Biomaterials. 2025 Jun 26;324:123528. doi: 10.1016/j.biomaterials.2025.123528. Online ahead of print.

ABSTRACT

Oral infectious diseases, including dental caries, pulpitis, periodontitis, peri-implantitis, and osteomyelitis of the jaws, are among the most prevalent conditions in dentistry, primarily caused by bacterial infections. Traditional treatment approaches, such as mechanical debridement and antibiotic therapy, face limitations due to bacterial resistance, inadequate infection control, and the inability to promote tissue regeneration effectively. Metal-based nanomaterials have emerged as promising candidates for addressing these challenges, offering broad-spectrum antibacterial activity, immunomodulation, and regenerative properties. This review provides an in-depth analysis of the therapeutic potential of metal-containing nanomaterials in managing oral infectious diseases. It explores their antibacterial mechanisms, including membrane disruption, oxidative stress induction, and metabolic interference. Additionally, we discuss their role in modulating inflammation and promoting tissue regeneration through stem cell differentiation and extracellular matrix remodeling. The application of these nanomaterials in caries prevention, endodontic therapy, periodontal treatment, and implantology is critically examined. Finally, we highlight key challenges, including biosafety concerns, clinical translation hurdles, and material optimization strategies. By summarizing recent advances and emerging trends, this review aims to provide insights into the development of innovative nanotherapeutics for enhanced oral healthcare.

PMID:40587916 | DOI:10.1016/j.biomaterials.2025.123528

JMIR Med Inform. 2025 Jun 30;13:e60204. doi: 10.2196/60204.

ABSTRACT

BACKGROUND: Evidence-based medicine combines scientific research, clinical expertise, and patient preferences to enhance the patient outcomes and improve health care quality. Clinical data are crucial in aligning medical decisions with evidence-based practices, whether derived from systematic research or real-world data sources. Quality assurance of clinical data, mainly through predictive quality algorithms and machine learning, is essential to mitigate risks such as misdiagnosis, inappropriate treatment, bias, and compromised patient safety. Furthermore, excellent quality of clinical data is a prerequisite for the replication of research results in order to gain insights from practice and real-world evidence.

OBJECTIVE: This study aims to demonstrate the varying quality of medical data in primary clinical source systems at a maximum care university hospital and provide researchers with insights into data reliability through predictive quality algorithms using machine learning techniques.

METHODS: A literature review was conducted to evaluate existing approaches to automated quality prediction. In addition, embedded in the process of integrating care data into a medical data integration center (MeDIC), metadata relevant to this clinical data was stored, considering factors such as data granularity and quality metrics. Completed patient cases with echocardiographic and laboratory findings as well as medication histories were selected from 2001 to 2023. Two authors manually reviewed the datasets and assigned a quality score for each entry, with 0 indicating unsatisfactory and 1 satisfactory quality. Since quality control was considered a binary problem, corresponding classifiers were used for the quality prediction. Logistic regression, k-nearest neighbors, a naive bayes classifier, a decision tree classifier, a random forest classifier, extreme gradient boosting (XGB), and support vector machines (SVM) were selected as machine learning algorithms. Based on preprocessing the dataset, training machine learning algorithms on echocardiographic, laboratory, and medication data, and assessing various prediction models, the most effective algorithms for quality classification were to be identified. The performance of the predictive quality algorithms was assessed based on accuracy, precision, recall, and scoring.

RESULTS: There were 450 patient cases with complete information extracted from the MeDIC data pool. The laboratory and medication datasets had to be limited to 4000 data entries each to enable manual review; the echocardiographic datasets comprised 750 examinations. XGB demonstrated the highest performance for the echocardiographic dataset with an area under the receiver operating characteristic curve (AUC-ROC) of 84.6%. For laboratory data, SVM achieved an AUC-ROC score of 89.8%, demonstrating superior discrimination performance. Finally, regarding the medication dataset, SVM showed the most balanced performance, achieving an AUC-ROC of 65.1%, the highest of all tested models.

CONCLUSIONS: This proposal presents a template for predicting data quality and incorporating the resulting quality information into the metadata of a data integration center, a concept not previously implemented. The model was deployed for data inspection using a hybrid approach that combines the trained model with conventional inspection methods.

PMID:40587839 | DOI:10.2196/60204

Proc Natl Acad Sci U S A. 2025 Jul 8;122(27):e2501111122. doi: 10.1073/pnas.2501111122. Epub 2025 Jun 30.

ABSTRACT

The endosomal-lysosomal network is a hub of organelles that orchestrate the dynamic sorting of hundreds of integral membrane proteins to maintain cellular homeostasis. VPS29 is a central conductor of this network through its assembly into Retromer, Retriever, and Commander endosomal sorting complexes, and its role in regulating RAB GTPase activity. Two VPS29 isoforms have been described, VPS29A and VPS29B, that differ solely in their amino-terminal sequences. Here, we identify a third VPS29 isoform, which we term VPS29C, that harbors an extended amino-terminal sequence compared to VPS29A and VPS29B. Through a combination of AlphaFold predictive modeling, in vitro complex reconstitution, mass spectrometry, and molecular cell biology, we find that the amino-terminal VPS29C extension constitutes an autoinhibitory sequence that limits access to a hydrophobic groove necessary for effector protein recruitment to Retromer, and association with Retriever and Commander. VPS29C is therefore unique in its ability to uncouple Retromer-dependent cargo sorting from the broader roles of VPS29A and VPS29B in regulating the endosomal-lysosomal network through accessory protein recruitment. Our identification and characterization of VPS29C points to additional complexity in the differential subunit assembly of Retromer, an important consideration given the increasing interest in Retromer as a potential therapeutic target in neurodegenerative diseases.

PMID:40587794 | DOI:10.1073/pnas.2501111122

J Vis Exp. 2025 Jun 10;(220). doi: 10.3791/68353.

ABSTRACT

Xenopus has long been a pivotal model organism for investigating vertebrate development and disease, offering deep insights into cellular processes and gene function. Despite the wealth of information on embryonic Xenopus, there remains a significant gap in standardized methods for adult tissue sampling, especially for modern approaches like quantitative proteomics. This study introduces a comprehensive protocol for rapid, precise, and efficient sampling of multiple tissues in adult Xenopus. The protocol addresses challenges associated with the subtle anatomical differences compared to other anurans, ensuring reproducibility even for those with limited experience in frog dissection. This protocol is optimized for high-quality biochemical analyses by prioritizing sample freshness. We are facilitating the rapid collection of up to 18 tissues within an hour. Additionally, the methods apply to perfused and unperfused conditions, providing flexibility for a range of experimental needs. This work not only fills a critical methodological gap for Xenopus laevis and tropicalis but also serves as a valuable resource for researchers adapting techniques to similar amphibian models, thereby enhancing the scope and reliability of comparative biological and evolutionary studies.

PMID:40587527 | DOI:10.3791/68353

PLoS One. 2025 Jun 30;20(6):e0320708. doi: 10.1371/journal.pone.0320708. eCollection 2025.

ABSTRACT

Fungi and aphids show mutual interactions on barley pathogenesis. Fungi promote pathogenesis, while aphids either weaken or strengthen the infection. Otherwise, fungi alter aphid behavior and performance, further highlighting their complex interactions. Characterizing these synergistic and antagonistic interactions is crucial for understanding pathogenesis. Therefore, we performed meta-analysis and co-expression gene network analyses of the barley transcriptome in response to fungus and aphid based on hormone signaling pathways. We selected 13 studies, including 380 fungal infection samples, 48 aphid-attack samples, and 34 hormone-treated samples. We showed that 1.1% of DEGs were common between fungal and aphid-related datasets, while only 0.1% of DEGs were shared among all datasets. In addition, 70% of common DEGs were uniquely regulated by JA or SA signaling. In contrast, 30% of DEGs were regulated by both JA and SA simultaneously. Regulatory element analysis revealed that 85% of DEGs contained at least one binding site from AP2/EREBP or C2H2 zinc-finger factors that show substantial roles in SAR/ISR pathways during plant defense. Gene network analysis identified key hub genes, including SSI2, PAD2, RPS1, RPS17, SHM1, CYP5, and RPL21C, which influence plant host preference in response to pathogens. Moreover, we identified novel hub genes with unknown functions that potentially interact with the genes involved in defense responses and host preference. This study presents the first systems biology analysis of barley transcriptomic responses to heterotroph/biotroph cross-talk focusing on the preference and performance of Rhopalosiphum padi. Our findings suggest critical insights into the molecular mechanisms underlying barley defense responses and identify valuable candidate genes to developing pathogen resistance genotypes in agricultural systems.

PMID:40587507 | DOI:10.1371/journal.pone.0320708

Elife. 2025 Jun 30;14:e107575. doi: 10.7554/eLife.107575.

ABSTRACT

The mRNA metabolism passively shapes the levels of an mRNA modification called m6A within a steady-state cell and upon stress.

PMID:40586780 | DOI:10.7554/eLife.107575

Elife. 2025 Jun 30;13:RP100448. doi: 10.7554/eLife.100448.

ABSTRACT

m6A is the most widespread mRNA modification and is primarily implicated in controlling mRNA stability. Fundamental questions pertaining to m6A are the extent to which it is dynamically modulated within cells and across stimuli, and the forces underlying such modulation. Prior work has focused on investigating active mechanisms governing m6A levels, such as recruitment of m6A writers or erasers leading to either 'global' or 'site-specific' modulation. Here, we propose that changes in m6A levels across subcellular compartments and biological trajectories may result from passive changes in gene-level mRNA metabolism. To predict the intricate interdependencies between m6A levels, mRNA localization, and mRNA decay, we establish a differential model 'm6ADyn' encompassing mRNA transcription, methylation, export, and m6A-dependent and -independent degradation. We validate the predictions of m6ADyn in the context of intracellular m6A dynamics, where m6ADyn predicts associations between relative mRNA localization and m6A levels, which we experimentally confirm. We further explore m6ADyn predictions pertaining to changes in m6A levels upon controlled perturbations of mRNA metabolism, which we also experimentally confirm. Finally, we demonstrate the relevance of m6ADyn in the context of cellular heat stress response, where genes subjected to altered mRNA product and export also display predictable changes in m6A levels, consistent with m6ADyn predictions. Our findings establish a framework for dissecting m6A dynamics and suggest the role of passive dynamics in shaping m6A levels in mammalian systems.

PMID:40586779 | DOI:10.7554/eLife.100448

Food Funct. 2025 Jun 30. doi: 10.1039/d5fo01239b. Online ahead of print.

ABSTRACT

Caenorhabditis elegans is widely used as a model for investigating longevity owing to its short life cycle and the presence of human orthologs. This study aimed to ultimately prolong the lifespan of C. elegans by evaluating the gastrointestinal tract conditions and intestinal permeability of C. elegans fed Lacticaseibacillus casei HY2782 alone or fermented fecal products. The anti-inflammatory effects of L. casei HY2782 were determined based on interleukin 8 (IL-8) levels and intestinal permeability in human intestinal epithelial cells. In the C. elegans model, intestinal permeability was assessed in the N2 wild-type as well as skn-1, pmk-1, daf-16, and aak-2 mutant worms. During simulated colonic fecal fermentation, changes in short chain fatty acid and microbial composition were investigated. L. casei HY2782 reduced IL-8 production and intestinal permeability from 1646.8 to 1009.1 pg mL-1 and 265.5 to 115.1%, respectively (p < 0.01). Additionally, L. casei HY2872 attenuated intestinal leakage in C. elegans and prolonged its lifespan via the DAF-16/FOXO and SKN-1/NRF2 pathways and gst-4 gene expression. Moreover, L. casei HY2782 inhibited intestinal leakage in aged worms. During fermentation, L. casei HY2782 produced butyrate under both normal and high-protein conditions. Additionally, L. casei HY2782 contributed to butyrate production by genera, such as Faecalibacterium and Lachnospira (p < 0.01), while inhibiting Fusobacterium (p < 0.05). L. casei HY2782 also prolonged lifespan of intestinally damaged worms (p < 0.001). Furthermore, C. elegans fed L. casei HY2782-fermented fecal products lived significantly longer than those fed the vehicle control (p < 0.001). Overall, L. casei HY2782 restored the intestinal tract by mitigating inflammation and microbial metabolic dysbiosis, ultimately extending the lifespan of C. elegans.

PMID:40586753 | DOI:10.1039/d5fo01239b

Genome Biol. 2025 Jun 29;26(1):185. doi: 10.1186/s13059-025-03620-8.

ABSTRACT

BACKGROUND: Cassava is an important crop for food security in the tropics where its production is jeopardized by several viral diseases, including the cassava mosaic disease (CMD) which is endemic in Sub-Saharan Africa and the Indian subcontinent. Resistance to CMD is linked to a single dominant locus, namely CMD2. The cassava genome contains highly repetitive regions making the accurate assembly of a reference genome challenging.

RESULTS: In the present study, we generate BAC libraries of the CMD-susceptible cassava cultivar (cv.) 60444 and the CMD-resistant landrace TME3. We subsequently identify and sequence BACs belonging to the CMD2 region in both cultivars using high-accuracy long-read PacBio circular consensus sequencing (ccs) reads. We then sequence and assemble the complete genomes of cv. 60444 and TME3 using a combination of ONT ultra-long reads and optical mapping. Anchoring the assemblies on cassava genetic maps reveals discrepancies in our, as well as in previously released, CMD2 regions of the cv. 60444 and TME3 genomes. A BAC-guided approach to assess cassava genome assemblies significantly improves the synteny between the assembled CMD2 regions of cv. 60444 and TME3 and the CMD2 genetic maps. We then performed repeat-unmasked gene annotation on CMD2 assemblies and identify 81 stress resistance proteins present in the CMD2 region, among which 31 were previously not reported in publicly available CMD2 sequences.

CONCLUSIONS: The BAC-assessed approach improved CMD2 region accuracy and revealed new sequences linked to virus resistance, advancing our understanding of cassava mosaic disease resistance.

PMID:40583058 | DOI:10.1186/s13059-025-03620-8

Mol Cell Proteomics. 2025 Jun 26:101024. doi: 10.1016/j.mcpro.2025.101024. Online ahead of print.

ABSTRACT

Crosslinking Mass Spectrometry (MS) can uncover protein-protein interactions and provide structural information on proteins in their native cellular environments. Despite its promise, the field remains hampered by inconsistent data formats, variable approaches to error control, and insufficient interoperability with global data repositories. Recent advances, especially in false discovery rate (FDR) models and pipeline benchmarking, show that Crosslinking MS data can reach a reliability that matches the demand of integrative structural biology. To drive meaningful progress, however, the community must agree on error estimation, open data formats, and streamlined repository submissions. This perspective highlights these challenges, clarifies remaining barriers, and frames practical next steps. Successful field harmonisation will enhance the acceptance of Crosslinking MS in the broader biological community and is critical for the dependability of the data, no matter where it is produced.

PMID:40581115 | DOI:10.1016/j.mcpro.2025.101024

Genomics Proteomics Bioinformatics. 2025 Jun 20:qzaf057. doi: 10.1093/gpbjnl/qzaf057. Online ahead of print.

ABSTRACT

As one of the most crucial post-translational modifications (PTMs), protein phosphorylation regulates a broad range of biological processes in eukaryotes. Biocuration, integration, and annotation of reported phosphorylation events will deliver a valuable resource for the community. Here, we present an updated database, the eukaryotic phosphorylation site database 2.0 (EPSD 2.0), which includes 2,769,163 experimentally identified phosphorylation sites (p-sites) in 362,707 phosphoproteins from 223 eukaryotes. From the literature, 873,718 new p-sites identified through high-throughput phosphoproteomic research were first collected, and 1,078,888 original phosphopeptides together with primary references were reserved. Then, this dataset was merged into EPSD 1.0, comprising 1,616,804 p-sites within 209,326 proteins across 68 eukaryotic organisms [1]. We also integrated 362,190 additional known p-sites from 10 public databases. After redundancy clearance, we manually re-checked each p-site and annotated 88,074 functional events for 32,762 p-sites, covering 58 types of downstream effects on phosphoproteins, and regulatory impacts on 107 biological processes. In addition, phosphoproteins and p-sites in 8 model organisms were meticulously annotated utilizing information supplied by 100 external platforms encompassing 15 areas. These areas included kinase/phosphatase, transcription regulators, three-dimensional structures, physicochemical characteristics, genomic variations, functional descriptions, protein domains, molecular interactions, drug-target associations, disease-related data, orthologs, transcript expression levels, proteomics, subcellular localization, and regulatory pathways. We expect that EPSD 2.0 will become a useful database supporting comprehensive studies on phosphorylation in eukaryotes. The EPSD 2.0 database is freely accessible online at https://epsd.biocuckoo.cn/.

PMID:40581078 | DOI:10.1093/gpbjnl/qzaf057

Genome Biol Evol. 2025 Jun 21:evaf126. doi: 10.1093/gbe/evaf126. Online ahead of print.

ABSTRACT

What accounts for the variation between proteins in their rate of evolution per synonymous substitution (i.e. dN/dS, alias ω )? Previous analyses suggested that cell location is predictive, with intracellular proteins evolving slower than membrane proteins, a result considered supportive of the extended complexity hypothesis (ECH). However, as they occur in 3D space, cytoplasmic proteins are expected to be more abundant. As the level of gene expression is the strongest predictor of ω , and many predictors of protein rate variation are explained by covariance with it, here we ask whether the cell compartment effect is explained by covariates. We employ two single-celled species for which there exist exceptional data, the bacterium (Escherichia coli) and the eukaryote (Saccharomyces cerevisiae). For both, we establish informative species trios to determine branch-specific ω values. In both species, in the absence of covariate control, cytoplasmic proteins evolve relatively slowly, while membrane proteins evolve fast, as originally claimed. After controlling for protein abundance, however, membrane proteins have the lowest rates, this inversion being resilient to multiple alternative abundance control methods. The effect size of the cell compartment as a predictor is of a comparable magnitude to the essentiality effect and remains when allowing for essentiality. We conclude that the effects of the cell compartments are real, but their direction is dependent on the presence or absence of abundance control. These results question any model, such as the ECH, that claims support from a slower evolution of cytoplasmic proteins and underscore the importance of covariate control.

PMID:40580929 | DOI:10.1093/gbe/evaf126

Neuro Oncol. 2025 Jun 24:noaf129. doi: 10.1093/neuonc/noaf129. Online ahead of print.

ABSTRACT

BACKGROUND: Understanding the factors that determine the spontaneous regression of pre-cancerous lesions is critical to advancing cancer prevention. Neuroblastoma, a pediatric cancer, undergoes spontaneous regression more frequently than other types of cancer.

METHODS: Here, we analyzed the transcriptomic features of spontaneous regression in pre-cancerous neuroblasts using Th-MYCN mice, an animal model that closely resembles human neuroblastoma. Single-cell transcriptomic analysis of ganglion tissues from Th-MYCN mice was conducted to elucidate the cellular and molecular underpinnings.

RESULTS: Trajectory analysis of pre-cancerous neuroblasts revealed a distinct subtype we designated as "uncommitted" cells, characterized by the expression of neuronal genes, indicative of a semi-differentiated state. Samples with predicted failed tumorigenesis had a greater proportion of these uncommitted cells, hinting at their association with spontaneous regression. In clinical specimens, heightened uncommitted gene expression corresponded with favorable neuroblastomas and an improved prognosis.

CONCLUSION: Collectively, the identification of this novel neuroblastoma-related cell subtype and its transcriptomic signature not only enhances our understanding of spontaneous regression mechanisms but also holds potential for therapeutic advancements in treating neuroblastomas.

PMID:40580553 | DOI:10.1093/neuonc/noaf129

Plant Physiol. 2025 Jun 28:kiaf283. doi: 10.1093/plphys/kiaf283. Online ahead of print.

ABSTRACT

Brassica napus (canola; AnAnCnCn) contains both complete diploid genomes from its progenitors B. rapa (An) and B. oleracea (Cn). Despite growing knowledge of the gene expression landscape of the B. napus seed, little is known about subgenome bias underpinning the development of specific cells and tissues across the seed lifecycle. Here, we present a large-scale transcriptome atlas of the B. napus seed, including both the maternal seed coat and filial embryo and endosperm subregions. We report on extensive, global Cn subgenome bias throughout development and use homoeologous gene pairs to describe how subgenomic bias differs across subregions. We find that subgenome bias is most prominent during early development and that the maternal subregions experience far more asymmetric transcript accumulation in favour of the Cn subgenome. In particular, the unexpectedly distinct transcriptome profile of the chalazal pole indicates the unique developmental processes involved within the chalaza. Further, we report that genes integral to seed storage comprise a large portion of the transcriptome of mature seeds, especially within the embryo, and that gene pairs previously documented to be instrumental in seed development exhibit low transcriptional bias. This work represents an important synthesis of polyploid transcriptomics in seed biology and provides a comprehensive overview of the B. napus gene expression landscape in both space and time.

PMID:40580494 | DOI:10.1093/plphys/kiaf283

Cell Rep. 2025 Jun 26;44(7):115907. doi: 10.1016/j.celrep.2025.115907. Online ahead of print.

ABSTRACT

We investigate the long-term impact of repeated COVID-19 vaccinations on adaptive immunity through a 3-year study of 78 individuals without reported symptomatic infections. We observe distinct dynamics in spike-specific responses across multiple vaccine doses. While antibody levels increase and stabilize with each booster, T cell responses quickly plateau and remain stable. Notably, approximately 30% of participants show evidence suggestive of asymptomatic infections. Single-cell RNA sequencing reveals a diverse and stable landscape of spike-specific T cell phenotypes without signs of exhaustion or functional impairment. Individuals with evidence of asymptomatic infection display increased frequencies of Th17-like CD4+ T cells and GZMKhi/IFNR+ CD8+ T cell subsets. In this group, repeated vaccinations correlate with an increase in regulatory T cells, potentially indicating a balanced immune response that may mitigate immunopathology. By regularly stimulating T cell memory, boosters contribute to a stable and enhanced immune response, which may provide better protection against symptomatic infections.

PMID:40580476 | DOI:10.1016/j.celrep.2025.115907

Bioinformatics. 2025 Jun 28:btaf383. doi: 10.1093/bioinformatics/btaf383. Online ahead of print.

ABSTRACT

MOTIVATION: Knowledge graphs (KGs) in life sciences have become an important application of systems biology as they delineate complex biological and pathophysiological phenomena. They are composed of biological and chemical entities represented with standard ontologies to comply with Findable, Accessible, Interoperable and Reusable (FAIR) principles. Alongside serving as a graph database, KGs hold the potential to address complex scientific queries and facilitate downstream analyses. However, the process of constructing KGs is expensive and time-consuming as it primarily relies on manual curation from published literature and experimental data. The existing text-mining workflows are still in their infancy and fail to achieve the accuracy and reliability of manual curation.

RESULTS: Knowledge Graph Generator (KGG) is an automated workflow for representing chemotype and phenotype of diseases and medical conditions. It embeds the underlying schema of curated databases such as OpenTargets, Uniprot, ChEMBL, Integrated Interactions Database and GWAS Central resembling a clockwork-esque mechanism. The resultant KG is a comprehensive and rational assembly of disease-associated entities such as proteins, protein-related pathways, biological processes and functions, genetic variants, chemicals, mechanism of actions, assays and adverse effects. As use cases, we have used KGs to identify shared entities for possible link of comorbidity and compared them with KGs from other sources. We have also demonstrated a use case of identifying putative new targets and repurposing drug candidates in Parkinson's Disease. Lastly, we have developed reusable workflows to explore drug-likeness of chemicals and identify structures of proteins.

AVAILABILITY AND IMPLEMENTATION: The resources and codes for KGG are publicly available at: https://github.com/Fraunhofer-ITMP/kgg.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online and GitHub.

PMID:40580451 | DOI:10.1093/bioinformatics/btaf383

Bioinformatics. 2025 Jun 28:btaf380. doi: 10.1093/bioinformatics/btaf380. Online ahead of print.

ABSTRACT

SUMMARY: Knowledge graphs are increasingly being used to integrate heterogeneous biomedical knowledge and data. General-purpose graph database management systems such as Neo4j are often used to host and search knowledge graphs, but such tools come with overhead and leave biomedical-specific standards compliance and reasoning to the user. Interoperability across biomedical knowledge bases and reasoning systems necessitates the use of standards such as those adopted by the Biomedical Data Translator consortium. We present PloverDB, a comprehensive software platform for hosting and efficiently serving biomedical knowledge graphs as standards-compliant web application programming interfaces. In addition to fundamental back-end knowledge reasoning tasks, PloverDB automatically handles entity resolution, exposure of standardized metadata and test data, and multiplexing of knowledge graphs, all in a single platform designed specifically for efficient query answering and ease of deployment. PloverDB increases data accessibility and utility by allowing data providers to quickly serve their biomedical knowledge graphs as standards-compliant web services.

AVAILABILITY AND IMPLEMENTATION: PloverDB's source code and technical documentation are publicly available under an MIT License at github: RTXteam/PloverDB, archived on Zenodo at doi : 10.5281/zenodo.15454600.

SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.

PMID:40580448 | DOI:10.1093/bioinformatics/btaf380

Bioinformatics. 2025 Jun 28:btaf381. doi: 10.1093/bioinformatics/btaf381. Online ahead of print.

ABSTRACT

MOTIVATION: The Adverse Outcome Pathways (AOP) framework advances alternative toxicology by prioritizing the mechanisms underlying toxic effects. It organizes existing knowledge in a structured way, tracing the progression from the initial perturbation of a molecular event, caused by various stressors, through key events (KEs) across different biological levels, ultimately leading to adverse outcomes that affect human health and ecosystems. However, the increasing volume of toxicological data presents a significant challenge for integrating all available knowledge effectively.

RESULTS: Text mining (TM) techniques, including natural language processing (NLP) and graph-based approaches, provide powerful methods to analyze and integrate large, heterogeneous data sources. Within this framework, the AOP-helpFinder text mining tool, accessible as a web server, was created to identify stressor-event and event-event relationships by automatically screening scientific literature in the PubMed database, facilitating the development of AOPs. The proposed new version introduces enhanced functionality by incorporating additional data sources, automatically annotating events from the literature with toxicological database information in a systems biology context. Users can now visualize results as interactive networks directly on the web server. With these advancements, AOP-helpFinder 3.0 offers a robust solution for integrative and predictive toxicology, as demonstrated in a case study exploring toxicological mechanisms associated with radon exposure.

AVAILABILITY: AOP-helpFinder is available at https://aop-helpfinder-v3.u-paris-sciences.fr.

SUPPLEMENTARY INFORMATION: Supplementary data are available on Zenodo 10.5281/zenodo.15193935 and on GitHub: https://github.com/systox1124/AOP-helpFinder.

PMID:40580447 | DOI:10.1093/bioinformatics/btaf381

Methods Mol Biol. 2025;2943:31-45. doi: 10.1007/978-1-0716-4642-7_3.

ABSTRACT

Pepino mosaic virus (PepMV) is a plant pathogen causing significant economic losses in tomato production. Sensitive, reliable, and robust detection methods are crucial for containing the spread of PepMV and reducing its damaging effects. Digital PCR (dPCR) presents several advantages to conventional real-time quantitative PCR (qPCR), including absolute quantification ability, robust quantitative multiplexing capabilities, and straightforward result analysis. Furthermore, dPCR is especially suitable for analysis of complex samples due to its remarkable tolerance to PCR inhibitors, which makes it a promising method for plant virus genotyping. In this chapter, we present two protocols for PepMV genotyping and quantification using one-step reverse transcription digital PCR (RT-dPCR). The first protocol outlines four simplex assays, while the second describes two duplex assays for precise and comprehensive genotyping of PepMV variants.

PMID:40580283 | DOI:10.1007/978-1-0716-4642-7_3

Methods Mol Biol. 2025;2943:19-29. doi: 10.1007/978-1-0716-4642-7_2.

ABSTRACT

The genotyping workflow described uses digital PCR (dPCR) to detect and quantify drug resistance mutations in human cytomegalovirus (HCMV). The method focuses on the detection and quantification of three common mutations in the UL97 gene at codons 460, 594, and 595, which are responsible for the majority of ganciclovir-resistant clinical isolates. The dPCR approach offers high sensitivity and accuracy, making it suitable for routine testing as well as a reference measurement procedure for external quality assessment schemes. The workflow includes several key steps: DNA isolation, preparation of the dPCR reaction mixture, partitioning, thermocycling, and data analysis. This method improves the detection capabilities of HCMV drug resistance and provides a robust and efficient tool for clinical and research applications.

PMID:40580282 | DOI:10.1007/978-1-0716-4642-7_2