Untargeted Metabolomic Analysis Using UPLC-MS/MS Reveals Metabolic Changes Associated With Lanmaoa asiatica Poisoning

Abstract

Lanmaoa asiatica is known for its unique flavor; however, improper consumption can induce severe neuropsychiatric symptoms, including hallucinations and irritability. The underlying toxicity mechanism remains unclear, and the lack of specific antidotes poses a significant threat to patient safety. This study employed ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to analyze the plasma metabolic profiles of patients with Lanmaoa asiatica poisoning and healthy controls. A total of 20 patients were included, with an average age of 36.9 ± 13.08 years. No significant differences were observed in age, gender, or laboratory indicators between the patient and control groups (p > 0.05). Poisoned patients primarily exhibited neuropsychiatric symptoms, including hallucinations (75%) and general weakness (60%), along with gastrointestinal symptoms such as nausea (60%) and vomiting (45%). Metabolomic analysis identified 914 differential metabolites, primarily involving benzene derivatives, organic acids and their derivatives, amino acid metabolites, and heterocyclic compounds. Notably, 5-methoxytryptophan (5-MTP) and protocatechuic acid were significantly upregulated, suggesting potential pharmacological relevance. KEGG pathway analysis revealed disturbances in oxidative phosphorylation and the morphine addiction pathway, implicating mitochondrial dysfunction as a key factor in Lanmaoa asiatica toxicity. Additionally, adenosine monophosphate (AUC = 0.917), adenosine 5'-diphosphate (AUC = 0.935), and adenosine 5'-triphosphate (AUC = 0.895) were identified as potential metabolic biomarkers and therapeutic targets. Despite the overall favorable prognosis and no significant damage to vital organs such as the liver and kidneys, the severe hallucinogenic effects raise concerns about increased risks of self-harm and accidental injury. However, this study has certain limitations, including a relatively small sample size and potential challenges in metabolite identification inherent to untargeted metabolomics. These factors may affect the generalizability and biological interpretation of the findings. Future studies with larger cohorts and integrated, targeted approaches are warranted to validate and refine these results.

Keywords: Lanmaoa asiatica; metabolomics; mushroom poisoning; neuropsychiatric symptoms; oxidative phosphorylation.

FIGURE 1

(A) Lanmaoa asiatica. (B) Experimental design.

FIGURE 2

(A) Total ion chromatogram in positive ion mode. (B) Total ion chromatogram in negative ion mode. (C) The Principal component analysis (PCA) of QC samples in positive ion mode. (D) The Principal component analysis (PCA) of QC samples in negative ion mode.

FIGURE 3

Metabolomics profiling of patient group and healthy control. (A) Principal component analysis (PCA) demonstrates distinct clustering patterns between healthy controls (red circles) and patient groups (green circles). (B) Orthogonal partial least squares‐discriminant analysis (OPLS‐DA) score plot reveals clear separation between healthy controls (red circles) and patient groups (green circles). (C) Differential metabolite distribution plot showing log2‐transformed fold change (log2FC) versus variable importance in projection (VIP) scores. The horizontal axis represents the magnitude of content difference between groups, with color coding indicating metabolite classification. (D) Volcano plot visualization of differential metabolites, highlighting significantly upregulated (red), downregulated (green), and non‐significant metabolites (gray) based on fold change and statistical thresholds. (E) Bar chart quantification of differential metabolite expression patterns (red: Upregulated; green: Downregulated). (F) Pathway enrichment analysis scatter plot displays significantly altered metabolic pathways, where circle size corresponds to the number of annotated metabolites, color intensity reflects p‐value significance (redder hues indicate lower p‐values), and horizontal position indicates enrichment factor magnitude. (G) Clustered heatmap visualization of differential metabolites across samples, with red indicating high content and green indicating low content.

FIGURE 4

Pathway‐specific metabolite analysis. (A) Morphine addiction pathway metabolite map showing statistically significant alterations (p < 0.05) in the patient group versus healthy control. (B) Oxidative phosphorylation pathway metabolite map showing statistically significant alterations (p < 0.05) in the patient group versus healthy control.

FIGURE 5

Differential metabolite ROC curve analysis of morphine addiction and oxidative phosphorylation pathways. ROC curve analysis identifies Adenosine monophosphate (AUC = 0.917), Adenosine 5′‐diphosphate (AUC = 0.935), and Adenosine‐5′‐triphosphate (AUC = 0.895) as high‐precision biomarkers for distinguishing patients from healthy controls.