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. 2025 Dec 10;16:1716911. doi: 10.3389/fphar.2025.1716911

Serum metabolomics identifies novel prognostic biomarkers in amanita poisoning

Dan Zhu 1,†, Jie Zhong 2,†, Yarong Liu 3, Sicheng Zhang 1,3, Lianhong Zou 1,3,*
PMCID: PMC12728034  PMID: 41451375

Abstract

Background

Amanita poisoning causes 90%–95% of global mushroom-related deaths, yet early prognostic biomarkers for Amanita poisoning are lacking.

Methods

33 patients with Amanita poisoning were recruited and categorized into survival and death group. Multivariate logistic regression analysis was used to investigate the independent mortality risk factors for Amanita poisoning patients. Untargeted serum metabolomics was performed to screen the differentially expressed metabolites. The quality control samples were used to evaluate the stability and reproducibility of ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS) analytical system. The prognosis predictive metabolic biomarkers were identified by ROC curve analysis. Correlations between metabolic biomarkers and biochemical indicators were analyzed by Spearman’s correlation analysis.

Results

Significant differences were observed between the survival and death groups in clinical manifestations—such as gastrointestinal bleeding, dizziness, headache, delirious coma, infection, and shortness of breath—and in biochemical indicators, including alanine transaminase (ALT), aspartate transaminase (AST), prothrombin time and activated partial thromboplastin time (APTT). Metabolomic analysis identified 80 differentially expressed metabolites involved primarily in amino acid and unsaturated fatty acid metabolism. ROC analysis (AUC >0.9) screened nine potential metabolic biomarkers for predicting clinical outcomes: 9,10-Epoxyoctadecenoic acid, Phosphatidylinositol(16:0/18:2 (9Z,12Z)), N-Acetyl-L-aspartic acid, PI(20:3 (5Z,8Z,11Z)/18:0), Propionylcarnitine, Proline betaine, 4′-Methyl-(−)-epigallocatechin 3-(4-methyl-gallate), PG (18:1 (11Z)/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)), and L-Proline. Notably, correlation analysis revealed that 9,10-Epoxyoctadecenoic acid was positively correlated with AST and activated partial thromboplastin time, whereas 4′-Methyl-(−)-epigallocatechin 3-(4-methyl-gallate), N-acetyl-L-aspartic acid, PI(16:0/18:2 (9Z,12Z)), PI(20:3 (5Z,8Z,11Z)/18:0), and Propionylcarnitine showed negative correlations with various liver and coagulation parameters.

Conclusion

Serum metabolomics has identified metabolic biomarkers capable of predicting mortality in Amanita poisoning, with significant correlation to liver and coagulation injury. These biomarkers may facilitate early risk stratification and guide targeted therapeutic interventions. Limitations include small sample size and single-center retrospective design, which may restrict result generalizability.

Keywords: mushroom poisoning, biomarkers, mortality risk, serum metabolomics, amatoxins, UPLC-QTOF-MS/MS

1. Introduction

Mushroom poisoning is a serious public health problem worldwide. It is estimated that 100-200 people die from mushroom poisoning each year in the United States and Europe (Mengs et al., 2012). Investigation through the Foodborne Disease Outbreak Surveillance System from 2010 to 2020 shows that 38676 cases and 788 deaths which were related to mushroom poisoning were reported in China (Li et al., 2021). Amanita phalloides, commonly known as “death cap”, is one of the most toxic mushrooms, and responsible for 90%–95% deaths induced by mushroom poisoning worldwide (Garcia et al., 2015a; Zuker-Herman et al., 2021). Three main group toxins were identified in Amanita phalloides: amatoxins, phallotoxins and virotoxins (Kayes and Ho, 2024). Among them, amatoxins, especially α-amatoxin, were considered as the main lethal toxins and responsible for organ injury in humans (Garcia et al., 2015b; Le Daré et al., 2021). Because of its stable physical structure, amatoxins are highly soluble in water and present a great resistant to heat, cold, acid, alkali and enzymes, making them difficult to eliminate via various processes (cooking, freezing, drying and metabolism) (Wieland et al., 2008). After a good absorption of gastrointestinal tract, amatoxins were primarily accumulated in liver via transporters including organic anion transport polypeptide 3 (OAYP1B3) (Letschert et al., 2006) and Na + -Taurocholate Co-transporting Polypeptide (NTCP) (Gundala et al., 2004) located in the cell membrane of hepatocytes, and eliminated by kidney during the first 72h of intoxication (Jaeger et al., 1993). Moreover, amatoxins could be excreted into bile and reabsorbed through the enterohepatic circulation, which might aggravate the hepatocyte injury (Sun et al., 2018).

The classic toxic mechanism of Amanita poisoning is that amatoxins could reduce the mRNA and protein synthesis via inhibiting RNA polymerase Ⅱ (RNAP Ⅱ) activities and promoting the degradation of RNA Polymerase II Subunit B1 (Rpb1) (Nguyen et al., 1996; Garcia et al., 2014; Xue et al., 2023). Furthermore, apoptosis, autophagy and oxidative stress were involved in the amatoxins induced liver damage (Chen et al., 2020; Gu et al., 2022; Xue et al., 2023). The clinical symptoms and signs of amatoxins intoxication, also known as phalloides toxic syndrome was classically divided into three phase: the gastrointestinal phase, the latency period, and the hepato-renal phase (Karlson-Stiber and Persson, 2003). The treatment for Amanita poisoning mainly includes intensive and supportive therapy, prevention of poison absorption, promotion of amatoxins elimination, use of potential antidotes and liver transplantation (Ye and Liu, 2018; Nieminen and Mustonen, 2020; Xu et al., 2023). Although the clinical management of Amanita poisoning is constantly evolving and improving, the prognosis of Amanita poisoning remains poor.

The reported fatality rates of Amanita poisoning vary widely Several recent studies have shown that the mortality rate of Amanita poisoning still ranged from 4.4% to 16% (Liu J. et al., 2020; De Olano et al., 2021; Tan et al., 2022; Lecot et al., 2023). Identification of the potential risk factors to predict the prognosis at an early stage may improve clinical outcomes of Amanita poisoning patients. A previous study showed that total bilirubin (TB) and APPT were significantly associated with the mortality of patients with wild mushroom induced acute liver injury (Kim et al., 2017). In addition, it is reported that the peak value of ALT, AST, international normalized ratio (INR), and total serum bilirubin (TSB) are more elevated in Amanita poisoning patients with fatal outcomes (Tan et al., 2022; Zhang S. et al., 2024). High INR (>3.61) and plasma ammonia (>95.1 μmol/L) were predictors of the poor outcome of Amanita phalloides poisoning (Ye et al., 2021). Biochemical factors such as the increase of AST, ALT, lactate dehydrogenase (LDH), TB, PT, INR, and APTT levels are associated with poor outcome in the Amanita-containing mushroom poisoning (Trabulus and Altiparmak, 2011). Although several studies have shown that biochemical indicators such as ALT, AST, TSB, TB, LDH, PT, APTT, INR and plasma ammonia can be used to predict the risk of death from Amanita phalloides toxin poisoning, the potential of current biochemical indicators for early prediction of the mortality of patients with Amanita phalloides toxin poisoning remains unknown. And the risk factors for predicting the mortality of Amanita poisoning is still limited.

Metabolomics has successfully utilized in biomarker screening, disease early diagnosis and characterization of biological pathways. Previous research has applied metabolomics to explore the early diagnosis of patients with amatoxin poisoning (Liu et al., 2023) and to investigate amatoxin - induced liver injury mechanisms (Zheng et al., 2023). However, no metabolomic studies have identified prognostic biomarkers for Amanita poisoning outcomes. Herein, we enrolled thirty-three patients with Amanita poisoning. Twenty-seven of them survived after the treatment, but six died. By comparing the biochemical indicators and serum metabolites of thirty-three patients with Amanita poisoning, new biomarkers were identified that could predict the prognosis of patients with Amanita poisoning. These biomarkers may contribute to predict disease course and outcomes in patients.

2. Materials and methods

2.1. Subjects

The study was a retrospective cohort study that examined thirty-three cases of poisoning due to the consumption of Amanita phalloides. These cases were admitted to the Department of Emergency Medicine, Hunan Provincial People’s Hospital (the First Affiliated Hospital of Hunan Normal University) in Changsha, China, from July 2017 to October 2020. The diagnosis of Amanita phalloides poisoning is most commonly made by a trained mycologist upon examining the mushroom itself or photographs provided. Alternatively, for a subset of cases, laboratory confirmation is achieved by detecting amatoxins via LC-MS/MS in patient samples such as vomitus, blood, urine or the suspected food items. Patients who experienced food poisoning from sources other than Amanita phalloides, as well as those with cancers, autoimmune disorders, severe infections, trauma, recent surgeries, or kidney and liver diseases were excluded from the analysis. The follow-up time was calculated from the first day of hospitalization to the date of discharge from hospital or death within 30 days. Based on the clinical outcomes, Amanita poisoning patients were divided into the survival group and mortality group. The study was performed in accordance with the Declaration of Helsinki (revised in Fortaleza, Brazil, 2013) and approved by the Medical Ethics Committee of the Hunan Provincial People’s Hospital (IRB Approval No.: [2024]-10).

2.2. The treatment for amanita poisoning

In the present study, all Amanita poisoning patients received the same treatment regimen. Fluid resuscitation and intensive supportive therapy were performed. Activated charcoals were used to minimize the absorption of amatoxins. Hemoperfusion, hemodialysis or plasmapheresis were performed to eliminate the absorbed amatoxins. Drugs such as acetylcysteine, penicillin G and silymarin were used to resist and reduce the toxicity of amatoxins.

2.3. Serum samples collection

The blood samples of Amanita poisoning patient were all collected in EDTA-containing tubes for serum isolation on the first day of admission to the emergency ward, within 24 h after Amanita poisoning occurred. The samples were allowed to stand at room temperature for 30 min, then centrifuged at 2,500 rpm for 10 min at 4 °C (Liu W. et al., 2020). The supernatant was then collected and stored at −80 °C for subsequent analysis. To evaluate the freeze-thaw and long-term storage stability of serum samples, the protein content in the samples was regularly and randomly sampled and tested.

2.4. Untargeted UPLC-MS/MS metabolomics

The serum samples were gently thawed at 4 °C. A 100 μL aliquot of each sample was mixed with 20 μL of an internal standard [L-2-chlorophenylalanine (Merck, Germany) 0.3 mg/mL, dissolved in methanol (Purity: ≥99.9% (GC), HPLC grade, Merck, Germany). The resulting mixture was vortexed for 10 s. Subsequently, 300 μL of a precooled methanol/acetonitrile (Purity:≥99.9% (GC), HPLC grade, Merck, Germany) mixture (v/v, 2:1) was added, and the solution was vortexed for 1 min. This was followed by ultrasonic extraction (10 min) in an ice water bath, static (−20 °C, 30 min), centrifuge (13000 rpm, 4 °C, 10 min), 300 μL supernatant was collected (Liu W. et al., 2020). Finally, 150 μL of the supernatant was transferred into an LC-MS injection vial with a leg liner. To avoid the batch effect, the supernatant samples were randomly placed on the sample loading platform of UPLC for analysis. A quality control sample (QC) was prepared by combining the extraction liquids from all samples in equal volumes, ensuring that the volume of the QC matched that of the individual samples. To test the stability and repeatability of the UPLC-QTOF-MS/MS, one QC sample was inserted for every five samples.

UPLC-QTOF-MS/MS analysis was performed on Ultimate 3000 LC system (Thermo Fisher Scientific, United States) and ESI-QTOF-MSMS (Impact II™, Bruker, Germany). Chromatographic separations were performed at 40 °C on an Acclaim TMRSLC120-C18 column (2.1 mm × 100 mm, 2.2 μm, Thermo Fisher Scientific,United States). The mobile phases consisted of phase A, which was a 0.1% ammonium formate aqueous solution (containing 2 mmol/L ammonium formate, prepared with pure water, Agilent, United States), and phase B, which was acetonitrile (Purity:≥99.9% (GC), HPLC grade, Agilent, United States). The flow rate was maintained at a constant 0.2 mL/min, and the injection volume was 10 μL. The gradient elution conditions were as follows: 0–2 min, 2% B; 2–12 min, 50% B; 12–20 min, 90% B; 20–30 min, 90% B; and 30–60 min, 2% B (Liu W. et al., 2020).

The mass spectrometer was operated in both positive and negative electrospray ionization (ESI) modes. The specific instrument parameters were as follows: the capillary voltage was set to 4.5 kV in positive mode and 3.5 kV in negative mode; the dry gas flow was maintained at 8 L/min, and the gas temperature was 200 °C. The nebulizer pressure was established at 2.0 bar, the fragmentor voltage was set to 500 V, and the scaning mode of Impact II™ QTOF mass spectrometer was full scan (20–1,000 m/z).

2.5. Data processing and statistical analysis of serum metabolomics

The serum metabolomics data of the survival group and the death group detected by the instrument were simply analyzed using Metaboscape 3.0 software. After noise reduction, peak detection, extraction, alignment and normalization processing in sequence, the detection data were exported for subsequent analysis. The serum metabolites were identified by comparing their molecular weights, fragment patterns and structural information, to the spectral data of metabolites with the same m/z in the standard database of Bruker Company, and freely available human metabolome database (HMDB, https://hmdb.ca/). The information required to be included in the data exported from Metaboscape 3.0 software was: compound name, chemical formula, molecular weight, retention time (RT), as well as sample name and grouping.

Metabolites were identified by comparing their molecular weights, fragment patterns and structural information, to the spectral data of metabolites with the same m/z in the standard database of Bruker Company, and freely available human metabolome database. Because this was an untargeted acquisition, MS/MS spectra were collected in a data-dependent mode; consequently, features were annotated solely by accurate-mass MS1 and retention-time matching to HMDB/Bruker libraries, without confirmatory fragment ions.

The metabolomics data preprocessed by Metaboscape 3.0 software were imported into the software MetaboAnalyst 6.0 for principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to achieve an in-depth analysis of the overall distribution of the two groups of serum samples, the metabolic profile characteristics and overall differences of the serum metabolites in the survival group and the death group of Amanita-containing mushroom poisoning. According to the OPLS-DA analysis, the variable importance in projection (VIP) value was obtained to represent the contribution value of the different expression metabolites in the comparison between groups.

The metabolomics data were preprocessed using MetaboAnalyst 6.0 software, and then t-test, Fold Change analysis, volcano plot and heat map were performed. Serum different expression metabolites in the survival group and the death group of Amanita poisoning patients were screened out with the criteria that the variable VIP value was greater than 1, the fold change (FC) was greater than 2 or less than 0.5, and the adjusted P value was less than 0.05. Then, metabolic pathway analysis was carried out using MetaboAnalyst 6.0 software, and metabolic pathways with significant interference were screened out with the criteria that the impact value (Impact) was greater than 0.1 and -lg(p) was greater than 0.5. Finally, ROC curve analysis was performed on the serum different expression metabolites in the significantly interfered metabolic pathways, and possible biomarkers were screened out with the criterion that ROC was greater than 0.9. The MetaboAnalystR 4.0 platform (https://dev.metaboanalyst.ca/docs/RTutorial.xhtml) provides full details and documentation for R-package.

2.6. Statistical analysis

Continuous variables were presented with mean and standard deviation (SD) and compared with Student’s t-test. Categorical variables were expressed as count and percent quantification, which were compaired with the chi-square test or Fisher’s exact test. The non-parametric Mann-Whitney U test was applied to further confirm the differences between survival and death groups. The Shapiro-Wilk test was used for the verification of normality of data distribution. Spearman’s correlation analysis was conducted to explore the correlation between the levels of metabolic biomarkers and biochemical indicators. P value <0.05 was considered as statistical significance. The data were analyzed using the statistical software package SPSS 25.0.

3. Result

3.1. The clinical characteristics of patients with amanita poisoning

A total of thirty-three Amanita poisoning patients, who were admitted to Hunan Provincial People’s Hospital (the first Affiliated Hospital of Hunan Normal University) between July 2017 and October 2020, were divided into survival and death groups based on the clinical outcomes. Both the t-test (Table 1) and non-parametric tests (Supplementary Table S1) yielded consistent results, revealing statistically significant differences in biochemical indices—including ALT, AST, PT, and APTT—between the survival and non-survival group. Specially, the levels of ALT, AST, PT and APTT in the death group were 4.97, 7.44, 1.84, 1.78 times higher more than those in the survival group respectively, and effect size analysis (Cohen’s d) further validated their clinical importance (Table. 1). In addition, there were significant differences between the survival and death groups of patients with Amanita poisoning in terms of clinical features including gastrointestinal bleeding, dizziness and headache, delirious coma, infection, shortness of breath (Table 2). Effect-size analyses revealed very large odds ratios for the five clinical features that distinguished fatal from non-fatal cases (Table 2; Supplementary Table S2), underscoring the clinical gravity of these manifestations beyond statistical significance. Collectively, the above results indicated that the death patients with Amanita poisoning had more serious liver and coagulation function injury.

TABLE 1.

Biochemical indicators of patients with Amanita poisoning.

Characteristic Total (N = 33) Survival (n = 27) Death (n = 6) t/t’ P-Value Effect size (Cohen’s d)
WBC (×109/L) 9.22 ± 0.57 9.42 ± 3.34 8.29 ± 3.19 0.755 0.456
HGB (g/L) 168.52 ± 33.01 177.70 ± 208.85 127.17 ± 28.92 0.584 0.563
PLT (×109/L) 179.88 ± 14.26 184.59 ± 71.34 158.67 ± 125.89 0.695 0.492
NEUT (%) 77.89 ± 14.18 76.41 ± 14.96 84.55 ± 7.57 −1.285 0.208
ALT (U/L) 644.38 ± 1,210.45 374.31 ± 693.56 1859.70 ± 2,170.16 −3.052 0.005 1.23 (very large)
AST (U/L) 584.55 ± 1,200.87 269.13 ± 431.35 2003.96 ± 2,303.67 −3.821 0.001 1.44 (very large)
TB (μmol/L) 40.46 ± 69,34 38.10 ± 75.50 51.08 ± 30.99 −0.409 0.685
ALB (g/L) 51.40 ± 58.18 54.07 ± 64.12 39.37 ± 8.24 0.554 0.584
UA (μmol/L) 359.93 ± 157.64 369.49 ± 150.80 316.93 ± 195.15 0.733 0.469
Cr (μmol/L) 213.46 ± 263.58 212.29 ± 275.41 218.69 ± 223.99 −0.053 0.958
BUN (mg/dL) 10.43 ± 6.57 9.85 ± 6.25 13.03 ± 7.92 −1.076 0.290
MYO (ng/mL) 216.50 ± 246.38 189.13 ± 251.53 339.65 ± 193.03 −1.372 0.180
CK (U/L) 289.23 ± 599.84 308.24 ± 660.42 203.65 ± 154.97 0.381 0.706
CK-MB (ng/mL) 46.94 ± 72.37 46.66 ± 78.80 48.67 ± 35.07 −0.064 0.950
PT (s) 12.65 ± 5.34 10.96 ± 2.39 20.22 ± 8.28 −5.150 0.000 1.73 (very large)
APTT (s) 31.19 ± 12.36 27.29 ± 6.07 48.71 ± 18.32 −5.147 0.000 0.99 (large)
GLU (mmol/L) 6.71 ± 2.65 6.81 ± 2.18 6.22 ± 4.43 0.493 0.625
LDL (mmol/L) 2.35 ± 1.05 2.46 ± 1.01 1.79 ± 1.13 1.448 0.158
HDL (mmol/L) 1.31 ± 0.38 1.35 ± 0.34 1.12 ± 0.54 1.338 0.190
TCHOL (mmol/L) 4.21 ± 1.18 4.35 ± 1.10 3.52 ± 1.35 1.623 0.115
TG (mmol/L) 1.22 ± 0.69 1.29 ± 0.61 0.89 ± 0.97 1.310 0.200
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Cohen’s d = (meandeath–meanSurvivor)/pooled SD., Thresholds: d ≥ 0.8 = large effect; d ≥ 1.0 = Very large effect. WBC, white blood cells; HGB, hemoglobin; PLT, platelets; NEUT%, the percentage of neutrophile granulocytes; ALT, alanine transaminase; AST, aspartate transaminase; TB, total bilirubin; ALB, albumin; UA, uric acid; Cr, creatinine; BUN, blood urea nitrogen; MYO, myoglobin; CK, creatine kinase; CK-MB, creatine kinase; MB, form; PT, prothrombin time; APTT, activated partial thromboplastin time; GLU, glucose; LDL, low density lipoprotein; HDL, high density lipoprotein; TCHOL, total cholesterol; TG, triglyceride.

TABLE 2.

Clinical data of Amanita poisoning patients.

Characteristic Total (N = 33) Survival (n = 27) Death (n = 6) X 2 P-value OR (95% CI)
Gender
Male [N (%)] 18 (54.5) 15 (55.56) 3 (42.86) 0.002 0.996
Female [N (%)] 15 (45.45) 12 (44.44) 3 (57.14)
Age (years) 56.67 ± 1.84 56.30 ± 11.19 58.33 ± 7.92 −0.421 0.677
Latency period (h)
<6 5 (15.15) 4 (14.81) 1 (16.67) −0.113 0.910
6–24 28 (84.85) 23 (85.19) 5 (83.33)
Initial treatment time(h) 7.99 ± 1.02 7.39 ± 5.49 10.67 ± 7.31 −1.246 0.222
Length of hospitalization (d) 7.47 ± 4.66 7.49 ± 4.28 5.5 ± 4.35 0.909 0.370
Disease history [N (%)]
Diabetes [N (%)] 2 (6.06) 1 (3.70) 1 (16.67) 1.125 0.289
Hypertension [N (%)] 7 (21.21) 5 (18.52) 2 (33.33) 0.349 0.554
Coronary heart disease 1 (3.03) 1 (3.70) 0 (0.00) 0.270 0.603
Personal history
Smoking [N (%)] 6 (18.18) 5 (18.52) 1 (16.67) 0.075 0.785
Drinking [N (%)] 3 (9.09) 3 (11.11) 0 (0.00) 0.873 0.350
Gastrointestinal symptoms
Abdominal pain [N (%)] 24 (72.73) 18 (66.67) 6 (100.00) 1.575 0.209
Nausea and vomiting [N (%)] 23 (69.70) 18 (66.67) 5 (83.33) 0.075 0.785
Gastrointestinal hemorrhage [N (%)] 7 (21.21) 3 (11.11) 4 (66.67) 7.472 0.006 12.6 (1.87–85.01)
Jaundice [N (%)] 11 (33.33) 7 (25.93) 4 (66.67) 2.653 0.103
Oliguria and anurohematuria [N (%)] 8 (24.24) 7 (25.93) 1 (16.67) 0.452 0.502
Palpitations and chest pain [N (%)] 2 (6.06) 2 (7.41) 0 (0.00) 0.560 0.454
Dizziness and headache [N (%)] 7 (21.21) 2 (7.41) 5 (83.33) 14.477 0.000 19.15 (2.42–151.45)
Delirious coma [N (%)] 3 (9.09) 0 (0.00) 3 (50.00) 12.827 0.000 55 (2.32–1,303.06)
Infection [N (%)] 9 (27.27) 4 (14.81) 5 (83.33) 9.668 0.002 19.15 (2.42–151.45)
Shortness of breath [N (%)] 6 (18.18) 2 (7.41) 4 (66.67) 9.746 0.002 31.8 (3.33–303.99)
Poor appetite [N (%)] 28 (84.85) 22 (81.48) 6 (100.00) 0.270 0.603
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OR, odds ratio; CI, confidence interval.

3.2. Serum metabolite profiles of the survival and death groups of amanita poisoning patients

We performed an untargeted metabolomics analysis using UPLC-QTOF-MS/MS to assess the metabolic differences between the survival and death groups of Amanita poisoning patients. The metabolomics data were then analyzed by PCA using MetaboAnalyst 6.0. The position of each sample represents a metabolic profile map (Figures 1A,B). In both positive and negative ESI modes, the serum samples from the survival and death groups were more centrally distributed on the PCA score plot, indicating consistency in the metabolic profiles within the groups. There was a significant separation of the distributions between the two groups, suggesting a significant difference in the metabolic profiles between the survival and death groups of Amanita poisoning patients in the positive and negative ESI modes. The sample location of the QC was centralized, indicating stable instrument operation and credible data (Figures 1A,B). PLS-DA analysis showed that serum samples from both the survival and death groups were distributed centrally on the PLS-DA score plots in both positive and negative ESI modes, indicating significant differences between the groups (Figures 1C,D). The metabolomic data of the survival and death groups of Amanita poisoning patient were further remodeled using supervised OPLS-DA to obtain the value of the differential authority contribution (VIP value). In both positive and negative ESI models, serum samples from the survival and death groups were differentially distributed on the OPLS-DA score plots (Figures 1E,F). To test the OPLS-DA degree of fitting, the Permutation Test was performed. The explanatory rate (R2Y) was 0.915 and 0.958 in positive and negative ESI models respectively, and the predictive rate (Q2) were 0.746 and 0.775 in positive and negative ESI models respectively, which were greater than 0.5, suggesting that the OPLS-DA model was well fitted and the results of this study were credible (Supplementary Figures 1A,B). The raw serum metabolomics data for patients with Amanita poisoning are provided in Supplementary Data sheet 1, 2.

FIGURE 1.

Six panels labeled A to F, each displaying a scores plot. Plots A and B show three categories: death (pink), QC (green), and survival (blue for A, green for B). Plots C to F show two categories: death (pink) and survival (green). Each plot depicts clusters of data points with overlapping shaded areas representing the categories. Axes are labeled with percentages indicating variance explained by principal components or scores. Plots A, B, E, and F use T or PC scores, while C and D use component axes.

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Serum metabolic profile analysis of survival and death groups of patients with Amanita poisoning. Plots of PCA (A), PLS-DA (C) and OPLS-DA (E) scores in the positive ESI mode; Plots of PCA (B), PLS-DA (D) and OPLS-DA (F) scores in the negative ESI mode.

3.3. Screening of serum different expression metabolites in the survival and death groups of amanita poisoning patients

To investigate the different expression metabolites, the Volcanos were analyzed using MetaboAnalyst 6.0 software (Figures 2A,B). 61 and 19 serum different expression metabolites in the positive and negative ESI modes respectively were screened according to the criteria of VIP >1, FC > 2 or <0.5, and P. adjusted <0.05 (Supplementary Table 3). Table 3 presents the list of the top 20 differential metabolites based on VIP scores. Subsequently, the significant different expression metabolites were clustered and analyzed in heatmaps, as shown in Figures 2C,D. According to the enrichment analysis, these different expression metabolites were mainly associated with a variety of amino acid metabolism and unsaturated fatty acid metabolism, including vitamin B6 metabolism, phenylalanine metabolism, ether lipid metabolism, tryptophan metabolism, starch and sucrose metabolism, One carbon pool by folate, pentose and glucuronide interconversions and cysteine and methionine metabolism (Figure 2E).

FIGURE 2.

Five panels showing data visualizations related to metabolic pathways. Panel A and B include scatter plots comparing log2 fold change and negative log10 p-values with various metabolites labeled. Panels C and D display heatmaps illustrating the expression levels of metabolites across different samples, with a color gradient from blue to red indicating low to high levels. Panel E shows a bubble plot with pathway impact on the x-axis and negative log10 p-value on the y-axis, highlighting pathways like Vitamin B6 metabolism and Tryptophan metabolism.

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Different expression metabolites analysis and pathways enrichment of surviving and dead patients with Amanita poisoning. Volcano plot in the positive (A) and negative (B) ESI models. Hierarchical clustering heatmaps of the different expression metabolites in the positive (C) and negative (D) ESI models. Pathway analysis of different expression metabolites between survival and death groups of patients with Amanita poisoning (E).

TABLE 3.

Different expression metabolites between survival and death patients with Amanita poisoning.

Metabolites RT MW Formula FC Change VIP P Value p.Ajusted ESI
PI(16:0/20:2 (11Z,14Z)) 11.82 432.26974 C45H83O13P 0.32 ↓ 2.14 <0.0001 <0.0001 ESI+
Pyridoxamine 1.05 169.09487 C8H12N2O2 0.27 ↓ 2.12 <0.0001 <0.0001 ESI+
PI(16:0/18:2 (9Z,12Z)) 10.94 418.2538 C43H79O13P 0.35 ↓ 2.07 <0.0001 <0.0001 ESI+
PS(18:1 (9Z)/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)) 8.59 417.75543 C46H76NO10P 0.29 ↓ 2.01 <0.0001 <0.0001 ESI+
Phytonadione 15.76 473.34452 C31H46O2 0.33 ↓ 1.95 <0.0001 <0.0001 ESI+
N-Acetyl-L-aspartic acid 1.18 176.04008 C6H9NO5 0.48 ↓ 1.92 <0.0001 <0.0001 ESI+
L-pipecolic acid 1.02 130.08611 C6H11NO2 0.13 ↓ 1.89 <0.0001 <0.0001 ESI+
PI(20:3 (5Z,8Z,11Z)/18:0) 8.75 445.2881 C47H85O13P 0.21 ↓ 1.89 <0.0001 <0.0001 ESI+
Phosphorylcholine 6.37 185.12831 C5H15NO4P 0.50 ↓ 1.84 <0.0001 <0.0001 ESI+
PG (18:2 (9Z,12Z)/18:2 (9Z,12Z)) 9.83 771.47118 C42H75O10P 0.59 ↓ 1.81 <0.0001 <0.0001 ESI+
PG (18:1 (11Z)/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)) 10.54 411.24588 C46H77O10P 0.39 ↓ 1.79 <0.0001 <0.0001 ESI+
trans-Aconitic acid 1.26 175.03433 C6H6O6 17.49 ↑ 1.79 <0.0001 <0.0001 ESI+
Enkephalin L 13.46 554.29384 C28H37N5O7 0.40 ↓ 2.37 <0.0001 0.00060251 ESI-
LysoPC(22:6 (4Z,7Z,10Z,13Z,16Z,19Z)) 14.97 566.32221 C30H50NO7P 0.39 ↓ 2.22 <0.0001 0.0010423 ESI-
LysoPC(20:5 (5Z,8Z,11Z,14Z,17Z)) 14.33 540.31545 C28H48NO7P 0.36 ↓ 2.17 <0.0001 0.0010423 ESI-
PE (P-16:0/16:0) 14.17 436.26744 C21H44NO6P 0.35 ↓ 2.00 0.000155 0.0048232 ESI-
Glycocholic acid 15.68 464.29968 C26H43NO6 0.32 ↓ 1.95 0.000178 0.0048232 ESI-
11-Beta-hydroxyandrosterone-3-glucuronide 8.8 481.26211 C25H38O9 0.28 ↓ 1.83 0.000199 0.021626 ESI-
Perfluorooctanesulfonic acid 9.62 498.91147 C8HF17O3S 0.40 ↓ 1.81 0.00246 0.023609 ESI-
Pi-methylimidazoleacetic acid 1.08 138.86257 C6H8N2O2 0.58 ↓ 1.77 0.00113 0.018417 ESI-
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3.4. Screening the metabolic biomarkers for predicting the mortality risk of patients with amanita poisoning

To identify the potential metabolic biomarkers for mortality risk prediction of Amanita poisoning patients, the serum different expression metabolites between the survival and death groups of Amanita poisoning patients were imported into MetaboAnalyst 6.0 software for ROC curve analysis. Based on the criterion of AUC >0.9, a total of 9 potential biomarkers including 9,10-Epoxyoctadecenoic acid (Figure 3A), PI(16:0/18:2 (9Z,12Z)) (Figure 3B), N-Acetyl-L-aspartic acid (Figure 3C), PI(20:3 (5Z,8Z,11Z)/18:0) (Figure 3D), Propionylcarnitine (Figure 3E), Proline betaine (Figure 3F), 4′-Methyl-(−)-epigallocatechin 3-(4-methyl-gallate) (Figure 3G), PG (18:1 (11Z)/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)) (Figure 3H) and L-Proline (Figure 3I) were screened to predict the clinical outcomes of Amanita poisoning patients. The normalized peak intensities of the selected metabolic biomarkers are presented in Figure 3. The optimal cutoff values, determined by the red lines, were used to evaluate the predictive accuracy of these metabolic markers.

FIGURE 3.

Nine panels labeled A to I, each displaying a ROC curve and a box plot. ROC curves measure true versus false positive rates with AUC values noted. Box plots compare 'Death' and 'Survival' groups for different compounds, highlighting distribution differences with color-coded boxes. Specific compounds include 9,10-Epoxyoctadecenoic acid, N-Acetyl-L-aspartic acid, and others.

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Biomarker screening in survivors and dead patients with Amanita poisoning. 9,10-Epoxyoctadecenoic acid (A), PI(16:0/18:2 (9Z,12Z)) (B), N-Acetyl-L-aspartic acid (C), PI(20:3 (5Z,8Z,11Z)/18:0) (D), Propionylcarnitine (E), Proline betaine (F), 4′-Methyl-(−)-epigallocatechin 3-(4-methyl-gallate) (G), PG (18:1 (11Z)/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)) (H) and L-Proline (I).

3.5. Correlation analysis between potential metabolic markers and biochemical indicators of liver and coagulation

To explore the relationship between metabolic biomarkers and serum biochemical indicators of coagulation and liver function in the survival and death groups of patients with Amanita poisoning, correlation analysis was performed. The correlation heat map of potential metabolic markers related to the biochemical indicators of liver and coagulation is shown in Figure 4A. Interestingly, 9,10-Epoxyoctadecenoic acid was positively correlated with AST (Pearson’s r = 0.555, P < 0.001) and APTT (Pearson’s r = 0.616, P < 0.001). 4′-Methyl-(−)-epigallocatechin 3-(4-methyl-gallate) was negatively correlated with AST (Pearson’s r = −0.561, P < 0.001) and APTT (Pearson’s r = −0.583, P < 0.001). N-acetyl-L-aspartic acid was negatively correlated with AST (Pearson’s r = −0.632, P < 0.001), ALT (Pearson’s r = −0.580, P < 0.001), and PT (Pearson’s r = −0.593, P < 0.001). PI(16:0/18:2 (9Z,12Z)) was negatively correlated with AST (Pearson’s r = −0.540, P < 0.001), PT (Pearson’s r = −0.648, P < 0.001), and APPT (Pearson’s r = −0.567, P < 0.001). PI(20:3 (5Z,8Z,11Z)/18:0) was negatively correlated with APPT (Pearson’s r = −0.578, P < 0.001). Propionylcarnitine was negatively correlated with AST (Pearson’s r = −0.555, P < 0.001). Taken together, these results suggest that the metabolic biomarkers are related to the serum hepatic injury and coagulation dysfunction indicators in Amanita poisoning patients (Figure 4B).

FIGURE 4.

Panel A shows a correlation matrix with colored circles indicating correlation strength and direction between various biochemical markers. Darker red signifies stronger positive correlations, while blue indicates negative. Panel B consists of twelve scatter plots displaying linear relationships between different biochemical pairs, with correlation coefficients (R) and significance levels (P-values) indicated, showing varying degrees of negative and positive correlations.

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Correlation analysis between potential metabolic markers and biochemical indicators in survival and death groups of Amanita poisoning patients. (A) The heatmap of correlation coefficients among metabolic biomarkers associated with blood biochemical indicators of liver and coagulation function. The color was corresponded to correlation coefficients with the red representing the positive correlations, and the blue representing the negative correlations. The strength of correlation was represented visually by both numerical percents and circle size. (B) Pearson correlations between metabolic markers (N-Acetyl-L-aspartic acid, 9,10-Epoxyoctadecenoic acid, PI(16:0/18:2 (9Z,12Z)), Propionylcarnitine, 4′-Methyl-(−)-epigallocatechin 3-(4-methyl-gallate), PI(20:3 (5Z,8Z,11Z)/18:0)) and serum biochemical indicators of liver and coagulation function (ALT, AST, PT and APPT).

4. Discussion

The entry of amatoxins into hepatocytes, mediated by the hepatic transporters OATP1B3 and NTCP, initiates lethal liver injury (Wang et al., 2023; Gong et al., 2024). Consequently, the pharmacological inhibition of these transporters presents a viable therapeutic approach for attenuating amatoxin-induced hepatotoxicity (Wang et al., 2023; Xue et al., 2025). This study has obtained a series of results with significant clinical significance through a comprehensive analysis of patients with Amanita poisoning from multiple perspectives. Our findings showed that there were significant differences between the survival and death groups of Amanita poisoning patients in terms of the clinical features (gastrointestinal bleeding, dizziness and headache, delirious coma, infection, and shortness of breath) and the biochemical indicators (ALT, AST, PT and APTT), suggesting that the death patients with Amanita poisoning had more serious liver and coagulation function damage, which were consistent with numerous previous research findings (Trabulus and Altiparmak, 2011; Le Daré et al., 2021). YZ Ye et al. identified the high international normalized ratio (INR) (>3.6) and plasma ammonia (>95.1 μmol/L) as predictive biomarkers of poor prognosis for Amanita poisoning patients (Ye et al., 2021). The previous study showed that hepatic encephalopathy (HE), upper gastrointestinal bleeding (UGB), TB concentration, indirect/direct bilirubin ratio, AST, PT, and APTT were significantly associated with the mortality risk factors of Amanita poisoning patients (Liu et al., 2023), which was consistent with our study.

Metabolomics analysis provides a novel perspective for revealing the pathophysiological mechanism of Amanita poisoning (Liu W. et al., 2020; Liu et al., 2023). In both positive and negative ESI mode, PCA, PLS-DA, and OPLS-DA analyses all demonstrated significant differences in the metabolic characteristics of serum samples between the survival and death groups. A total of 120 serum different expression metabolites were identified, primarily involving multiple amino acid metabolism and unsaturated fatty acid metabolism-related pathways. Vitamin B6 metabolism is the process the body uses to break down and utilize vitamin B6. The primary forms of vitamin B6 in the body include pyridoxal (PL), pyridoxal phosphate (PLP), pyridoxine (PN) and pyridoxamine (PM), as well as their 5′-phosphate esters, with pyridoxal 5′-phosphate (PLP) being the most important and active form. The metabolism of vitamin B6 begins with its absorption in the small intestine. After absorption, vitamin B6 is transported to the liver, where it is converted into its active form, PLP. PLP then acts as a coenzyme for various enzymes involved in amino acid, carbohydrate and lipid metabolism, neurotransmitter synthesis and heme production (Stach et al., 2021). In the present study, Pyridoxamine 5′-phosphate, Pyridoxal and Pyridoxamine were downregulated in the death group of Amanita poisoning. When the dysfunction of liver induced by Amanita poisoning decreased the conversion of active form Pyridoxamine 5′-phosphate, Pyridoxal and Pyridoxamine from vitamin B6. Therefore, the liver injury caused by Amanita poisoning can cause abnormal vitamin B6 metabolism. In addition, the abnormal metabolism of taurine and hypotaurine may interfere with the osmotic pressure regulation and antioxidant function of cells, exacerbating cell damage (Marcinkiewicz and Kontny, 2014); the change in cysteine and methionine metabolism may affect the synthesis of glutathione, weakening the antioxidant defense ability of the body (Martínez et al., 2017). Several studies have indicated a connection between microbial tryptophan metabolism and liver injury (Chen et al., 2025; Ding et al., 2025; Tu et al., 2025). Furthermore, a serum metabolomics study conducted on an α-Amanitin-induced liver injury animal model demonstrated the involvement of the tryptophan metabolism pathway in the hepatotoxic process (Zheng et al., 2023), which aligns with our findings. These consistent results suggest that disrupted tryptophan metabolism may be a key mechanism underlying liver injury in amatoxin poisoning and could represent a potential therapeutic target. These abnormal metabolic pathways are interrelated and interact with each other, jointly constituting the complex metabolic network disorder after Amanita poisoning, which may play a key role in the development of the disease.

9,10-Epoxyoctadecenoic acid, PI(16:0/18:2 (9Z,12Z)), N-Acetyl-L-aspartic acid, PI(20:3 (5Z,8Z,11Z)/18:0), Propionylcarnitine, Proline betaine, 4′-Methyl-(−)-epigallocatechin 3-(4-methyl-gallate), PG (18:1 (11Z)/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)) and L-Proline were identified as potential metabolic biomarkers for predicting the mortality risk of Amanita poisoning patients. The correlation analysis between these biomarkers and serum biochemical indicators of liver and coagulation functions provided deeper insights into the poisoning mechanism. PI(16:0/18:2 (9Z,12Z)), and PI(20:3 (5Z,8Z,11Z)/18:0) belong to Phosphatidylinositol (PI), which are one of the components of cell membrane and involved in a variety of intracellular signal transduction processes (Porta and Figlin, 2009). During coagulation, PI provides the phospholipid surface required for the conversion of prothrombin to thrombin, which is essential for the blood coagulation process (Liu and McCoy, 1975). Consistent with this, our results showed that the levels of PI(16:0/18:2 (9Z,12Z)) and PI(20:3 (5Z,8Z,11Z)/18:0) were correlated with the PT and APTT. N-Acetyl-L-aspartic acid (NAA) is an amino acid derivative known to be highly abundant in the central nervous system, which is synthesized form aspartic acid and acetyl-CoA by N-acetyltransferase-8-like (NAT8L) and breaked down into aspartate and acetate by aspartoacylase (ASPA) (Krause and Wegner, 2024). Accumulating independently evidences reveals that reduction of N-Acetyl-L-aspartic acid was correlation with acute and chronic central nervous system injury (Niddam et al., 2018; Shibasaki et al., 2018; Li et al., 2020; Hu et al., 2024) and involved in inflammation (Krause and Wegner, 2024). Additional study showed acute liver failure which associated with the poor outcome always accompanied by complication such as hepatic encephalopathy, infection and coagulopathy (Rovegno et al., 2019). Hepatic encephalopathy and septic shock can also cause a degree of central nervous system injury. In the present study, we revealed that the death patients with Amanita poisoning had more serious liver injury and infection. Moreover, the level of N-Acetyl-L-aspartic acid is decreased in the death group. Furthermore, the correlation analysis showed that NAA negatively associated with the ALT, AST, PT and APTT in the Amanita poisoning patients. Collectively, these results suggested that NAA could serve as a potential indicator of liver damage and coagulopathy, which were associated with the unfavorable prognosis of Amanita poisoning. 9,10-Epoxyoctadecenoic acid (9,10-EOA) is a proliferator-activated receptors (PPAR) gamma2 ligand (Lecka-Czernik et al., 2002). An accumulating body of research indicates that PPARgamma/ligand system plays a critical role in regulation of liver regeneration and involves in non-alcoholic fatty liver disease (Yamamoto et al., 2008; Cheng et al., 2018; Zhang L. et al., 2024). In the present study, our results showed that 9,10-Epoxyoctadecenoic acid was upregulated in the death group of Amanita poisoning patients and associated with liver injury. These results indicated that 9,10-Epoxyoctadecenoic acid was involved in Amanita induced liver damage. However, the specific mechanism by which 10-Epoxyoctadecenoic acid is involved in Amanita-induced liver injury requires further experimental confirmation and in-depth exploration. Propionylcarnitine is a derivative of the propionic acid, which is involved in the conversion of fatty acids to energy and thus plays an important role in the energy metabolism. Furthermore, Propionylcarnitine may affect coagulation by improving vascular endothelial function and reducing inflammatory response. Collectively, routine metabolomic screening in suspected cases could expedite hemodialysis or liver transplant decisions.

5. Limitations

This study still has some limitations. Firstly, the sample size of this study is small, which may affect the generalizability of the results. Moreover, small sample size limits statistical power. Future multi-center studies with larger cohorts are needed to validate these biomarkers. Secondly, the study is a single-center retrospective study and selection bias inevitably exists. Further validation of these experimental results should be pursued through future multi-center studies. Thirdly, we lacked data on ingested dose, mushroom dry weight, or precise time-to-treatment—variables known to influence outcome. Diabetes, hypertension and coronary disease were evenly distributed between groups (Table 2) and were therefore unlikely drivers of the metabolic signature, but larger studies should include dose–response curves and adjust for Charlson comorbidity index. In addition, although a series of different expression metabolites and potential metabolic biomarkers have been identified, their exact biological functions and molecular mechanisms in the poisoning process have not been thoroughly studied. Further basic experimental research is needed to clarify these aspects. Furthermore, non-targeted metabolomics may miss low-abundance metabolites, and some metabolite identifications relied solely on the first-order mass spectrometry information without MS/MS fragment ion validation. Thus, some of the identification results need to be validated by targeted analysis.

6. Conclusion

In summary, this study has conducted a relatively comprehensive analysis of clinical features and metabolomics in patients with Amanita poisoning. The death patients with Amanita poisoning had more serious liver and coagulation function injury. 9,10-Epoxyoctadecenoic acid, PI(16:0/18:2 (9Z,12Z)), N-Acetyl-L-aspartic acid, PI(20:3 (5Z,8Z,11Z)/18:0), Propionylcarnitine, Proline betaine, 4′-Methyl-(−)-epigallocatechin 3-(4-methyl-gallate), PG (18:1 (11Z)/22:6 (4Z,7Z,10Z,13Z,16Z,19Z)) and L-Proline were served as potential metabolic biomarkers for predicting the prognosis of Amanita poisoning patients, which were correlated with serum biochemical indicators of liver and coagulation functions. The present study provided new metabolic biomarkers for prognosis prediction and contributed to improve the therapeutic effect of Amanita poisoning.

Funding Statement

The authors declare that financial support was received for the research and/or publication of this article. This study was funded by the Changsha Science and Technology Bureau project, China (No. kq1901057).

Footnotes

Edited by: Jiangxin Wang, Shenzhen University, China

Reviewed by: Zheng Yuan, China Academy of Chinese Medical Sciences, China

Arian Karimi Rouzbahani, Western Health, Australia

Data availability statement

The data presented in the study are deposited in the Metabolights database, accession number MTBLS13397, available at: https://www.ebi.ac.uk/metabolights/editor/MTBLS13397/files?reviewCode=cd1a517d-715b-4a62-929b-7dc9d64cf526.

Ethics statement

The studies involving humans were approved by the Medical Ethics Committee of the Hunan Provincial People’s Hospital (IRB Approval No.: [2024]-10). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

DZ: Data curation, Formal Analysis, Visualization, Writing – original draft. JZ: Data curation, Writing – original draft. YL: Data curation, Formal Analysis, Writing – original draft. SZ: Data curation, Formal Analysis, Writing – original draft. LZ: Project administration, Writing – review and editing.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Generative AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphar.2025.1716911/full#supplementary-material

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Table2.docx (18.5KB, docx)
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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table3.xlsx (20.8KB, xlsx)
Table1.docx (20.8KB, docx)
Image1.tif (129.5KB, tif)
Table2.docx (18.5KB, docx)
DataSheet1.xlsx (56.3KB, xlsx)
DataSheet2.xlsx (50.3KB, xlsx)

Data Availability Statement

The data presented in the study are deposited in the Metabolights database, accession number MTBLS13397, available at: https://www.ebi.ac.uk/metabolights/editor/MTBLS13397/files?reviewCode=cd1a517d-715b-4a62-929b-7dc9d64cf526.

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