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Tick-bite hypersensitivity encompasses a range of clinical manifestations, from localized allergic reactions to systemic conditions like alpha-gal syndrome (AGS), an IgE-mediated allergy to galactose-α-1,3-galactose (α-Gal). This study investigated the clinical, molecular, immunological, and genetic features of two hypersensitivity cases. Two cases were analyzed: a 30-year-old woman with fixed drug reaction (FDR)-like hypersensitivity and a 10-year-old girl with AGS exhibiting borderline α-Gal-specific IgE. Diagnostic methods included allergen-specific IgE quantification, HLA genotyping, histopathological examination, and the molecular detection of tick-borne pathogens using microfluidic PCR. Case I demonstrated histopathological features of chronic lymphocytic inflammation and eosinophilic infiltrates, with HLA-B13 and DRB113 alleles indicating genetic susceptibility to hypersensitivity, while histological findings suggested a localized FDR-like reaction. Case II exhibited borderline α-Gal-specific IgE, resolving completely with a mammalian-free diet. The presence of HLA-DRB101 and DQB1*05 in the second patient indicated a genetic predisposition to AGS and other atopic conditions. No infectious etiology was identified in either case. These findings emphasize the heterogeneity of tick-related hypersensitivity and the importance of HLA genotypes in susceptibility. Comprehensive molecular, immunological, and genetic profiling offers valuable insights into the mechanisms of hypersensitivity, supporting personalized approaches for the diagnosis and management of tick-induced allergic conditions. [ABSTRACT FROM AUTHOR]

The pursuit of obtaining enzymes with high activity and stability remains a grail in enzyme evolution due to the stability-activity trade-off. Here, we develop an isothermal compressibility-assisted dynamic squeezing index perturbation engineering (iCASE) strategy to construct hierarchical modular networks for enzymes of varying complexity. Molecular mechanism analysis elucidates that the peak of adaptive evolution is reached through a structural response mechanism among variants. Furthermore, this dynamic response predictive model using structure-based supervised machine learning is established to predict enzyme function and fitness, demonstrating robust performance across different datasets and reliable prediction for epistasis. The universality of the iCASE strategy is validated by four sorts of enzymes with different structures and catalytic types. This machine learning-based iCASE strategy provides guidance for future research on the fitness evolution of enzymes. The authors design an isothermal compressibility-assisted dynamic squeezing index perturbation (iCASE) methodology to improve enzyme stability and efficacy, which is combined with machine learning predictive models to advance enzyme optimization. [ABSTRACT FROM AUTHOR]

Ankylosing spondylitis (AS) is a rheumatic disease that causes inflammation and bone formation in the spine. Despite significant advances in treatment, adverse side effects have triggered research into natural compounds. Epimedium (EP) is a traditional Chinese herb with a variety of pharmacological activities, including antirheumatic, anti-inflammatory, and immunomodulatory activities; however, its direct effects on AS treatment and the underlying molecular mechanisms have not been systematically studied. Thus, here, we used network pharmacology, molecular docking, and molecular dynamics simulations to explore the targets of EP for treating AS. We constructed an interaction network to elucidate the complex relationship between EP and AS. Sixteen active ingredients in EP were screened; 80 potential targets were identified. In particular, 8-(3-methylbut-2-enyl)-2-phenylchromone, anhydroicaritin, and luteolin were the core components and TNF, IL-6, IL-1β, MMP9, and PTGS2 were the core targets. The GO and KEGG analyses indicated that EP may modulate multiple biological processes and pathways, including the AGE-RAGE, TNF, NF-κB/MAPK, and TLR signaling pathways, for AS treatment. Molecular docking and molecular dynamics simulations showed good affinity between the active components and core targets of EP, with stable binding within 100 nanoseconds. In particular, 8-(3-methylbut-2-enyl)-2-phenylchromone possessed the highest free energy of binding to PTGS2 and TNF (-115.575 and - 87.676 kcal/mol, respectively). Thus, EP may affect AS through multiple pathways, including the alleviation of inflammation, oxidative stress, and immune responses. In summary, we identified the active components and potential targets of EP, highlighting new strategies for the further experimental validation and exploration of lead compounds for treating AS., Competing Interests: Declarations. Conflict of interest: The authors declare no competing interests. Ethical approval: Not applicable., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)