Abstract

   α-Amanitin is a representative toxin found in the Amanita genus of
   mushrooms, and the consumption of mushrooms containing α-Amanitin can
   lead to severe liver damage. In this study, we conduct toxicological
   experiments to validate the protective effects of Ganoderic acid A
   against α-amanitin-induced liver damage. By establishing animal models
   with different durations of Ganoderic acid A treatment and conducting a
   metabolomic analysis of the serum samples, we further confirmed the
   differences in serum metabolites between the AMA+GA and AMA groups. The
   analysis of differential serum metabolites after the Ganoderic acid A
   intervention suggests that Ganoderic acid A may intervene in
   α-amanitin-induced liver damage by participating in the regulation of
   retinol metabolism, tyrosine and tryptophan biosynthesis, fatty acid
   biosynthesis, sphingosine biosynthesis, spermidine and spermine
   biosynthesis, and branched-chain amino acid metabolism. This provides
   initial insights into the protective intervention mechanisms of GA
   against α-amanitin-induced liver damage and offers new avenues for the
   development of therapeutic drugs for α-Amanitin poisoning.

   Keywords: α-Amanitin, Ganoderic acid A, metabonomics, intervention
   treatment

1. Introduction

   Mushrooms enjoy significant popularity among individuals at the dining
   table; nevertheless, notwithstanding their delectable nature, mushrooms
   possess the potential to be a “table killer” [[38]1]. In the case of
   mushroom poisoning, Amanita species are significant causes of death,
   and the most lethal of Amanita toxins is α-Amanitin (AMA)
   [[39]2,[40]3]. While AMA has been shown to cause damage to both the
   liver and kidneys, the liver stands out as the predominant affected
   target organ according to AMA toxicokinetic performance [[41]4].
   Furthermore, the main target and its primary biological target are
   thought to be RNA polymerase II in liver cells [[42]5]. However, other
   studies have reported that an inhibition of RNA polymerase II by AMA is
   not the only mechanism by which liver injury can occur [[43]6,[44]7].
   Understanding the toxic mechanisms of AMA is of paramount importance
   for the development of potential therapeutic drugs.

   Ganoderic acid A (GA), characterized by the molecular formula
   C[30]H[44]O[7] and represented by its molecular structure in [45]Figure
   1, is a prominent bioactive compound found in Ganoderma lucidum. It
   boasts a range of pharmacological attributes, including anticancer,
   antioxidant, and anti-inflammatory properties, which render it
   extensively employed in various biomedical applications [[46]8].
   Furthermore, GA displays promising potential in the therapeutic
   intervention of liver injuries [[47]9]. Hence, we embarked on utilizing
   GA to intervene in α-amanitin-induced liver injury. Through a
   metabolomics analysis, we seek to investigate the intervention
   mechanism of GA on α-amanitin-induced liver injury, thereby offering
   novel insights into drug development for the treatment of AMA
   poisoning.

Figure 1.

   [48]Figure 1
   [49]Open in a new tab

   The molecular structure of Ganoderic acid A (GA).

2. Materials and Methods

2.1. Chemicals

   Ultrapure water was generated using a Milli-Q water-purification system
   from Milipore (Bedford, MA, USA). Acetonitrile (LC/MS grade) and
   methanol (LC/MS grade) were purchased from Fisher Scientific (Waltham,
   MA, USA). Formic acid was bought from Aladdin (Shanghai, China) and
   α-Amanitin was purchased from Med Chem Express (Shanghai, China).
   Ganoderic acid A was purchased from Shanghai Yuanye Bio-Technology
   (Shanghai, China).

2.2. Animal Treatment

   Male Kunming (KM) mice (18~22 g) were supplied by the Animal Center of
   Guizhou Medical University (Guizhou, China). The animal usage license
   number was (SCXK Guizhou) 2018-0001 and the animal quality certificate
   number was 1202700. Animal welfare and experimental procedures were
   conducted in accordance with National Institute of Health (NIH)
   guidelines. The mice were acclimatized for a week at a temperature of
   25 ± 2 °C at humidity levels of 50–60% using 12 h light/dark cycles
   before any animal experiments were performed.

   After acclimatization to the environment, the experimental mice were
   randomly divided into 6 groups, with 6 mice in each group, for the
   validation experiment of GA treatment on α-amanitin-induced
   hepatotoxicity. Based on the preliminary research, a dose of 0.3 mg/kg
   of AMA was used to induce stable pathological indicators in the mouse
   model [[50]10]. The experimental groups were divided into the control
   group (a single intraperitoneal injection of physiological saline
   without AMA), the AMA group (AMA dose of 0.3 mg/kg bw), and the AMA+GA
   group (after AMA administration, GA was administered at concentrations
   of 5, 10, 20, and 40 mg/kg bw, respectively). Both AMA and AMA+GA
   groups received a single intraperitoneal injection of AMA at a
   concentration of 30 μg/mL, with an injection volume of 0.01 mL/g bw. In
   the AMA+GA group, after AMA administration, GA was administered via
   oral gavage at doses of 5, 10, 20, and 40 mg/kg bw immediately and
   repeated every 6 h. All experimental animals were euthanized 48 h
   later.

   After determining the optimal therapeutic dosage of GA, three groups
   were established: healthy mice (control), α-amanitin-exposed mice
   (AMA), and GA-treatment mice (AMA+GA). The dosing regimen for AMA in
   the control, AMA, and AMA+GA groups was as previously described.
   Additionally, in the AMA+GA group, GA was administered at a dose of 40
   mg/kg of body weight every 6 h, immediately after AMA administration by
   gavage. Additionally, two separate GA treatment time groups were
   established at 24 and 48 h. Animals in each group were euthanized after
   24 and 48 h of GA treatment, respectively.

2.3. Sample Collection and Preparation

   The animals were handled using the retro-orbital venous plexus puncture
   method to extract blood samples. Equal amounts of individual blood
   samples were combined to create quality-control (QC) samples for each
   group. All the samples were centrifuged at 2500× g for 15 min to
   separate the serum. After a biochemical analysis of the serum samples,
   the remaining serum samples were stored at −80 °C, awaiting
   high-resolution mass spectrometry analysis.

   The liver tissues of each mouse were fixed in 10% neutral formalin,
   dehydrated in a graded manner using a Leica ASP200S automatic tissue
   dehydrator, and embedded in paraffin. Tissue sections, 5 μm thick, were
   cut using a Leica RM2255 microtome. The tissue sections were mounted on
   glass slides and coverslipped using a Leica CV5030 automated
   coverslipper. The sections were stained with hematoxylin and eosin
   (H&E) and observed under a Leica DM500 biological microscope to examine
   tissue pathological changes.

   Frozen serum samples for the mass spectrometry analysis were allowed to
   thaw at room temperature before the analysis. Serum samples of 20 μL
   were collected and placed in 1.5 mL centrifuge tubes, to which 60 μL of
   acetonitrile was added to precipitate proteins. After standing for 5
   min, the samples were vortexed for 10 min and then centrifuged at
   12,000 rpm for 10 min. The supernatant was collected and placed into an
   inner support tube for UPLC/Q-TOF-MS analysis. The samples were
   randomly arranged for the collection of mass spectrometry data, with QC
   sample information collected after every three sample collections.

2.4. Oxidative Damage and Inflammatory Markers

   A total of 0.5 g of liver tissue was used to prepare a liver
   homogenate. A total of 1 mL of liver homogenate was mixed with 5 mL of
   pancreatic enzyme digestion solution and digested in a
   constant-temperature water bath at 37 °C for 30 min. The digestion was
   terminated with cold phosphate-buffered saline (PBS). To the prepared
   tissue suspension, 10 μM of dichlorofluorescein diacetate was added
   using dimethyl sulfoxide as a solvent control. The mixture was then
   incubated at 37 °C for 60 min, followed by centrifugation at 1000× g
   for 10 min. The supernatant was discarded and the pellet was washed
   twice with PBS [[51]11]. The pellet was resuspended in PBS and used for
   fluorescence detection to measure reactive oxygen species (ROS)
   indicators. The wavelength settings were as follows: optimal excitation
   wavelength at 500 nm and optimal emission wavelength at 525 nm. The
   measurements of interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α),
   and cyclooxygenase-2 (COX-2) were conducted using the conventional
   ELISA (enzyme-linked immunosorbent assay) method.

2.5. UPLC/Q-TOF-MS Analysis

   Chromatographic separations were performed with an Agilent 1290 UHPLC
   system using an Agilent Zorbax Eclipse Plus C18 column (100 mm × 2.1
   mm, 1.8 μm), with elution performed at a flow rate of 0.3 mL/min at 35
   °C. In the positive-ion acquisition mode, mobile phase A consisted of
   0.1% of formic acid in water, while mobile phase B comprised 0.1% of
   formic acid in acetonitrile. In the negative-ion acquisition mode,
   mobile phase A consisted of 5 mmol/L of ammonium acetate in water and
   mobile phase B contained 5 mmol/L of ammonium acetate in acetonitrile.
   The gradient elution program was as follows: 0–3.0 min, 5–30% B;
   3.0–12.0 min, 30–80% B; 12.0–14.0 min, 80–100% B; and 14.0–18.0 min,
   100% B. This elution profile produced an overall elution time of 18
   min, which was followed by a post-elution time of 4 min. Aliquots of 2
   μL of each supernatant fraction were used for UPLC/Q-TOF-MS for the
   analysis.

   An MS analysis was performed using an Agilent 6545 Q-TOF mass
   spectrometer (Agilent, Singapore, Singapore) with electrospray
   ionization in positive and negative modes and a full-scan mode from
   50–1000 m/z. The capillary voltage was set to 4.0 kV in the
   positive-ion mode and 3.5 kV in the negative-ion mode, using a drying
   gas flow rate of 12 L/min and a gas temperature of 350 °C. The
   nebulizer pressure used was 40 psi, with the fragmentor voltage set to
   110 V and the skimmer voltage set to 65 V. The MS analysis was
   performed using a mixture of purine (121.0508 m/z) and hexakis
   phosphazine (922.0097 m/z) as internal standards to ensure mass
   accuracy for an acquisition time of 500 ms.

2.6. Data Processing and Statistical Analysis

   Profinder software (version 10.0, Agilent) was used to convert raw data
   into tables containing pairs of mass and retention time data with
   associated intensities for all peaks detected. The parameters used in
   the processing method used a retention time window of 0.2 min and a
   mass tolerance level of 10 ppm ± 10 mDa. Peaks that were not present in
   80% of the samples of a group were filtered out, with peak significance
   subsequently measured using one-way ANOVA, t-tests, fold change
   analyses, and variable importance in projection (VIP). Metabolites were
   considered significant when p < 0.05 and VIP > 1.0 [[52]12].

2.7. Identification of Potential Biomarkers

   Metabolites were identified through comparisons with known data present
   in the Human Metabolome Database ([53]www.hmdb.ca, accessed on 15
   August 2022) and METLINE ([54]http://metlin.scripps.edu, accessed on 15
   August 2022) for allowable mass window errors of 10 ppm. The metabolic
   pathways responsible for production were analyzed using MetaboAnalyst
   5.0 ([55]https://www.metaboanalyst.ca, accessed on 25 August 2022).

3. Results

3.1. Blood Biochemical Indicators

   In the AMA group, the levels of alanine aminotransferase (ALT),
   aspartate aminotransferase (AST), alkaline phosphatase (ALP),
   gamma-glutamyl transpeptidase (GGT), blood urea nitrogen (BUN), and
   creatinine (Cr) were significantly higher than those in the control
   group, with a statistical significance of (p < 0.05). Compared to the
   AMA group, animals treated with varying doses of GA exhibited a
   substantial reduction in serum levels of ALT, AST, ALP, GGT, BUN, and
   Cr. Furthermore, this reduction demonstrated a noteworthy dose–response
   relationship (p < 0.05) ([56]Table 1). The most optimal therapeutic
   outcome was observed for a GA treatment dose of 40 mg/kg bw. At the
   optimal dose of GA (40 mg/kg bw), the animals in each time group
   exhibited a notable reduction in serum levels of ALT, AST, ALP, GGT,
   BUN, and Cr when compared to the AMA group. This reduction became more
   prominent with the prolongation of the treatment duration (p < 0.05)
   ([57]Table 2).

Table 1.

   Comparison of blood biochemical indicators for the treatment of
   α-amanitin-induced liver injury with different doses of GA (mean ± SD,
   n = 6, 48 h).
   Group ALT (U/L) AST (U/L) ALP (U/L) GGT (U/L) BUN (mmol/L) Cr (μmol/L)
   Control 38.50 ± 4.83 61.26 ± 4.15 61.23 ± 6.25 4.36 ± 1.27 8.23 ± 0.82
   32.45 ± 2.83
   AMA 262.98 ± 34.28 ^a 315.12 ± 42.13 ^a 333.25 ± 56.47 ^a 59.52 ± 10.92
   ^a 100.02 ± 11.28 ^a 130.82 ± 18.43 ^a
   AMA+GA
   (5 mg/kg bw) 141.39 ± 15.45 ^b 258.62 ± 20.98 ^b 265.50 ± 34.58 ^b
   40.46 ± 9.83 ^b 85.92 ± 10.52 ^b 109.35 ± 14.33 ^b
   AMA+GA
   (10 mg/kg bw) 99.73 ± 8.41 ^bc 200.62 ± 43.91 ^bc 200.37 ± 23.21 ^bc
   33.39 ± 2.90 ^bc 60.59 ± 9.66 ^bc 87.75 ± 11.26 ^bc
   AMA+GA
   (20 mg/kg bw) 79.55 ± 9.74 ^bcd 138.88 ± 14.30 ^bcd 178.62 ± 36.17 ^bcd
   24.20 ± 2.03 ^bcd 40.56 ± 4.65 ^bcd 67.90 ± 11.28 ^bcd
   AMA+GA
   (40 mg/kg bw) 46.61 ± 9.95 ^bcde 106.62 ± 10.42 ^bcde 142.38 ± 22.13
   ^bcde 13.71 ± 2.18 ^bcde 15.76 ± 5.53 ^bcde 51.01 ± 1.62 ^bcde
   [58]Open in a new tab

   Note. ^a p < 0.05 compared to control. ^b p < 0.05 compared to AMA. ^c
   p < 0.05 compared to AMA+GA (5 mg/kg bw). ^d p < 0.05 compared to
   AMA+GA (10 mg/kg bw). ^e p < 0.05 compared to AMA+GA (20 mg/kg bw).

Table 2.

   Comparison of blood biochemical indicators for the treatment of
   α-amanitin-induced liver injury with different administration times
   (mean ± SD, n = 6).
   Group ALT (U/L) AST (U/L) ALP (U/L) GGT (U/L) BUN (mmol/L) Cr (μmol/L)
   Control 40.92 ± 6.94 72.75 ± 6.34 55.62 ± 11.72 4.84 ± 0.46 5.64 ± 0.70
   35.41 ± 1.98
   AMA (24 h) 157.24 ± 22.72 ^a 140.86 ± 23.81 ^a 114.00 ± 11.03 ^a 19.07
   ± 9.76 ^a 12.93 ± 2.71 ^a 80.67 ± 10.92 ^a
   AMA+GA (24 h) 51.21 ± 16.14 ^b 118.62 ± 11.21 ^b 74.00 ± 7.82 ^b 10.39
   ± 2.91 ^b 9.85 ± 0.96 ^b 49.54 ± 13.07 ^b
   AMA (48 h) 216.65 ± 28.01 ^ab 192.75 ± 44.87 ^ab 253.50 ± 15.70 ^ab
   27.24 ± 4.70 ^ab 23.04 ± 4.47 ^ab 160.35 ± 26.43 ^ab
   AMA+GA (48 h) 68.46 ± 8.85 ^c 117.86 ± 15.26 ^c 101.43 ± 37.66 ^c 14.74
   ± 1.25 ^c 8.96 ± 0.85 ^c 54.14 ± 4.91 ^c
   [59]Open in a new tab

   Note. ^a p < 0.05 compared to control. ^b p < 0.05 compared to AMA (24
   h). ^c p < 0.05 compared to AMA (48 h).

3.2. Histopathological Findings for Liver Tissue

   The liver tissue structure in the 24 and 48 h control groups appeared
   normal. The hepatic cords and hepatic sinusoids were clearly visible,
   and the hepatic sinusoids were filled with numerous red blood cells (as
   indicated by arrows in [60]Figure 2A,D). In the AMA group, the
   histopathological images of liver tissue specimens showed a disruption
   of the liver lobule structure, the disappearance of the hepatic cord
   structure, focal necrosis and swelling of hepatocytes, a significant
   reduction in the hepatic sinusoidal area, and poor filling of the
   hepatic sinusoids. There was an evident infiltration of inflammatory
   cells (mainly mononuclear cells and macrophages) in the liver lobules
   (as indicated by arrows in [61]Figure 2B,E). Under treatment with GA
   (40 mg/kg body weight), the severity of liver damage gradually
   decreased. The liver cord and hepatic sinusoid structures gradually
   recovered. With the extension of the treatment time, improvements were
   observed in features, such as hepatic vacuolation, bleeding,
   infiltration of inflammatory cells (mainly mononuclear cells and
   macrophages), and hepatocyte necrosis (as indicated by arrows in
   [62]Figure 2C,F). Under the influence of GA, the progression of liver
   lesions gradually slowed down, indicating that GA had a certain
   therapeutic effect on liver damage induced by AMA.

Figure 2.

   [63]Figure 2
   [64]Open in a new tab

   Histopathological images of healthy liver tissue, liver tissue exposed
   to AMA, and liver tissue after GA treatment. (A) Histopathological
   image of liver tissue at 24 h. (B) Histopathological image of liver
   tissue after 24 h exposure to AMA. (C) Histopathological image of liver
   tissue after 24 h GA treatment. (D) Histopathological image of liver
   tissue at 48 h. (E) Histopathological image of liver tissue after 48 h
   exposure to AMA. (F) Histopathological image of liver tissue after 48 h
   GA treatment.

3.3. ROS, IL-6, TNF-α, and COX-2 Levels

   Compared to the control group, the expression levels of ROS, IL-6,
   TNF-α, and COX-2 in the liver tissues of mice in the AMA group were
   significantly increased, and the differences were statistically
   significant (p < 0.05). When comparing the AMA+GA group with the AMA
   group, the expression levels of ROS, IL-6, TNF-α, and COX-2 were
   significantly reduced, and this reduction increased with the
   prolongation of the treatment time (p < 0.05) ([65]Table 3).

Table 3.

   The expression levels of ROS, IL-6, TNF-α, and COX-2 after treatment
   with GA at different times (mean ± SD, n = 6).
   Group ROS (pmoL/mL) IL-6
   (μg/mL) TNF-α
   (μg/mL) COX-2 (mg/mL)
   Control 40.67 ± 6.14 32.21 ± 7.04 444.67 ± 96.90 113.33 ± 14.71
   AMA (24 h) 100.38 ± 9.39 ^a 103.67 ± 5.20 ^a 1813.50 ± 84.60 ^a 218.17
   ± 15.97 ^a
   AMA+GA (24 h) 69.52 ± 5.33 ^b 69.78 ± 7.23 ^b 793.03 ± 134.86 ^b 152.67
   ± 16.79 ^b
   AMA (48 h) 202.65 ± 13.85 ^ab 171.67 ± 11.91 ^ab 3749.48 ± 109.50 ^ab
   311.17 ± 15.50 ^ab
   AMA+GA (48 h) 103.50 ± 9.63 ^c 96.91 ± 14.04 ^c 1272.83 ± 89.96 ^c
   212.50 ± 11.31 ^c
   [66]Open in a new tab

   Note. ^a p < 0.05 compared to control. ^b p < 0.05 compared to AMA (24
   h). ^c p < 0.05 compared to AMA (48 h).

3.4. Multivariate Analysis of Analytes Present in the Blood

   Metabolic profiling of the blood samples was conducted using Mass
   Hunter Workstation Software (version 11.0, Agilent, Bejing, China).
   After performing the data collection, data preprocessing was performed
   using Profinder software (Version 10.0, Agilent), which included data
   retention time alignment, filtering, matching, and peak identification.
   In the positive-ion mode, 3074 compounds were obtained, and in the
   negative-ion mode, 2887 compounds were obtained. The relative standard
   deviation (RSD) of peak areas was calculated using QC samples.
   Compounds with an RSD higher than 30% were filtered out. The results
   show that 82% of the peaks in the QC sample group meet the required
   standards, indicating good stability during the sample analysis
   process.

   To comprehensively understand the metabolic differences in the serum
   samples, the obtained compound data were converted into CEF files and
   imported into Mass Profiler Professional software (Version 15.1,
   Agilent) for the multivariate analysis. Statistical filtering was
   applied to both the 24 and 48 h experimental groups. Compounds with
   data missing in each group exceeding 20% were filtered out, while
   compounds exhibiting statistically significant differences between the
   control group versus the AMA group and the AMA+GA group versus the AMA
   group (p < 0.05) were retained. Following filtering, 116 compounds were
   obtained for the 24 h experimental group ([67]Supplementary Table S1),
   and 157 compounds were obtained for the 48 h experimental group
   ([68]Supplementary Table S2). A Principal component analysis (PCA)
   model was applied for the clustering analysis of the filtered data.
   Missing peak response intensities were replaced with the within-group
   mean. The data were centered around this within-group mean and then
   normalized by dividing them by the standard deviation of variables
   within the group. At 24 h, there was a significant separation between
   the control and AMA groups, while the AMA+GA group was positioned
   between the control and AMA groups with unclear clustering
   differentiation and two principal components, including PC 1 (70.7%)
   and PC 2 (5.2%) in [69]Figure 3A. At 48 h, there was a significant
   separation between the control and AMA groups, and the AMA+GA group
   also showed significant differentiation from the AMA group, approaching
   the control group, resulting in clear clustering distinctions and two
   principal components, including PC 1 (52.2%) and PC 2 (10.3%) in
   [70]Figure 3B. Subsequently, the filtered compounds were subjected to
   clustering analysis once again using the partial least squares
   discriminant analysis (PLS-DA) model for cluster scoring validation.
   The results of the PLS-DA analysis further confirm the improved cluster
   separation for both the 24 and 48 h experimental groups after the GA
   intervention ([71]Supplementary Figure S1). The results show distinct
   grouping trends with the increasing intervention time. These results
   indicate that the process of AMA poisoning disrupts the metabolism of
   endogenous metabolites, while GA can alter the metabolic disturbances
   caused by AMA poisoning, thereby exhibiting therapeutic effects. In the
   24 and 48 h experimental groups, the filtered compounds’ response
   intensities were evaluated using the orthogonal partial least squares
   discriminant analysis (OPLS-DA), and statistical differences in the
   metabolite response intensities were obtained between the control and
   AMA groups, as well as between the AMA and AMA+GA groups
   ([72]Supplementary Figure S2). These findings underscore significant
   serum metabolic distinctions between the AMA and control groups, as
   well as noteworthy shifts in serum metabolites following GA treatment
   compared to the AMA group.

Figure 3.

   [73]Figure 3
   [74]Open in a new tab

   PCA (unsupervised) analysis of serum metabolites in healthy mice
   (control), α-amanitin-exposed mice (AMA), and GA treatment (AMA+GA).
   (A) Score plot after 24 h of GA treatment. (B) Score plot after 48 h of
   GA treatment.

3.5. Metabolites’ Identification and Pathway Analysis

   Peak response intensities between the control and AMA groups, as well
   as between the AMA+GA and AMA groups, in the 24 and 48 h experiments
   were statistically compared. Compounds with significantly altered
   inter-group peak response intensities, as determined by t-tests (p <
   0.05) and VIP scores higher than 1.0, were considered potential
   differential metabolites. The selected ions were identified by a
   comparison with ion data stored in the online Human Metabolome
   ([75]www.hmdb.ca, accessed on 25 August 2022) and METLINE
   ([76]https://metlin.scripps.edu, accessed on 25 August 2022) databases.
   The mass tolerances between the measured m/z values and exact masses of
   the compounds were set to 10 ppm. The mass spectrometry information for
   potential differential metabolites can be found in [77]Table 4. The
   statistical results of the peak response intensities for the
   differential metabolites between the control and AMA groups can be
   found in [78]Supplementary Table S3. The statistical results of the
   peak response intensities for the differential metabolites between the
   AMA and AMA+GA groups after the GA intervention are presented in
   [79]Supplementary Table S4.

Table 4.

   The mass spectrometry information for potential differential
   metabolites.
   Metabolite Retention Time (min) Mass Found Adducts Exact Mass MS/MS
   Fragments MS/MS
   CE (eV)
   Retinal 11.92 285.2217 [M+H]^+ 284.214 161.0955 20
   175.1479
   119.0849
   Retinyl ester 13.87 303.2311 [M+H]^+ 302.2246 285.221 20
   135.1157
   103.0533
   Retinol 14.56 287.2373 [M+H]^+ 286.2297 121.0351 20
   269.2248
   93.0686
   Retinoate 11.06 301.2167 [M+H]^+ 300.2089 123.1166 20
   161.0963
   81.0696
   Spermidine 0.68 146.1645 [M+H]^+ 145.1579 72.0809 20
   84.0809
   112.1122
   Spermine 0.7 203.2234 [M+H]^+ 202.2157 112.1122 20
   129.1389
   84.0804
   Sphinganine 8.76 302.3058 [M+H]^+ 301.2981 60.0451 20
   284.2949
   252.2844
   Tryptophan 2.59 205.0967 [M+H]^+ 204.0899 146.0602 20
   118.0649
   188.0714
   Palmitic acid 14.78 255.2324 [M−H]^− 256.2402 255.2324 20
   Docosahexaenoic acid 13.42 327.2325 [M−H]^− 328.2402 283.2424 20
   327.2323
   229.1955
   Myristic acid 12.51 227.2019 [M−H]^− 228.2089 277.2019 20
   68.9957
   α-Linolenic acid 12.66 277.2174 [M−H^]− 278.2246 277.2174 20
   59.0138
   γ-Linolenic acid 12.84 277.2177 [M−H]^− 278.2246 277.2177 20
   59.0141
   Tyrosine 0.92 180.0667 [M−H]^− 181.0739 119.0547 20
   163.0535
   72.0099
   4-Methyl-2-oxopentanoate 0.93 129.0562 [M−H]^− 130.063 129.0562 10
   101.0604
   Hydroxyphenylpyruvic acid 0.92 179.0353 [M−H]^− 180.0423 179.0353 20
   135.0452
   106.9578
   [80]Open in a new tab

   The analysis of differential metabolites in the 24 h experimental group
   revealed 15 identified differential metabolites, mainly including amino
   acids, fatty acids, sphingolipids, and other compounds. Among these,
   six compounds exhibited significant changes during the early stages of
   GA treatment (p < 0.05, VIP > 1). This suggests that GA can modulate
   the metabolic abnormalities induced by AMA during the initial
   treatment, particularly affecting metabolites, like spermidine,
   sphinganine, myristic acid, tyrosine, 4-methyl-2-oxopentanoate, and
   hydroxyphenylpyruvic acid ([81]Figure 4). Subsequently, we conducted a
   metabolic pathway enrichment analysis using MetaboAnalyst 5.0. The
   enrichment analysis showed that, in the early stages of GA treatment,
   there was a significant regulation (p < 0.05) of spermidine and
   spermine biosynthesis, tyrosine and tryptophan biosynthesis,
   sphingolipid metabolism, fatty acid biosynthesis, and branched-chain
   amino acid metabolism induced by AMA ([82]Supplementary Figure S3).

Figure 4.

   [83]Figure 4
   [84]Open in a new tab

   Fifteen metabolites with significant differences (p < 0.05, VIP > 1) in
   the 24 h experimental group after identification, involving metabolic
   pathways related to nucleotide metabolism, amino acid metabolism, and
   lipid metabolism. Among these metabolites, spermidine, tyrosine,
   hydroxyphenylpyruvic acid, 4-methyl-2-oxopentanoate, myristic acid, and
   sphinganine exhibited significant changes after GA treatment, as
   highlighted by the yellow dashed boxes in the figure. Red boxes
   represent the AMA group, green boxes represent the AMA+GA group, blue
   boxes represent the control group, and yellow dots represent the mean
   peak response intensity of compounds within each group.

   Subsequently, an analysis of differential metabolites in the 48 h
   experimental group identified 15 differential metabolites. Among these,
   eight compounds exhibited significant changes during the mid-term GA
   treatment (p < 0.05, VIP > 1). This indicated that, in the mid-term of
   the treatment, GA could modulate the metabolic abnormalities caused by
   AMA, including retinal, retinol, retinoate, spermidine, sphinganine,
   tryptophan, myristic acid, and 4-methyl-2-oxopentanoate ([85]Figure 5).
   The metabolic pathway enrichment analysis was conducted using
   MetaboAnalyst 5.0. The enrichment analysis demonstrated that after GA
   treatment, several metabolic pathways induced by AMA, including retinol
   metabolism, tyrosine and tryptophan biosynthesis, fatty acid
   biosynthesis, sphingosine biosynthesis, spermidine and spermine
   biosynthesis, and branched-chain amino acid metabolism, were
   significantly modulated (p < 0.05) ([86]Supplementary Figure S3).

Figure 5.

   [87]Figure 5
   [88]Open in a new tab

   Fifteen metabolites with significant differences (p < 0.05, VIP > 1) in
   the 48 h experimental group after identification, encompassing
   metabolic pathways associated with nucleotide metabolism, amino acid
   metabolism, and lipid metabolism. Notably, the compounds spermidine,
   retinal, retinol, retinoate, tryptophan, 4-methyl-2-oxopentanoate,
   myristic acid, and sphinganine exhibited substantial changes following
   GA treatment, as highlighted by the yellow dashed boxes in the figure.
   Red boxes represent the AMA group, green boxes represent the AMA+GA
   group, blue boxes represent the control group, and yellow dots
   represent the mean peak response intensity of compounds within each
   group.

4. Discussion

   The hepatotoxicity of AMA primarily arises from its inhibition of RNA
   polymerase II activity, disrupting normal RNA synthesis in liver cells,
   ultimately leading to hepatocyte death [[89]13]. However, the
   inhibition of RNA polymerase II is not the sole pathway through which
   α-amanitin-induced liver damage occurs [[90]14,[91]15,[92]16]. There
   may be other therapeutic targets for α-amanitin-induced liver injury.
   Ganoderma lucidum (Lingzhi) has a long history of medicinal use in
   China, and GA is one of the major triterpenoid compounds found in
   Lingzhi [[93]17]. It has shown a protective effect against liver damage
   [[94]18]. In this study, we aimed to validate the protective role of GA
   against α-amanitin-induced liver injury using an animal model, and to
   explore the metabolic pathways regulated by GA during the treatment
   process through a metabolomics analysis.

   Serum biochemical markers can reflect the body’s metabolic function and
   tissue damage status [[95]19]. Among them, alterations in AST, ALT, and
   ALP levels are indicative of liver tissue damage [[96]20,[97]21], while
   GGT concentrations are utilized for liver injury diagnosis [[98]22]. In
   our study, mice exposed to AMA exhibited a notable elevation (p < 0.05)
   in serum levels of ALT, AST, ALP, and GGT. Furthermore, a
   histopathological examination of liver specimens confirmed disruptions
   in the hepatic lobular structure, focal necrosis, hepatocyte swelling,
   and inflammatory cell infiltration induced by AMA. Following GA
   treatment, there was a substantial reduction (p < 0.05) in serum levels
   of ALT, AST, ALP, and GGT, with these changes becoming more pronounced
   with an extended GA-treatment duration. Histopathological observations
   of liver specimens in the AMA+GA group revealed a marked decrease in
   inflammatory cell infiltration compared to the AMA group, and this
   amelioration became increasingly significant with prolonged treatment.
   These findings underscore that GA exhibits therapeutic potential in
   mitigating liver damage induced by AMA. BUN and Cr levels serve as
   crucial biochemical markers for diagnosing kidney injury and
   dysfunction [[99]23,[100]24]. In our study, AMA exposure led to a
   significant increase (p < 0.05) in BUN and Cr levels, while these
   markers exhibited a substantial decrease (p < 0.05) in serum following
   GA treatment. Furthermore, this decreasing trend became more
   significant with prolonged treatment, suggesting that GA not only
   ameliorated liver damage induced by AMA, but also intervened in the
   renal toxicity induced by AMA.

   ROS, IL-6, TNF-α, and COX-2 are biomolecules associated with
   inflammation and cellular damage. When liver cells are damaged, they
   can trigger the release of IL-6 and TNF-α by immune cells, promoting an
   inflammatory response that leads to cell apoptosis [[101]25,[102]26].
   Increased COX-2 expression accelerates prostaglandin synthesis, further
   exacerbating the inflammatory process in liver cells [[103]27]. In
   addition, the expression of inflammatory factors resulting from liver
   cell damage can promote the production of more ROS, leading to
   oxidative stress damage and further exacerbating cell apoptosis
   [[104]28]. In this study, α-amanitin-treated mice showed a significant
   increase in ROS, IL-6, TNF-α, and COX-2 levels in serum (p < 0.05).
   Combined with histopathological images of liver specimens, this
   indicated that mice exhibited an inflammatory response in the liver
   under the influence of AMA. Following treatment with GA, there was a
   significant decrease in ROS, IL-6, TNF-α, and COX-2 levels (p < 0.05).
   This, in conjunction with the histopathological results of the liver
   specimens, suggests that GA can significantly reduce the inflammatory
   response in liver cells induced by AMA and mitigate the oxidative
   stress in liver cells, thereby exerting a protective effect on the
   liver.

   PCA models can reflect differences in metabolites between different
   experimental groups and present the trends in metabolite changes under
   different intervention times. In this study, PCA models were used to
   analyze the serum metabolic trends at different intervention times
   during the process of liver damage induced by AMA and treated with GA.
   Comparing the serum metabolite PCA clustering results for 24 and 48 h
   under GA intervention, at 24 h, there was differentiation between the
   control and AMA groups, but the AMA+GA group was positioned between the
   two groups and not distinctly separated from the AMA group. However, at
   48 h, the AMA+GA group could be significantly distinguished from the
   AMA group. After GA treatment, the metabolic characteristics of the
   AMA+GA group gradually approached those of the control group,
   demonstrating that a GA intervention could regulate the metabolic
   abnormalities caused by AMA.

   The results of the analysis of the metabolic differences between the
   control and AMA groups indicate that AMA induces relatively minor
   differences in metabolites at different time points. These differences
   all point to similar metabolic pathways, primarily involving
   abnormalities in retinol metabolism, spermidine and spermine
   metabolism, sphingolipid metabolism, fatty acid metabolism, and
   unsaturated fatty acid metabolism. Following the GA intervention, it
   was observed that GA participated in the regulation of retinol
   metabolism, tyrosine and tryptophan biosynthesis, fatty acid
   biosynthesis, sphingosine biosynthesis, spermidine and spermine
   biosynthesis, and branched-chain amino acid metabolism. Retinal,
   retinol, and retinoate are different forms of vitamin A metabolism. The
   experimental results indicate that, under the influence of AMA, the
   concentrations of retinal, retinyl ester, retinol, and retinoate in the
   serum samples of the experimental animals decrease (p < 0.05), which
   may lead to increased oxidative stress [[105]29], subsequently
   accelerating the accumulation of ROS within the cells and triggering
   cell apoptosis [[106]30,[107]31]. Moreover, vitamin A can modulate
   intracellular lipid metabolism. The decreased levels of different forms
   of vitamin A compounds can disrupt intracellular lipid body metabolism,
   promoting lipid body accumulation [[108]32]. Intracellular lipid body
   accumulation can induce the further aggravation of liver inflammation,
   hastening liver cell apoptosis [[109]33]. Under GA intervention
   conditions, significant alterations (p < 0.05) were observed in the
   concentrations of retinal, retinol, and retinoate. This indicates that
   GA can ameliorate the cellular oxidative environment by regulating
   different forms of vitamin A compounds, reducing oxidative stress.
   Simultaneously, GA can regulate lipid metabolism through retinal,
   retinol, and retinoate, lowering the risk of liver cell inflammation.

   Spermidine and spermine are polyamine compounds that are involved in
   the interaction between DNA and proteins, regulating cell proliferation
   and differentiation [[110]34]. Maintaining normal levels of polyamines
   in the body is crucial. Cell damage can lead to an increase in
   polyamine levels in the body, and excessively high levels of polyamines
   can produce hydrogen peroxide and acrolein during polyamine metabolism,
   increasing oxidative stress damage. This can disrupt proteins, DNA, and
   other cellular components, causing severe toxic damage
   [[111]35,[112]36]. The experimental results indicate that, under the
   influence of AMA, the concentrations of spermidine and spermine in the
   serum samples of the experimental animals significantly increase (p <
   0.05). This leads to an exacerbation of oxidative stress, and the
   excess metabolites of polyamines may cause damage to liver cells,
   accelerating the process of liver cell apoptosis. Under GA intervention
   conditions, the concentrations of spermidine and spermine both
   decreased, with spermidine showing the most significant decrease. This
   suggests that GA can intervene in the polyamine metabolism process in
   the organism, promoting the return of abnormal polyamine concentration
   levels caused by AMA to normal values. It reduces oxidative stress
   levels and the cytotoxic effects of excess polyamine metabolites, thus
   playing a protective role in the liver.

   Sphinganine belongs to sphingolipid compounds, and sphingolipids play a
   crucial role in membrane and lipoprotein structures, as well as in
   cellular regulation, serving as growth factors, differentiation
   factors, cytokines, and, increasingly, as second messengers for various
   stimuli. Therefore, maintaining stable concentrations of sphingolipids
   in the body is of great significance [[113]37]. Disruptions in the
   metabolism of sphinganine-type sphingolipids can lead to cellular
   toxicity [[114]38,[115]39]. The experimental results indicate that,
   under the influence of AMA, the concentration levels of sphinganine in
   the serum samples of the experimental animals significantly increase (p
   < 0.05), leading to cellular toxicity and accelerating apoptosis in
   liver cells. Under GA intervention conditions, the concentration levels
   of sphinganine decreased and approached those of the control group.
   This suggests that GA can effectively regulate the disrupted metabolism
   of sphingolipids caused by AMA, promoting the return of sphinganine to
   normal concentration levels and reducing the cytotoxic effects caused
   by its disruption.

   Myristic and palmitic acids are saturated fatty acids that play
   important roles in the normal metabolism of liver cells [[116]40].
   Changes in the concentration levels of saturated fatty acids in the
   body can affect the oxidative stress levels in liver cells.
   Specifically, the excessive accumulation of high-concentration
   saturated fatty acids in liver cells can directly lead to mitochondrial
   dysfunction and oxidative stress responses, accelerating apoptosis in
   liver cells [[117]41]. The experimental results indicate that, under
   the influence of AMA, the serum samples of the experimental animals
   show a significant increase (p < 0.05) in the concentration of
   saturated fatty acids, represented by myristic and palmitic acids. This
   leads to an abnormal mitochondrial function in liver cells. Under GA
   intervention conditions, there was a significant decrease (p < 0.05) in
   the concentration of myristic acid. This suggests that GA can regulate
   the metabolism of saturated fatty acids and, in turn, the metabolic
   dysfunction of mitochondria in liver cells. This regulation leads to a
   reduction in oxidative stress levels in liver cells, ultimately
   protecting them.

   Tyrosine and tryptophan are essential amino acids in the body that play
   crucial roles in protein synthesis. They are also precursors to various
   bioactive molecules. Hydroxyphenylpyruvic acid is one of the
   metabolites of tyrosine. Tyrosine is primarily metabolized in the
   liver, and severe liver damage can lead to an increase in the
   concentration of tyrosine and its metabolites [[118]42]. Additionally,
   abnormally elevated levels of tyrosine in the body can lead to the
   accumulation of toxic metabolic intermediates, exacerbating liver
   damage [[119]43]. The experimental results indicate that, under the
   influence of AMA, there is a significant increase (p < 0.05) in the
   concentrations of tryptophan, tyrosine, and hydroxyphenylpyruvic acids
   in the serum samples of the experimental animals. This suggests that
   liver damage caused by AMA disrupts the metabolism of tyrosine and
   tryptophan pathways. Under GA intervention conditions, there was a
   significant decrease (p < 0.05) in the concentrations of tyrosine and
   hydroxyphenylpyruvic acids in the 24 h AMA+GA group, and a significant
   decrease (p < 0.05) in tryptophan concentration in the 48 h
   intervention group. This indicates that a GA intervention can regulate
   the amino acid metabolism abnormalities caused by AMA.

   Methyl-2-oxopentanoate is an intermediate product in the metabolism
   pathway of valine, and it is involved in branched-chain amino acid
   metabolism. Branched-chain amino acids are a critical group of amino
   acids that play essential roles in protein synthesis and energy
   metabolism [[120]44]. Under the influence of AMA, there is an increase
   (p < 0.05) in the concentration of 4-methyl-2-oxopentanoate, indicating
   that AMA may interfere with the metabolism of branched-chain amino
   acids. Branched-chain amino acids are involved in important energy
   metabolism pathways in the body, and abnormalities in their metabolism
   can lead to mitochondrial dysfunction in liver cells, resulting in
   disrupted energy metabolism [[121]45]. Under GA intervention
   conditions, there was a significant decrease (p < 0.05) in the
   concentration of 4-methyl-2-oxopentanoate, and it returned to normal
   levels. This suggests that GA can regulate the abnormal metabolism of
   branched-chain amino acids induced by AMA.

5. Conclusions

   In summary, we utilized the UPLC-Q/TOF-MS analysis in combination with
   a pathological analysis to confirm that GA can intervene in liver
   damage caused by AMA. Observing the trends and differences in
   metabolites at different stages of the GA intervention suggests that GA
   can intervene in the liver damage caused by AMA by regulating retinol
   metabolism, tyrosine and tryptophan biosynthesis, fatty acid
   biosynthesis, sphingosine biosynthesis, spermidine and spermine
   biosynthesis, and branched-chain amino acid metabolism. Our findings
   elucidate the protective intervention mechanisms of GA against
   α-amanitin-induced liver damage, presenting the possibility of GA as a
   potential therapeutic target for AMA poisoning.

Abbreviations

   The following abbreviations are used in this manuscript:
   ALP     Alkaline Phosphatase
   ALT     Alanine Aminotransferase
   AMA     α-Amanitin
   AST     Aspartate Aminotransferase
   BUN     Blood Urea Nitrogen
   COX-2   Cyclooxygenase-2
   Cr      Creatinine
   GA      Ganoderic Acid A
   GGT     Gamma-Glutamyl Transpeptidase
   IL-6    Interleukin-6
   OPLS-DA Orthogonal Partial Least Squares Discriminant Analysis
   PBS     Phosphate Buffered Saline
   PCA     Principal Component Analysis
   PLS-DA  Partial Least Squares Discriminant Analysis
   QC      Quality Control Samples
   ROS     Reactive Oxygen Species
   RSD     Relative Standard Deviation
   TNF-α   Tumor Necrosis Factor-α
   VIP     Variable Importance in Projection
   [122]Open in a new tab

Supplementary Materials

   The following supporting information can be downloaded at:
   [123]https://www.mdpi.com/article/10.3390/metabo13111164/s1, Figure S1.
   PLS-DA (supervised) analysis of serum metabolites in healthy mice
   (control), α-amanitin-exposed mice (AMA), and GA treatment (AMA+GA).
   (A) Score plot after 24 h of GA treatment (R^2 = 0.998, Q^2 = 0.935).
   (B) Score plot after 48 h of GA treatment (R^2 = 0.999, Q^2 = 0.903).
   Figure S2. Orthogonal partial least squares discriminant analysis
   (OPLS-DA) score plots of serum metabolites in healthy mice (control),
   α-amanitin-exposed mice (AMA), and GA treatment (AMA+GA). (A) Score of
   the 24 h control versus AMA groups (R^2X = 0.639, R^2Y = 0.849 and Q^2
   = 0.815). (B) Score of the 24 h AMA versus AMA+GA groups (R^2X = 0.443,
   R^2Y = 0.676 and Q^2 = 0.623). (C) Score of the 48 h control versus AMA
   groups (R^2X = 0.563, R^2Y = 0.952 and Q^2 = 0.930). (D) Score of the
   48 h AMA versus AMA+GA groups (R^2X = 0.493, R^2Y = 0.933 and Q^2 =
   0.906). Figure S3. Metabolic pathway analysis results. (A) The 24 h
   control versus AMA groups. (B) The 24 h AMA versus AMA+GA groups. (C)
   The 48 h control versus AMA groups. (D) The 48 h AMA versus AMA+GA
   groups. Table S1. Accurate molecular weight, retention time, and peak
   response intensity of compounds in the 24 h experimental group after
   statistical filtration. Table S2. Accurate molecular weight, retention
   time, and peak response intensity of compounds in the 48 h experimental
   group after statistical filtration. Table S3. The statistical analysis
   of differential metabolites between control and AMA groups. Table S4.
   The statistical analysis of differential metabolites between AMA and
   AMA+GA groups.
   [124]Click here for additional data file.^ (802.7KB, zip)

Author Contributions

   Conceptualization, L.L. and Y.D.; methodology, C.Z., S.L. and L.L.;
   formal analysis, J.Y. and K.Z.; investigation, L.L. and H.L.;
   resources, L.Z., S.L. and C.Z.; data curation, C.Z., J.Y. and K.Z.;
   writing—original draft preparation, C.Z. and L.Z.; writing—review and
   editing, L.L., S.L., C.Z. and L.Z.; supervision, L.L., H.L. and Y.D.
   All authors have read and agreed to the published version of the
   manuscript.

Institutional Review Board Statement

   The study was conducted in accordance with the Declaration of Helsinki
   and the protocol was approved by the Ethics Committee of Guizhou
   Provincial Center for Disease Control and Prevention (No. S2020-005,
   approved in August 2020).

Informed Consent Statement

   Not applicable.

Data Availability Statement

   The data presented in this study are available from the corresponding
   authors upon request. The data are not publicly available due to
   institutional requests.

Conflicts of Interest

   The authors declare no conflict of interest.

Funding Statement

   This research was funded by the Guizhou Provincial Science and
   Technology Support Program (No. Qiankehe-ZK (2021) 496) and the Science
   and Technology Fund of Guizhou Health Commission (No. gzwkj 2021-407).

Footnotes

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References