Mushroom Poisoning Research Articles

Alternatives for mushroom poisoning timely care in Mexico and Central America.

In countries where the consumption of mushrooms is common, hundreds of mushroom poisonings occur every year, which represents a public health problem. In Mexico, mushroom poisoning is classified as a non-bacterial gastrointestinal poisoning, which prevents timely care. To create a free-access platform that synthesizes and standardizes the information on mycetism cases and offers tools for diagnosis and timely treatment. In locations where cases of mycetism have occurred, information was obtained on the fungi involved, the poisonings that occurred, care protocols, and sample processing. Records were generated that synthesize and describe the types of mycetism with the highest probability of occurrence in Mexico. Therein, the biological characteristics of fungi, the symptoms they cause and their treatment are described. A protocol proposal for patient care and for the processing of biological samples is presented. Finally, a form is included to collect information on cases of poisoning. Systematized and analyzed information on mycetism allows to simplify its diagnosis, attention and treatment. The protocols for clinical care and sample processing are the basis for generating strategies that prevent deaths due to mycetism.

Molecular Recognition Mechanism of an Anti-Amatoxins mAb and Its Application in Centrifugal Disk-Based Immunoassay.

Amatoxins are polypeptides that cause 90% of fatalities from accidental ingestion of poisonous mushrooms. Unfortunately, there are no specific antidotes against amatoxins poisoning, hence preparation of high-affinity antibodies, understanding the receptor (amatoxins) and ligand (antibody) mechanism, and establishing a straightforward screening approach are of great significance for confirming poison agents and clinical diagnosis. Here, anti-amatoxins monoclonal antibody (mAb) 9B2 was prepared and the recognition mechanism was investigated. The approach is useful for designing desirable immunogens, developing new antibodies with improved performance, and constructing effective immunoassays. Based on the mAb, we designed a centrifugal disk-like microfluidics chip and developed a fully automated immunoassay capable of detecting amatoxins poisoning in various samples including serum, urine, and mushrooms. The whole detection process could be automatically accomplished within 30 min, with a limit of detection of 0.08 to 0.12 μg/L for real samples, ∼30-fold more sensitive than conventional enzyme-linked immunosorbent assay (ELISA). Our platform not only provided a practical approach for performing poison agent confirmation and clinical diagnosis but also had important implications for improving the survival of patients with mushroom poisoning.

Management and publication of scientific data on traditional mycological and lichenological knowledge in Africa

AbstractAfrica is an important global reservoir for biological, cultural and traditional knowledge about fungi and lichens, which are used as food, medicine and in mythology, among other things. African human populations are undergoing highly significant changes and adaptation processes, which are accompanied by rapid urbanization, meeting with western civilization, high rural migration and the loss of natural ecosystems. Indigenous knowledge is being lost, including that concerning fungi and lichens. Ethnomycology and ethnolichenology provide a diversity of knowledge about beneficial and poisonous fungi and lichens, and give insights into their sociological impact on human behaviour and use. Here we present a working and publishing environment established with the Diversity Workbench software in line with national and international initiatives for FAIR guided provision of research data. The database application called ‘EthnoMycAfrica’ contains published ethnomycological and ethnolichenological information from Africa. The content is created and curated by team partners from Central, East, West, North and Southern Africa. Data entry is performed both online and offline, optionally via a mobile device. Currently, the system with the tools DiversityDescriptions and DiversityNaviKey contains a total of 1350 well-structured and freely and openly accessible data records. EthnoMycAfrica is the first database with a data schema, standard descriptors and data content created mainly by African scholars. The data can be useful for researchers, students, conservationists, policy makers, and others. It will also provide a basis for facilitating hypothesis generation and meta-analysis.

Truncated DNA aptamer for rapid fluorometric detection of the lethal toxin α-amanitin

Mushroom poisoning is the main cause of foodborne poisoning in China. Of these deaths, 90% are caused by the consumption of amatoxins-containing mushrooms. The aim of this study was to investigate the mechanism by which a novel recognition factor-aptamer recognizes the deadly amatoxin known as α-amanitin, and to establish a method for the rapid detection of the α-amanitin. Apt-20 was obtained and characterized to possess the same affinity as the original chain with significantly higher specificity. A rapid fluorescence assay for α-amanitin was developed using Apt-20. The assay system was designed using the principle that the complementary chain of α-amanitin competes with the aptamer for binding, resulting in a change in the fluorescence signal value. The α-amanitin concentration showed a linear relationship with the fluorescence signal in the range of 51.25–800 ng/mL (Y = 1.243x + 351.6, R2 = 0.991) with a detection limit of 51.25 ng/mL. The specificity of the assay system was good. The recovery rates from urine were 93.88%–95.01%, with an accompanying detection time of 1 h, indicating it can achieve the rapid detection of α-amanitin in urine samples.

Study on 4 cases of mushroom poisoning with amanita and identification of poison

Different kinds of poisonous mushrooms contain different toxic components. Acute liver injury caused by amanita mushroom is the main cause of death from poisonous mushroom poisoning in China. Consumption of poisonous mushrooms has an incubation period, there is a false recovery period in the clinical process, and the early performance is slight and does not attract enough attention from doctors, and it is easy to miss the treatment opportunity. The clinical characteristics, treatment and identification of mushrooms containing amanita in 4 patients were analyzed in order to improve clinicians' understanding of the diagnosis and treatment of mushroom poisoning and early species identification.

The Discursive Construction of Antisemitism in Nazi Children’s Books: Elvira Bauer’s Trust No Fox (1936) and Ernst Hiemer’s The Poisonous Mushroom (1938)

AbstractThis article deals with the construction and performance of antisemitism in Nazi children’s books. It provides an explorative discourse analysis ofTrust No Foxas reported (Bauer, Trau keinem Fuchs auf grüner Heid und keinem Jud bei seinem Eid! Ein Bilderbuch für Gross und Klein, Stürmer-Verlag, Nuremberg, 1936) andThe Poisonous Mushroomas reported (Hiemer, Der Giftpilz—ein Stürmerbuch für Jung u. Alt, Stürmer-Verlag, Nuremberg, 1938) through the lens of Critical applied legal linguistics (CrALL). It seeks to elucidate how ‘Jewishness’ is constructed in the two books, with a view to enhancing our understanding of the intertextual and interdiscursive embeddedness of anti-Semitic rhetoric generally and nomination and predication strategies specifically. To this end, a specialised corpus of 10,002 tokens, 2,345 types and 43 illustrations was compiled. Subsequently XML annotation was applied with the data being parsed into headings, sentences and/or lines. It was found that the books follow a Jewish/non-Jewish dichotomy, consistently referring to breaches of custom, morality, religion and law. Both works purport to provide descriptive accounts of ‘Jews as they are,’ when, in fact, they generate the normative illusion of ‘Jews as they ought to be’ according to Nazi ideology. It is suggested to use the insights gained into the anti-Semitic rhetoric of children’s books to detect, describe and critique patterns of anti-Semitic rhetoric today.

Attribution Analysis of Household Foodborne Disease Outbreaks in China, 2010-2020.

Foodborne diseases have become a serious public health problem worldwide, and foodborne disease outbreaks have placed a heavy disease burden on China. Foodborne disease outbreaks occur most frequently among families in China. The objectives of this study were to analyze the cause of household foodborne disease outbreaks in China from 2010 to 2020 and to identify where preventive measures could be targeted. All data were obtained from the China Foodborne Disease Surveillance System Report. A total of 17,985 outbreaks, which resulted in 73,252 illnesses, 38,829 hospitalizations, and 1269 deaths, were reported in this period. Most household outbreaks of foodborne diseases occurred in May-October, and the highest number occurred in July (3620 outbreaks, 20%). The province with the highest number of outbreaks was Yunnan Province (4829 outbreaks), followed by Hunan Province (2264 outbreaks). The attribution analysis revealed that fungi (mainly poisonous mushrooms) were the most implicated food category, with 8873 (49.3%) cases. The second was poisonous plants and their products, with 1552 (8.6%) cases. Fungi were the primary etiologic agent, with 31,125 illnesses, accounting for 42.5% of the incidents. Inedibility and misuse (9423 outbreaks), unknown origin (2505 outbreaks), and improper processing (2365 outbreaks) were the main contributing factors causing outbreaks of foodborne diseases. The results show that southwest China was a high-risk area for household foodborne diseases. Therefore, public health institutions should strengthen supervision and food safety education of residents to reduce the outbreaks of household foodborne diseases.

Application to several suspected poisoning cases of a validated analytical method for the determination of muscarine in human biological matrices using liquid chromatography with high-resolution mass spectrometry detection.

Despite prevention efforts, many cases of mushroom poisoning are reported around the world every year. Among the different toxins implicated in these poisonings, muscarine may induce parasympathetic neurological damage. Muscarine poisonings are poorly reported in the current literature, implying a lack of available data on muscarine concentrations in human matrices. A validated liquid chromatography with high-resolution mass spectrometry detection (Orbitrap technology) method was developed to determine muscarine concentrations in human urine, plasma, and whole blood samples. Muscarine was determined using 100 μL of biological fluids, and precipitation was used for sample preparation. Liquid chromatography-mass spectrometry was performed using an Accucore Phenyl-X analytical column with the electrospray source in positive ion mode. Muscarine was quantitated in parallel reaction monitoring (PRM) mode with D9-muscarine as the internal standard. The method was validated successfully over the concentration range 0.1-100 μg/L for plasma and whole blood and 1-100 μg/L for urine, with acceptable precision and accuracy (<13.5%), including the lower limit of quantification. Ten real cases of suspected muscarine poisoning were successfully confirmed with this validated method. Muscarine concentrations in these cases ranged from 0.12 to 14 μg/L in whole blood, <LOQ to 43 μg/L in plasma, and <LOQ to 1537 μg/L in urine. This work provides a tool of choice for the diagnosis of muscarine poisoning when mushrooms are not available for identification, as well as the possibility of determining toxins in the blood, which is an important step toward understanding the pharmacokinetics of the mycotoxin.

Classification of Poisonous and Edible Mushrooms with Optimized Classification Algorithms

Within the scope of this study, it is aimed to classify the mushroom species consumed as a staple food. For this purpose, 8124 mushroom data with 22 different mushroom feature information were used. 5686 of these data were used for training and 2438 for testing. In the study, poisonous and edible mushroom species were classified by random forest, decision tree, and logistic regression classification methods. The parameters used in the random forest and decision tree classification algorithms used in the study were optimized with the GridSearchCV optimization method. With the random forest algorithm, the highest precision, recall, and F1 score values are 0.93, 0.98, and 0.95, respectively. When these values are examined on a class basis, the highest distinctiveness results were obtained in the poisonous class. In the edible class, the highest performance results were measured as 0.97, 0.92, and 0.95 for precision, recall, and F1 score values, respectively. With the decision Tree algorithm, the highest precision, recall, and F1 score values are 0.98, 0.98, and 0.92, respectively. The highest precision, recall, and F1 score values of the best poisonous class are 0.90, 0.98, and 0.92, respectively. The best performance results of the edible class were obtained with the highest precision, recall, and F1 score values of 0.98, 0.89, and 0.90, respectively. The average accuracy rate was 0.9028 with the Logistic Regression algorithm, and the precision, recall, and F1 score values of the poisonous class were obtained as 0.86, 0.97, and 0.91, respectively. Precision, recall, and F1 score values of the Edible class were obtained as 0.96, 0.83, and 0.89, respectively.

Mushroom Poisonous Prediction Based on the Logistic Regression Model

Mushroom poisoning is a critical food safety issue that poses a significant threat to public health. In China alone, in 2022, there were 1332 reported cases of mushroom poisoning, leading to 28 deaths. With the existence of numerous toxic mushroom species that can cause fatal consequences, it is crucial to develop a reliable model for predicting mushroom toxicity. In this study, a logistic regression model was developed using a dataset consisting of over 60,000 records with 20 different variables. The model was constructed using the Python programming language and the Scikit-learn package. Logistic regression is a binary classification algorithm that uses a sigmoid function to transform its results into probability values. It relies on relationships between variables to predict a value and subsequently converts it to either a positive or negative outcome, corresponding to two classes. The results of this study indicated that the developed model achieved 100% prediction accuracy with the use of 2000 or more records in the dataset. Therefore, the proposed logistic regression model presents a promising tool for accurately predicting mushroom toxicity and mitigating the risks associated with mushroom poisoning. Further research could focus on expanding the dataset to include additional variables, such as environmental and geographical factors, to improve the model's accuracy and applicability.

Türkiye’deki Mantar Zehirlenmeleri: Bir Meta-Analiz Çalışması

Objectives: The aim of this research is to examine the characteristics of mushroom poisoning (MP) in Turkey in the last 20 years and evaluate the clinical results of MP.&#x0D; Materials and methods: Google Scholar, PubMed, Science Direct and Scopus databases were scanned with the phrases "mushroom poisoning, mushroom poisoning in Turkey, mushroom poisining, mushroom poisinings in Turkey".17 studies including sample volume, gender distribution, age average, complaint to the health institution, transplantation and full recovery rates were included in the analysis. In statistical data, random effects model was used.&#x0D; Results: The average age in studies involving MP cases was 38.2 years (95Cl% 36.1-40.3). The average age determined as to whether the family members are adults, elderly or childhood is as expected.&#x0D; Conclusion: Although patients vary in age and gender distribution of MP Turkey, symptoms and findings in admission to the hospital, liver and kidney failure distributions, and transplantation complete remission rates vary significantly.

α-amanitin induces autophagy through AMPK-mTOR-ULK1 signaling pathway in hepatocytes

Amanitin poisoning is one of the most life-threatening mushroom poisonings. α-Amanitin plays a key role in Amanita phalloides intoxication. α-Amanitin shows toxic effects on the liver. However, the mechanism by which α-amanitin induces liver injury has not been elucidated. Autophagy plays a crucial role in maintaining cellular homeostasis and is closely related to the occurrence of a variety of diseases. Studies have shown that autophagy may play an important role in the process of α-amanitin-induced liver injury. However, the mechanism of α-amanitin-induced autophagy remains unclear. Thus, this study aimed to explore the mechanisms of α-amanitin in inducing hepatotoxicity in Sprague Dawley (SD) rats and the normal human liver cell line L02 cells. The SD rats and L02 cells exposed to α-amanitin were observed to determine whether α-amanitin could induce the autophagy of rat liver and L02 cells. The regulatory relationship between autophagy and the AMPK-mTOR-ULK pathway by exposing the autophagy agonist (rapamycin (RAPA)), autophagy inhibitor (3-methylademine (3-MA)), and AMPK inhibitor (compound C) was also explored. Autophagy-related proteins and AMPK-mTOR-ULK pathway-related proteins were detected using Western blot. The results of the study indicated that exposure to different concentrations of α-amanitin led to morphological changes in liver cells and significantly elevated levels of ALT and AST in the serum of SD rats. Additionally, the expression levels of LC3-II, Beclin-1, ATG5, ATG7, AMPK, p-AMPK, mTOR, p-mTOR, and ULK1 were significantly increased in the rat liver. And we found that L02 cells exposed to 0.5 μM α-amanitin for 6 h significantly induced autophagy and activated the AMPK-mTOR-ULK1 pathway. Pretreated with RAPA, 3-MA, and compound C for 1 h, the expression levels of autophagy-related proteins and AMPK-mTOR-ULK pathway-related proteins significantly changed. Our results indicates that autophagy and the AMPK-mTOR-ULK pathway are involved in the process of α-amanitin-induced liver injury. This study may foster the identification of actionable therapeutic targets for A. phalloides intoxication.

Machine Learning-based Classification of Edible and Poisonous Mushrooms: A Performance Comparison

Abstract: In the disciplines of computer vision and machine learning, classifying mushrooms is a key problem. The objective of this study is to create a machine learning model that can correctly categorise different species of mushrooms as either edible or harmful based on their visual characteristics. The Field Guide to North American Mushrooms of the Audubon Society served as the study's data source, and it includes descriptions of hypothetical samples of 23 species of gilled mushrooms from the Agaricus and Lepiota families. Each sample is categorised as either definitely edible, definitely poisonous. To create our machine learning model for this study, we combined five different algorithms: SVM, logistic regression, decision tree, random forest, and KNN. Metrics such as accuracy, precision, recall, and F1 score were used to assess each algorithm's performance. Our findings show that the random forest algorithm outperformed the other algorithms with a 95% accuracy rate. However, by employing feature selection strategies and hyperparameter tuning, we further improved our model. We were able to raise the accuracy of our model to 97% by choosing the most pertinent features and fine-tuning its hyperparameters. The outcomes of this study should offer a practical method for precisely categorising mushrooms and lowering the danger of mushroom poisoning. This paper proposes an ensemble approach utilizing five different machine learning classifiers to detect mushroom classification. The study analyzes various features, such as cap-shape, gill-colour, stem texture and ring-type &amp; stalk features, and the finding

Studies on macrofungi diversity and discovery of new species of Abortiporus from Baotianman World Biosphere Reserve.

This research focuses on macrofungi in Baotianman Biosphere Reserve and their relationships with plant ecosystems. The findings demonstrate the reserve's macrofungal resources. The study collected 832 specimens, among which 351 macrofungi species were identified, belonging to six classes, 19 orders, 54 familiae, and 124 genera, and one new species of Abortiporus was found. Among them, 11 familiae with a total of 231 species were dominated, accounting for 20.37% of the total number of familiae and 65.81% of the total number of species; 14 genera with a total of 147 species were dominated, accounting for about 11.38% the total number of genera and 41.88% of the total number of species. The richness of macrofungi at the species level was considerably different across the four vegetation types in the reserve, showing that the vegetation types had a bigger influence on macrofungi. In the evaluation of macrofungal resources, a total of 196 species of edible fungi, 121 species of medicinal fungi, 52 species of poisonous fungi, and 37 species of macrofungi with unclear economic value were counted. Abortiporus baotianmanensis is a new species of podoscyphaceae in the genus Abortiporus. The new species display the reserve's richness. Next, the project seeks to generate and conserve macrofungal resources.

Pangenomics of the death cap mushroom Amanita phalloides, and of Agaricales, reveals dynamic evolution of toxin genes in an invasive range

The poisonous European mushroom Amanita phalloides (the “death cap”) is invading California. Whether the death caps’ toxic secondary metabolites are evolving as it invades is unknown. We developed a bioinformatic pipeline to identify the MSDIN genes underpinning toxicity and probed 88 death cap genomes from an invasive Californian population and from the European range, discovering a previously unsuspected diversity of MSDINs made up of both core and accessory elements. Each death cap individual possesses a unique suite of MSDINs, and toxin genes are significantly differentiated between Californian and European samples. MSDIN genes are maintained by strong natural selection, and chemical profiling confirms MSDIN genes are expressed and result in distinct phenotypes; our chemical profiling also identified a new MSDIN peptide. Toxin genes are physically clustered within genomes. We contextualize our discoveries by probing for MSDINs in genomes from across the order Agaricales, revealing MSDIN diversity originated in independent gene family expansions among genera. We also report the discovery of an MSDIN in an Amanita outside the “lethal Amanitas” clade. Finally, the identification of an MSDIN gene and its associated processing gene (POPB) in Clavaria fumosa suggest the origin of MSDINs is older than previously suspected. The dynamic evolution of MSDINs underscores their potential to mediate ecological interactions, implicating MSDINs in the ongoing invasion. Our data change the understanding of the evolutionary history of poisonous mushrooms, emphasizing striking parallels to convergently evolved animal toxins. Our pipeline provides a roadmap for exploring secondary metabolites in other basidiomycetes and will enable drug prospecting.

Possible antidote for death cap mushroom poisoning found

Amanita phalloides , also known as the death cap mushroom, contains the toxin α-amanitin, which causes irreparable liver and kidney damage. While this type of mushroom poisoning is rare in the US, it accounted for 788 deaths in China from 2010 to 2020, according to a study of China’s Foodborne Disease Outbreaks Surveillance System . Scientists led by Guohui Wan and Qiaoping Wang at Sun Yat-sen University wanted to understand how α-amanitin kills cells and possibly discover an antidote. Using genome-wide CRISPR screening, they found that a protein called STT3B is required for α-amanitin toxicity. The researchers then virtually screened a library of more than 3,000 compounds approved by the US Food and Drug Administration to find one that could block STT3B. In molecular-docking simulations, they found that indocyanine green—a dye used to determine heart and liver function—could slip into the entrance of STT3B’s substrate-binding pocket. Indocyanine green prevented α-amanitin

The ghost fungus Omphalotus nidiformis (Berk.), new to Indonesia, poisoned foragers

Most Indonesian wild mushrooms remain unexplored, including genus Omphalotus. Omphalotus nidiformis (Omphalataceae), which is primarily distributed in arid parts of Australia and is globally known as a poisonous mushroom. No prior report exists on this species' occurrence in Indonesia. In November 2021, Indonesian foragers consumed the pleurotoid mushroom, which was misidentified as Pleurotus, and were poisoned at Mount Tangkuban Perahu, West Java. This study aimed to ensure the taxonomical position of the poisonous macrofungus and record the poisoning event to increase public awareness in Indonesia. Fresh basidiomata materials were assessed for morphological and molecular information. The specimen's morphology was described based on its macroscopic and microscopic characteristics. The molecular and phylogenetic approach used internal transcribed spacer (ITS) 4/5 sequence data. Combined morphological and molecular analyses confirmed the taxonomical position of our specimen as O. nidiformis (Berk.). Morphology illustrations and the phylogenetic tree of O. nidiformis are provided. This is the first report on O. nidiformis distribution in Indonesia, and this study contributed to Indonesia's macrofungi diversity data. Schima wallichii was confirmed as a host for O. nidiformis. Additionally, the poisoned foragers had symptoms of nausea and vomiting, as well as weakness, perspiration, and trembling. Our report indicated the urgent need for mycological dissemination to the public in Indonesia regarding O. nidiformis identification.

Combining Untargeted Lipidomics Analysis and Chemometrics to Identify the Edible and Poisonous Mushrooms (Pleurotus cornucopiae vs Omphalotus japonicus).

The global phenomenon of eating poisonous mushrooms by mistake occurs every year. Untargeted lipidomics analysis combined with chemometrics was used to identify mushroom varieties. Two kinds of mushrooms with similar appearance, namely, Pleurotus cornucopiae (P. cornucopiae) and Omphalotus japonicus (O. japonicus) were selected as models, where O. japonicus was a poisonous mushroom and P. cornucopiae was an edible mushroom. First, the lipid extraction efficiency of eight solvents was compared. The methyl tert-butyl ether/methanol (2:1, v/v) had higher lipid extraction efficiency of extracting mushroom lipids than other solvents, in terms of the lipid coverage, response intensity, and solvent safety. Afterward, the comprehensive lipidomics analysis of the two mushrooms was conducted. A total of 21 lipid classes and 267 molecular species were identified in O. japonicus, whereas 22 lipid classes and 266 molecular species in P. cornucopiae. The principal component analysis demonstrated that 37 characteristic metabolites, including TAG 18:1_18:2_18:0;1O, TAG 18:1_18:1_18:2, TAG 16:2_18:2_18:2, etc., could be used to distinguish the two mushrooms. These differential lipids were able to identify P. cornucopiae blended with 5% (w/w) O. japonicus. This study explored a novel method for identifying poisonous mushrooms from edible mushrooms and provided a reference for food safety of consumers.

Identification of indocyanine green as a STT3B inhibitor against mushroom α-amanitin cytotoxicity

The “death cap”, Amanita phalloides, is the world’s most poisonous mushroom, responsible for 90% of mushroom-related fatalities. The most fatal component of the death cap is α-amanitin. Despite its lethal effect, the exact mechanisms of how α-amanitin poisons humans remain unclear, leading to no specific antidote available for treatment. Here we show that STT3B is required for α-amanitin toxicity and its inhibitor, indocyanine green (ICG), can be used as a specific antidote. By combining a genome-wide CRISPR screen with an in silico drug screening and in vivo functional validation, we discover that N-glycan biosynthesis pathway and its key component, STT3B, play a crucial role in α-amanitin toxicity and that ICG is a STT3B inhibitor. Furthermore, we demonstrate that ICG is effective in blocking the toxic effect of α-amanitin in cells, liver organoids, and male mice, resulting in an overall increase in animal survival. Together, by combining a genome-wide CRISPR screen for α-amanitin toxicity with an in silico drug screen and functional validation in vivo, our study highlights ICG as a STT3B inhibitor against the mushroom toxin.

Serum metabolomic analyses reveal the potential metabolic biomarkers for prediction of amatoxin poisoning

Amatoxin poisoning leads to over 90% of deaths in mushroom poisoning. The objective of present study was to identify the potential metabolic biomarkers for early diagnosis of amatoxin poisoning. Serum samples were collected from 61 patients with amatoxin poisoning and 61 healthy controls. An untargeted metabolomics analysis was performed using the ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS/MS). Multivariate statistical analysis revealed that the patients with amatoxin poisoning could be clearly separated from healthy controls on the basis of their metabolic fingerprints. There were 33 differential metabolites including 15 metabolites up-regulated metabolites and 18 down-regulated metabolites in patients with amatoxin poisoning compared to healthy controls. These metabolites mainly enriched in the lipid metabolism and amino acid metabolism pathways, such as Glycerophospholipid metabolism, Sphingolipid metabolism, Phenylalanine tyrosine and typtophan biosynthesis, Tyrosine metabolism, Arginine and proline metabolism, which may serve important roles in the amatoxin poisoning. Among the differential metabolites, a total of 8 significant metabolic markers were identified for discriminating patients with amatoxin poisoning from healthy controls, including Glycochenodeoxycholate-3-sulfate (GCDCA-S), 11-Oxo-androsterone glucuronide, Neomenthol-glucuronide, Dehydroisoandrosterone 3-glucuronide, Glucose 6-phosphate (G6P), Lanthionine ketimine, Glycerophosphocholine (GPC) and Nicotinamide ribotide, which achieved satisfactory diagnostic accuracy (AUC>0.8) in both discovery and validation cohorts. Strikingly, the Pearson's correlation analysis indicated that 11-Oxo-androsterone glucuronide, G6P and GCDCA-S were positively correlated with the liver injury induced by amatoxin poisoning. The findings of the current study may provide insight into the pathological mechanism of amatoxin poisoning and screened out the reliable metabolic biomarkers to contribute the clinical early diagnosis of amatoxin poisoning.