BACKGROUND
Acute lung injury (ALI), a common respiratory disease with high morbidity and mortality, poses a serious public health threat. Apart from mechanical ventilation, limited safe and effective clinical therapies are available. Alisol B 23-acetate (AB23-a), one of the principal bioactive components of Rhizoma Alismatis, alleviates diverse inflammatory conditions, yet its role in LPS-induced ALI has not been reported.
PURPOSE
This study aimed to explore whether AB23-a alleviates ALI and to identify potential mechanisms contributing to its protective effects.
STUDY DESIGN
LPS-induced BEAS-2B cells, RAW264.7 macrophages, mouse lung organoids (MLOs) and an LPS-induced mouse ALI model were used as experimental systems. Cells and animals were treated with gradient AB23-a. Functional tests assessed inflammation and macrophage polarization. FPR2 inhibition and ANXA1 knockout/rescue assays verified target engagement and receptor-dependent downstream signaling.
METHODS
qRT-PCR and western blotting detected mRNA and protein levels of inflammatory mediators and key ANXA1/FPR2-mediated MAPK/NF-κB signaling molecules. Immunofluorescence evaluated cellular phenotypes. Surface plasmon resonance (SPR) and cellular thermal shift assay (CETSA) confirmed direct AB23-a-ANXA1 binding.
RESULTS
The present study demonstrates that AB23-a alleviates LPS-induced inflammation (e.g., IL-6 fell from 3460.89 ± 150.76 pg/ml to 320.00 ± 106.82 pg/ml in LPS-stimulated RAW264.7 cells), and suppresses M1 macrophage polarization. It reduces CD86⁺ macrophage infiltration in lung tissue and improves mouse respiratory function (lung injury score: 0.98 ± 0.04 down to 0.32 ± 0.12; peak expiratory flow (PEF): 1.97 ± 0.20 ml/s up to 5.48 ± 0.25 ml/s). Mechanistically, SPR assay (KD = 1.8 × 10-5 M) and follow-up assays revealed that AB23-a bound directly to ANXA1 and inhibited excessive activation of the MAPK and NF-κB pathways.
CONCLUSION
These findings demonstrate the preventive effects of AB23-a in LPS-induced ALI models and identify ANXA1 as an important functional mediator.
Mass spectrometry (MS) is central to molecular discovery, yet the interpretation of tandem mass spectra (MS/MS) remains limited by database dependence and an incomplete structural resolution. Here, we explore whether large language models (LLMs) can directly translate MS/MS spectra to chemically meaningful molecular descriptions. We introduce MS2LLM, a framework that represents spectra and molecular structures as natural languages and learns their correspondence through instruction tuning. MS2LLM generates hierarchical molecular descriptions, including functional groups, substructures, and chemical classes, directly from the spectral input. Across multiple datasets, it outperforms general-purpose LLMs and conventional spectral learning methods in both descriptive accuracy and chemical classification. Importantly, the model produces interpretable outputs that capture structural semantics rather than exact structures, offering a complementary paradigm for structural inference of unknowns.