz genome, 172 TkbHLH members were identified and phylogenetically classified into 16 subfamilies. Among these, 37 genes were selected due to their significant induction by MeJA. Sequence analysis confirmed all encoded proteins contain the conserved bHLH domain. Subcellular localization verified nuclear localization of five core TkbHLH proteins. Interactions were shown by yeast two-hybrid and bimolecular fluorescence complementation, revealing these proteins form homodimers and heterodimers. Notably, a specific interaction was detected between TkbHLH162 and TkHMGS1, a key enzyme in the mevalonate (MVA) pathway, suggesting a potential molecular link between JA signaling and the rubber biosynthesis precursor pathway. Functional characterization via overexpression assays showed that selected TkbHLH genes significantly either promoted or inhibited root elongation. In summary, this study presents the first systematic characterization of the bHLH TF family in T. kok-saghyz, elucidating its involvement in JA signal response, protein interaction networks, and root development regulation. These findings provide a crucial foundation for further investigation into the molecular mechanisms by which TkbHLH TFs influence root morphogenesis and NR biosynthesis in T. kok-saghyz.

Taraxacum kok-saghyz (T. kok-saghyz)↗

PubMed2026

Cytonuclear conflict and reticulate evolution in the Morelloid clade (Solanum, Solanaceae): Insights from genome skimming and network Phylogenomics.

The Morelloid clade (black nightshades) is one of the most strongly supported clades within the megadiverse Solanum genus. It comprises 76 globally distributed, non-spiny herbaceous and suffrutescent species. While often erroneously considered poisonous weeds, several species are economically important as orphan crops. The clade is closely related to tomato and potato but, due to a lack of focused breeding efforts, remains a putative reservoir of genetic diversity for crop improvement. Despite this potential, we lack fundamental knowledge on the evolution of the Morelloid clade. The group includes polyploid species with unknown parental origins-likely reflecting reticulate processes such as hybridization, introgression, and associated backcrossing events. Prior analyses have been unable to disentangle these processes, leaving the mechanisms underlying reticulate evolution in the Morelloid clade poorly understood. Here, we use genome skimming to produce a well-supported maximum likelihood plastid phylogeny from complete circularized plastomes and a coalescent-based species tree from combined Angiosperms353 and conserved ortholog set nuclear markers. Our dataset, composed of previously published data and deep genome skimming from herbarium samples, spans 26 Morelloid species. To investigate phylogenetic discordance, we used a nuclear phylogenetic network, multispecies coalescent simulations, a fused rooted nuclear chloroplast tree, and quantification of nuclear gene tree concordance. We show that incongruence between nuclear and plastid trees is pervasive and cannot be explained by incomplete lineage sorting alone. Instead, our results demonstrate that events consistent with repeated chloroplast capture have shaped the reticulate evolutionary history of the clade, especially among African polyploid and Pan-American diploid lineages.

Phylogeny↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Molecular Diagnostics for WHO Priority Bacterial Pathogens: A Bibliometric Mapping of Diagnostic Platforms, Resistance Markers, and Antimicrobial Resistance Research Trends.

Antimicrobial resistance (AMR) constrains effective treatment and carries implications for infection control, surveillance, and public health. The World Health Organization (WHO) priority bacterial pathogen framework has intensified the need for diagnostic innovation by redefining research priorities around organisms combining high disease burden with complex resistance profiles. Molecular diagnostics have accordingly moved beyond culture-based workflows, integrating rapid pathogen identification, resistance-marker detection, genomic surveillance, and clinical decision support. The present study conducted a bibliometric mapping of the literature on WHO priority pathogens. Rather than addressing resistance at a general level or a single pathogen or technology, it integrates priority pathogens, molecular platforms, and resistance markers within a single framework, tracing their joint thematic and temporal evolution along an explicit pathogen-platform-marker axis. Scopus-indexed articles and reviews (2000-2025) were retrieved, yielding 1746 publications after screening adapted from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Analyses used Bibliometrix/Biblioshiny, R, and VOSviewer. The literature expanded markedly after 2018, led by China and the United States. Methicillin-resistant Staphylococcus aureus (MRSA), Mycobacterium tuberculosis, Enterococcus faecium, and the Enterobacterales-carbapenemase axis constituted the principal thematic cores, whereas conventional polymerase chain reaction (PCR)/nucleic acid amplification testing (NAAT) and whole-genome sequencing were the dominant platforms. Overall, the field has evolved from pathogen detection into an AMR-centered translational domain encompassing resistance prediction, genomic epidemiology, surveillance, and clinical decision support. Diagnostic development, stewardship, and surveillance depend on hybrid workflows coupling rapid marker-targeted assays with genome-based characterization, delivering actionable resistance within clinically meaningful timeframes, and extending coverage to underrepresented pathogens and platforms.

Humans↗

PubMed2026

A complete hlyCABD-like RTX operon marks a virulence-associated subset of trh-positive Vibrio parahaemolyticus from Hangzhou Bay, China.

Vibrio parahaemolyticus remains a major cause of seafood-associated gastroenteritis, yet routine surveillance still relies largely on the canonical hemolysin markers thermostable direct hemolysin (tdh) and tdh-related hemolysin (trh). To determine whether this framework overlooks accessory virulence determinants in trh-positive lineages, we analyzed 193 V. parahaemolyticus isolates collected between 2022 and 2025 from clinical, environmental, and seafood-associated sources in the Hangzhou Bay region of China. Serotyping identified 45 serotypes, with O10:K4 predominating among clinical isolates. Both clinical and non-clinical populations showed open pan-genomes, although the non-clinical group carried a larger accessory gene pool. We identified a complete hlyCABD-like RTX operon in 10 trh-positive isolates with T3SS2-associated virulence backgrounds. These RTX-positive isolates were distributed across seven sequence types and three of five phylogenetic groups. This distribution was lineage-restricted but non-clonal. In the representative hybrid-assembled genome, the operon occurred within a mosaic genomic region containing additional virulence- and mobility-associated genes, indicating a composite pathogenicity island-like element. In the tested subset, RTX-positive isolates showed significantly greater hemolytic activity than RTX-negative trh-positive isolates. This significant difference was consistently observed in both plate-based and liquid assays, and within the RTX-positive subset, hlyA expression correlated with hemolytic activity, whereas the trh gene and the tlh (thermolabile hemolysin) gene did not. A complete hlyCABD-like RTX operon therefore identifies a hemolysis-associated subset of trh-positive V. parahaemolyticus and supports its further evaluation as an additional target for food safety surveillance.

Vibrio parahaemolyticus↗

PubMed2026

Genomic and Molecular Interaction Analysis of NodD1 in a Novel Bradyrhizobium yuanmingense sp. B64 Isolate for Nodulation and Symbiosis of Legume Plants.

Rhizobial bacteria are known for their ability to fix nitrogen for leguminous plants and their essential function for sustainable agriculture. This study characterizes the taxonomic status and functional potential of the Bradyrhizobium B64 isolate using integrated genomic and molecular approaches. The whole genome of the B64 isolate was sequenced via Illumina paired-end technology. Species delimitation was performed using average nucleotide identity (ANI) and digital DNA-DNA Hybridization (dDDH). The NodD1 protein structure was modeled using AlphaFold3 and validated by Ramachandran plot analysis. Molecular docking was then conducted to evaluate interactions between NodD1 and four signaling flavonoids: Apigenin, Daidzein, Genistein, and Naringenin. Genomic analysis revealed a maximum ANI of 94.4% and dDDH values between 51.4 and 62.4%. Since these values fall below the standard prokaryotic thresholds (ANI < 95%; dDDH < 70%), the B64 isolate is identified as a novel species. Physiological assays confirmed nitrogen fixation (1.97 ppm), IAA production (3.67 ppm), and phosphate solubilization (26.10 ppm). Structural validation showed 100% of NodD1 residues in allowed regions, ensuring high model reliability. Docking simulations demonstrated strong binding affinities across all flavonoids, with binding free energies ranging from - 8.8 to - 9.0 kcal/mol. Daidzein exhibited the highest thermodynamic stability (- 9.0 kcal/mol), whereas apigenin showed the most extensive residue interaction network. The B64 isolate is a novel Bradyrhizobium species with a high symbiotic capacity. The stable NodD1-flavonoid interactions provide a molecular basis for efficient nodulation, positioning B64 as a promising candidate for developing lipo-chitooligosaccharide (LCO)-based biofertilizers.

Bradyrhizobium↗