Introduction

Trametes sanguinea belongs to the Polyporaceae family1, grows on the fallen trees, stumps, and rotten wood of broad-leaved trees, occasionally on conifers and its distribution is strongly associated with sun-exposed habitats and forest edges. The fruiting body of T. sanguinea is annual and woody, often appearing in summer and autumn, and is closely attached to the substrate which makes it difficult to harvest2. The fruiting bodies of T. sanguinea are rich in nutrients, hence, in some East Asian countries such as China, they are often used in soups and stews, not only to enhance the flavor of the food but also used as mineral and protein supplements3. As a white-rot fungi, T. sanguinea degrades lignocellulose by secreting laccase extracellularly, lignin initially gives wood its brown color, but it turns white after being infected by white-rot fungus4,5,6. Due to its degradation potential, it is also commonly used in wastewater treatment, food processing and other fields6,7,8.

Furthermore, T. sanguinea is also a medicinal fungus, several studies represented that the various active substances such as terpenoids, polysaccharides and sterols9,10 exhibit significant pharmacological activities, such as antitumor, antibacterial, anti-inflammatory and immune-enhancing effects3,11,12. In recent decades, polysaccharides from medicinal fungi have attracted extensive interest from researchers worldwide due to their high anti-tumor activity and low side effects13,14,15. Some studies demonstrated that polysaccharides extracted from fungi can act as natural immune modulators13.

Recent studies on medicinal macro fungi have predominantly focused on secondary metabolites derived from mycelia and fruiting bodies, particularly polysaccharides, triterpenoids, and phenolic compounds with demonstrated pharmaceutical potentials16,17. However, growing evidence suggests that fungal spores may represent a unique and underexplored repository of bioactive substances. During sporulation, fungi undergo specialized metabolic reprogramming to synthesize stress-resistant compounds that are typically absent or present in minimal quantities within vegetative tissues18,19,20. Despite these advances, the biochemical potential of T. sanguinea spores remains largely uncharted. Current research on this species has prioritized laccase production from fruiting bodies and antioxidant profiling of mycelial extracts21,22 leaving its spore-derived components systematically uncharacterized. The spores of T. sanguinea formed by the fragmentation of hyphae, so the number of spores inseparably related to the length and quality of the hyphal body. The purpose of optimizing the growth conditions of hyphal bodies is to obtain more healthy hyphae, which can solve the problem of a small number of spores, which is a prerequisite for the subsequent induction and collection of spores. The striking orange-red pigmentation observed in T. sanguinea fruiting body is a phenotypic trait potentially linked to naphthoquinone (a class of redox-active aromatic compounds) biosynthesis, which provides compelling rationale for investigating their antioxidant metabolite profiles23.

In recent years, with the increasing research on the medicinal value of T. sanguinea, it has shown that the chemical components of the fruiting body and mycelium of T. sanguinea are similar3,10,12. The spores, mycelium and fruiting body are different morphological stages in the life cycle of fungi, and they have obvious differences in morphology, structure and function17. However, the metabolites of T. sanguinea spores were yet to be known and there is a lack of comprehensive analysis to differentiate the key metabolites in the spores, mycelium and fruiting body of T. sanguinea. Therefore, to better develop and utilize the resources of T. sanguinea, it is necessary to systematically analyze the chemical components of different morphological stages of T. sanguinea.

At present, there are few studies on the active ingredients of T. sanguinea, and most of them adopt a single detection technology, such gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (LC-MS) and other techniques, which can only detect a few components. Untargeted metabolomics is a method that can systematically and comprehensively analyze the endogenous metabolites in biological organisms24. It can analyze the types, quantities and changes of small molecular metabolites in biological systems. Traditional analysis technology can only detect a certain type or several types of components. Metabolomics comprehensively and systematically carry out metabolite analysis and combine multivariate statistical analysis efficiently and screen characteristic differential metabolites, which have been widely used in the field of microorganisms.

Therefore, in current study, we conducted a comprehensive investigation for the first time to collect the spores by several optimization conditions and analyzed their viability, morphology and germination potentiality of T. sanguinea ZHSJ and further we have analyzed for the presence of differential metabolites in spores by comparing with the other morphological stages of fungi that is mycelium and fruiting body through LC-MS based untargeted metabolomics analysis, (Fig. 1), thereby transcending the current narrow understanding of this fungus spores.

Fig. 1
Fig. 1The alternative text for this image may have been generated using AI.
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The overall outline of the Trametes sanguinea ZHSJ spores.

Materials and methods

Isolation and identification of fungus

Sample collection

Red-colored fruiting bodies morphologically consistent with T. sanguinea were collected by Lizeng Peng from the Mengyin Yun Meng scenic area, Linyi city, Shandong Province, China. Samples were aseptically transferred to sterile sampling bags, promptly transported to the laboratory, and stored at 4 ℃ for further analysis.

Isolation of fungi

The pure mycelium of T. sanguinea strain was obtained by tissue culture techniques. Under aseptic conditions in a laminar flow cabinet, fruiting bodies were surface sterilized by cleaning with sterile water, followed by disinfection with 75% (v/v) ethanol 3 times to ensure the surface impurities were completely removed. Then, it was washed with sterile water 5 times, and the sterile water on the surface was completely removed by sterile absorbent paper. Approximately, 100 µL of the final rinse water was inoculated on Potato Dextrose Agar medium (PDA) (Haibo Biotechnology Co., Ltd., Qingdao, China) to examine surface sterilization. Further, T. sanguinea samples were cut into 0.5 cm × 0.5 cm pieces with a sterile scalpel and placed on PDA, followed by incubation at 28 ℃ for 4–7 days for fungal growth. After 7 days, white velvety mycelium was observed around the fruiting body pieces, then mycelium was inoculated onto a new PDA plate and incubated at 28 ℃ for 7–14 days to obtain the pure T. sanguinea mycelium and named as T. sanguinea ZHSJ. T. sanguinea ZHSJ has been stored at China General Microbiological Culture Collection Center with deposit number 40866This procedure allowed pure cultures to be obtained, which were preserved for further study.

Morphological identification

The macroscopic characterization of T. sanguinea ZHSJ, such as color, shape, and growth location of the wild fruiting bodies were first observed and their length, width, and height were measured and recorded. The mycelium was cultured in the dark at 28 ℃ on PDA for 14 days, during which the morphological and color changes of the mycelium were observed and recorded. The mycelium was subjected to Lactophenol Cotton Blue staining to analyze the structure of the mycelium and spores using the 40× and 100× magnifying lenses of an upright microscope. Images of T. sanguinea ZHSJ were obtained by using scanning electron microscopy (SEM) (VEGA3, Tescan L TD., Czech Republic) to observe the morphology of hyphae and spores. The magnifications of the image were 2000× and 10,000×.

Genetical identification

Fungal DNA was extracted by using a Biospin Fungi Genomic DNA Extraction Kit (Hangzhou Bioer Technology Co., Ltd., China) following the manufacturer’s instructions. The 18S rRNA internal transcribed spacer regions (ITS) of the T. sanguinea ZHSJ was amplified by PCR using the universal primers 18SF (5’AACTTAAAGGAATTGACGGAAG 3’) and18SR (5’TCCGCAGGTTCACCTACGGA 3’) along with the ReadyMix™ Taq PCR mix with MgCl2 (Sigma Aldrich, USA). PCR reaction conditions: 98 ℃ pre-denaturation for 5 min, 98 ℃ denaturation for 15 s, annealing at 60–68 ℃ for 30 s, extension at 72 ℃ for 1 min, and these steps were repeated thirty times. Later 72 ℃ extensions for 10 min and stabilization at 4 ℃ for 10 min. Then the PCR products were recovered and by agarose gel electrophoresis with an AxyPrep DNA gel extraction kit (Axygen, USA) for sequencing. These sequences were aligned with the NCBI database, and the relevant sequences from GenBank were downloaded. The phylogenetic tree was constructed using the Neighbor-Joining method in MEGA11 software.

Optimization of T. sanguinea ZHSJ growth conditions

pH

The optimum pH that favors the growth of T. sanguinea ZHSJ was analyzed by adjusting the pH of Potato Dextrose Water (PDW) (Haibo Biotechnology Co., Ltd., Qingdao, China) (150 mL) and PDA plates with pH 4, 5, 6, 7 and 8 by using 1 M NaOH and 1 M HCl before autoclave. A 5 mm diameter mycelial disc from 7 days old T. sanguinea ZHSJ plate was cut and added to the PDW and placed at the center of the PDA plates. PDW cultures were incubated in shaking incubator 120 r/min, 28 ℃, PDA plates were incubated at 28 ℃. The mycelia grown in PDW for seven and fourteen days were centrifuged at 8000 g for 10 min and freeze-dried, and their weights were recorded. The mycelial diameter of the T. sanguinea ZHSJ grown on PDA was measured in 7 days.

Temperature

The optimum temperature for the growth of T. sanguinea ZHSJ was evaluated from the temperature ranging15, 20, 28, 30, and 37 ℃. The growth of T. sanguinea ZHSJ was analyzed by its growth on PDA and in PDW incubated at different temperatures for about 7–14 days. The diameter of the mycelium growth on PDA and the dry weight of the mycelium growth in PDW represents the optimum temperature for the growth of T. sanguinea ZHSJ.

Carbon source

The carbon source suitable for optimum growth of T. sanguinea ZHSJ has been analyzed by using several sugars in PDA and PDW, such as glucose, soluble starch, sucrose and maltose with different concentrations 0, 10, 15, 20, 25, 30 g/L. Optimum growth with reference to carbon source was performed in two ways one is by solid culturing and another is liquid culturing. The growth diameter of the mycelium was measured on solid medium and the dry weight was measured as the optimum growth index in the liquid medium. The growth of these carbon sources was compared with the PDA and PDW growth of T. sanguinea ZHSJ.

Nitrogen source

The nitrogen source for the optimum growth of T. sanguinea ZHSJ was analyzed by infusing PDA and PDW with several nitrogen sources such as NH4Cl, yeast extract, (NH4)2SO4 and peptone with several concentrations such as 0, 1, 2, 3, 4 and 5 g/L. The infused PDA and PDW were inoculated with a disc of freshly grown T. sanguinea ZHSJ and incubated at 28 ℃ for 7–14 days in shaking or normal incubator. The optimum nitrogen source was analyzed by the diameter of T. sanguinea ZHSJ grown on PDA and the dry weight of the mycelium grown in PDW.

Spore collection and viability analysis

Spore collection

The optimum growth conditions were stabilized and selected for the growth of T. sanguinea ZHSJ and spore collection was performed by using these optimum growth conditions to obtain the viable spores. Under suitable growth conditions, T. sanguinea ZHSJ preferentially carries out saprophytic growth, that is, mycelium expansion. When mycelium grows to fill the entire plate, T. sanguinea ZHSJ produces many spores due to nutrient exhaustion. Harvesting spores with a brush or scraper might damage these structures and alter the age composition of the spores, hence, the collection of conidia from T. sanguinea ZHSJ was performed according to the method of Jae Su Kim et al. (Kim et al., 2010) by using polysiloxane polyether copolymer (siloxane) (Aladdin, USA). Conidia on PDA plates were serially collected by washing the PDA plates with the low (0.01%) and high (0.08%) concentration of siloxane solution. First, 3 mL of a 0.01% (v/v) siloxane solution was added to the centrifuge tube containing three agar discs (10 mm diameter). Later, glass beads were added to the centrifuge tube, that facilitates in displacing the conidium. The tube was vortexed for 30 s to release maximum conidium from the three discs into the solution. The spore suspension was transferred to the new test tube and 3 mL of 0.08% (v/v) silicone solution was added to the centrifuge tube which contained the agar discs at the beginning. Then, the spore suspension was aspirated into a new test tube and 3 mL of 0.16% (v/v) siloxane solution was added to the centrifuge tube which contains agar discs at the beginning. Then, the spore suspension was filtered with 3 layers of 75 μm sterile gauze to remove the agar debris or mycelium. The collected spores were counted by using cytometer (400×) and the percentage of spores in each siloxane solution was calculated as (N0.01% or N0.08%)/(N0.01%+ N0.08%) ×100%, where N0.01% and N0.08% refer to the number of spores collected in 0.01% and 0.08% siloxane solutions. This experiment was repeated three times on the same day using different batches of cultures. The collected conidium suspension was freeze-dried, and the spore powder was stored in a 4 ℃ refrigerator for subsequent experiments.

Spore viability test

The viability of spores was tested by suspending the lyophilized spores 0.1 mg in 1 mL of sterile distill water and inoculate into 10 mL of PDW and incubated for 0, 6, 12 and 24 h. Results were calculated by testing the OD values at 540 nm as mean value calculated by standard deviation. Subsequently, 100 µL of the spore suspension after 24 h was inoculated onto the PDA plate and incubated at 28 ℃ for 7–14 days.

Spore germination test

The lyophilized spore powder was suspended in sterile distilled water with concentration 1 × 107 CFU/mL and inoculated into 5 mL PDW and incubated in shaking incubator at 28 ℃ and 180 rpm. The germination of the spore was observed in specific time intervals such as 0, 6, 12, and 24 h by using SEM.

Extraction method

The samples spores (SP) that were produced by above optimized conditions were collected by above said method that is by growing mycelium on plate and then separation of spores from the mycelium by above said method, mycelium (MY) grown in PDW was separated by centrifuging the liquid culture at 8000 g for 10 min and fruiting body (MU)) of T. sanguinea ZHSJ and each weighing 20 ± 5 mg were transferred into 2 ml centrifuge tube and 6 mm diameter grinding beads were added and 400 µL of extraction solution (methanol: water = 4:1 (v: v) containing four internal standards 0.02 mg/ml L-2- chlorophenyl alanine, etc.) were added and frozen in six replicates each25. Then, the frozen tissues were grinded for 6 min at −10 ℃, 50 Hz and followed by ultrasonic extraction (SCIENTZ-IID, Xinzhi Biotechnology Co., Ltd., Ningbo, China) for 30 min at 5 ℃, 40 kHz. Then, sample was allowed to stand for 30 min − 20 ℃ and centrifuged at 13000x g, 4 ℃ for 15 min. Then, supernatant was transferred to a vial and stored for further analysis. Then 20 µL of each sample was transferred separately and mixed where this serves as quality control sample (QC).

UHPLC MS/MS analysis for untargeted metabolomics

T. sanguinea ZHSJ SP, MY and MU extracts of were analyzed by LC-MS through using UHPLC Exploris 480 system. The chromatographic column ACQUITY UPLC HSST3 (100 mm × 2.1 mm i.d, 1.8 μm; water, Milford, USA) was used. Here, mobile phase A is 95% water and 5% acetonitrile (containing 0.1% formic acid), mobile phase B is 47.5% acetonitrile, 47.5% isopropanol and 5% water (containing 0.1% formic acid) were used for chromatographic conditions. Whereas, for mass spectrometry conditions, samples were ionized by electrospray ionization and mass spectral signals (ESI-MS) were collected in both positive and negative ion scanning modes with the dynamic exclusion and mass scan range from 100 to 1500 m/z at a scan rate of 40 Hz. Mass spectrophotometry parameters were represented in the following table (Table 1).

Table 1 Mass spectrophotometry parameters.
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Data acquisition and processing

The raw data was imported into the metabolomics processing software Progenesis QI v3.0 (Waters Corporation, Milford, USA) for baseline filtering, peak detection, integration, retention time correction, and peak alignment, ultimately resulting in a data matrix containing information such as retention time, mass-to-charge ratio, and peak intensity. Subsequently, this software was used for feature peak library identification, matching MS and MS/MS spectral information with metabolite databases. The MS mass error was set to less than 10 ppm, and metabolites were identified based on the scoring of secondary mass spectrum matching. The main databases used were mainstream public databases such as http://www.hmdb.ca/ and https://metlin.scripps.edu/, as well as a self-built database. The raw data obtained by LC-MS analysis were pre-processed and corrected by further by several metabolomics softwares. Principal component analysis (PCA) and differential metabolite analysis- multivariate analysis by ropls (R packages) v1.6.2. ropls source: R/ropls-package. R. R Cluster analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) by scipy (python) v1.0.0.0. SciPy 1.0.0 Release Notes — SciPy v1.0.0 Reference Guide. Variable projection importance (VIP) analysis by ropls (R) and scipy (python) v1.6.2 and v1.0.0 and Venn pictures by Venn diagram (R packages) CRAN: Package VennDiagram.

Result

Morphological and genetical identification of T. sanguinea ZHSJ

The fruiting body of the T. sanguinea ZHSJ polypore grows in overlapping layers or as a single layer. The cap is fan-shaped, with a smooth surface and concentric rings, the edge is light orange, and wavy, the flesh is pale pink and corky (Fig. 2A). The mycelium of T. sanguinea ZHSJ was initially white, velvety, and had a ring-like pattern on PDA, after 5 days of incubation, some part of the mycelium turns orange. The morphology of mycelium represents obvious clamp connection structure, which is a typical feature of Basidiomycota fungi. The spores were observed elliptical under the oil lens and cylindrical under the SEM, with a relatively smooth surface, the size was about 1 × 4 μm, and the color is white (Fig. 2A–D). By using the BLAST program to align Genbank sequences, T. sanguinea ZHSJ and T sanguinea SDBR-CMUNK0189 (GenBank accession number: MW267631.1) and T. sanguinea WRF-5 (GenBank accession number: MT340981.1) were clustered into one phylogenetic branch, with a bootstrap value of 69 (Fig. 2E). Based on the combination of morphological identification and molecular identification, the strain was identified as Trametes sanguinea.

Fig. 2
Fig. 2The alternative text for this image may have been generated using AI.
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Morphological and genetical identification of T. sanguinea ZHSJ. (A) Fruiting body of T. sanguinea ZHSJ. (B) The mycelium of T. sanguinea ZHSJ growing on PDA upward and backward images. (C) Microscopic observation of hyphae and spore structure at 40 × and 100 ×. (D) SEM observation of hyphae and spore structure at 2000 × and 10,000 ×. (E) The phylogenetic tree of T. sanguinea ZHSJ.

Optimization results of T. sanguinea ZHSJ spore growth conditions

Optimum pH

The optimum pH was analyzed by culturing T. sanguinea ZHSJ spores in different pH such as 4, 5, 6, 7 and 8. However, T sanguinea ZHSJ showed optimal growth at pH 5, the colony diameter was 7.4 cm which is comparatively higher than other pH (Fig. 3A). Moreover, T. sanguinea ZHSJ grown in liquid medium, its mycelium dry weight at pH 5 represented 5.15 g/l, which is comparatively higher than the other pH (Fig. 3C). Hence, pH 5 was represented as the optimum pH for T. sanguinea ZHSJ.

Optimum temperature

The optimum temperature of T. sanguinea ZHSJ spores was analyzed by culturing the fungi in several temperatures such as 15 ℃, 20 ℃, 28 ℃, 30 ℃ and 37 ℃ in both PDA and PDW and adjusted pH of the culture medium to 5 at the same time. The optimum growth of the mycelium has been observed at the temperature 30 ℃ with the diameter of the mycelium was 8.3 cm and there was a dense orange colored mycelium (Fig. 3B). Simultaneously, the mycelium grown in liquid culture at temperature 30 ℃ represented more dry weight 6.73 g/l compared to other temperatures, hence 30 ℃ was represented as the optimum temperature for the growth of T. sanguinea ZHSJ (Fig. 3C).

Effect of carbon sources

Optimization of carbon sources was carried out by adding different carbohydrates to PDA and altering the concentration. However, among the four carbon sources, glucose, soluble starch, sucrose and maltose evaluated in this experiment, the mycelium of T. sanguinea ZHSJ showed optimum growth with maltose, its diameter was measured 8.27 cm which is highest when compared to others (Fig. 3D). Additionally, the liquid culturing of T. sanguinea ZHSJ with maltose concentration 20 g/L evinced highest dry weight 12.08 g/l, when compared to others of the Hence, maltose is referred to as the optimal carbon source for optimum growth of T. sanguinea ZHSJ. Other substrates, such as soluble starch, sucrose, and glucose, are ranked in descending order (Fig. 3F).

Effect of nitrogen sources

The optimum nitrogen source was identified for the growth of T. sanguinea ZHSJ was by adding several nitrogen sources such as NH4Cl, yeast extract, (NH4)2SO4 and peptone with varied concentrations such as 0, 1, 2, 3, 4 and 5 g/L to the basal medium. However, T. sanguinea ZHSJ possessed optimum growth with 4 g/L of yeast extract. On PDA T. sanguinea ZHSJ evidenced 8.3 cm diameter which is the among all the nitrogen sources and their concentrations (Fig. 3E). Moreover, the dry weight of the mycelium was 6.6 g/l with 4 g/L of yeast extract which is highest among all the nitrogen sources and their concentrations. Hence, yeast extract was represented as the optimum nitrogen source for the growth of T. sanguinea ZHSJ (Fig. 3G).

Fig. 3
Fig. 3The alternative text for this image may have been generated using AI.
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Optimum the conditions of T. sanguinea ZHSJ spores. (A) Growth of T. sanguinea ZHSJ under different pH conditions on PDA. (B) Growth of T. sanguinea ZHSJ under different temperature conditions on PDA. (C) The dry weight of the mycelium of T. sanguinea ZHSJ cultured for 14 days under different pH and temperature conditions. (D) Growth of T. sanguinea ZHSJ under different carbon source conditions on PDA. (E) Growth of T. sanguinea ZHSJ under different nitrogen source conditions on PDA. (F) The dry weight of the mycelium of T. sanguinea ZHSJ cultured for 14 days under different carbon source conditions. (G) The dry weight of the mycelium of T. sanguinea ZHSJ cultured for 14 days under different nitrogen source conditions.

Spore viability and germination

To analyze the viability of the spores collected from the above optimum growth conditions siloxane-mediated method was used. The lyophilized spores collected by siloxane-mediated method adjusted to 1 × 107 CFU/mL and subjected to spore germination test, OD values at OD540 were measured for every 6 h and the corresponding spore samples were photographed simultaneously by SEM to observe the process of spore germination. The results showed that from 6 h there is an increase in the OD values (Fig. 4A) and the germination tube of the spore has been increased (Fig. 4B). Further to confirm the viability of the spores, the spore suspension after 24 h of incubation 50 µL has been Inoculated on to the PDA plate and subjected for incubation at 28 ℃ and the growth was observed for 3 and 12 days (Fig. 4C).

Fig. 4
Fig. 4The alternative text for this image may have been generated using AI.
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Spore viability and germination test. (A) The absorbance value of T. sanguinea ZHSJ at OD540 nm. (B) Observe the spore germination status using SEM. (C) Growth status of spore suspension on PDA plate.

LC-MS analysis and metabolite variations among three stages of T. sanguinea ZHSJ

The morphological stages of T. sanguinea ZHSJ such as SP, MY and MU were analyzed for the metabolite variations by conducting multivariate analysis. The compounds in methanol extract were analyzed by LC-MS and the data preprocessing identified 3429 positive ions and 3286 negative ions (Fig. 5A) in total 6715 metabolites and these metabolites were analyzed by unsupervised statistical analysis PCA represents the variation of the overall metabolic differences between the samples in each group and the magnitude of variation between the samples was analyzed, here, the QC samples were also analyzed. The PCA score plot represents R2X (cum) = 0.671and the metabolite variance of spores, mycelium and fruiting body were separated by PC1 represented 67.10% and PC2 represented 24.60% (Fig. 5B). Further, partial least squares discriminant analysis (PLS-DA) which is supervised discriminant analysis reveal the metabolites responsible for discrimination in these three morphological stages represents R2X (cum) = 0.923. R2Y (cum) = 0.698 and Q2 (cum) = 0.689 (Fig. 5C). Moreover, the Venn plot represents the common and unique metabolites of SP, MY and MU of T. sanguinea ZHSJ (Fig. 5D).

Fig. 5
Fig. 5The alternative text for this image may have been generated using AI.
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LC-MS analysis and metabolite variation. (A) LC-MS analysis, total ion chromatography (TIC) of QC sample depicts the intensities of all ions in the mass spectrum in both positive and negative modes, each component metabolite was represented as peaks at corresponding retention times. (B) PCA score chart represents the separation trend between the groups, when the sample analyzed through dimensionality reduction the relative coordinate point on the PC1 and PC2 and the distance between the coordinates represent the degree of aggregation and dispersion between the samples. (C) PLS-DA score chart represents the significance in separation of two samples, here, component 1 represents the PC1 interpretation and component 2 represents PC2 interpretation. The greater the distance between the samples the more significant is the classification effect. (D) Venn diagram represents the common and different metabolites of each morphological stage of T. sanguinea ZHSJ.

Metabolic variations among three groups

The average ion intensities of three groups represented 13 categories and each category represented significant variation of metabolites (Fig. 6). Among them lipid and lipid like molecules (fatty acids), organic acids (amino acids, peptides and analogues) and organoheterocyclic compounds (nucleic acids) compounds represented highest intensities in all the groups. However, there was a significant metabolite variation among these categories in each group and the variant metabolites of each group were mentioned in (Table S1, S2 and S3).

Fig. 6
Fig. 6The alternative text for this image may have been generated using AI.
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Metabolic variations. The metabolite variation of the compared groups (MY vs. MU, SP vs. MY and SP vs. MU) has been represented based on the number of metabolites in each category. The different colors represent different categories of metabolites.

Differential metabolite cluster and VIP analysis

The differential metabolite analysis refers to the unique and commonly expressed metabolites that are important in metabolic pathways and biological functions between the groups such as SP vs. MU, SP vs. MY and MY vs. MU of T. sanguinea ZHSJ (Fig. 7A). Here, in comparison, one on the left represents the experimental group and one on the right represents as the control. Further, differential metabolite analysis also represents the upregulated and downregulated metabolites in comparison of each group, student’s t- test with two tailed inspection referred P value < 0.05 and VIP_Pred_OPLS-DA > 1, that represents the metabolites in the classification and discrimination of samples in each group in the model were significantly different. (Fig. 7B). Furthermore, metabolite cluster analysis represents the specific metabolite pattern of expression and their hierarchical clustering among 10 subclusters with top 50 expressed metabolites in each metabolic set i.e. SP vs. MU, SP vs. MY and MY vs. MU (Fig. 7C). Moreover, VIP value of first principal component that influence the expression pattern of biologically significant top 30 metabolites their classification and discrimination in each group were tested and predicted through 7- fold cross validation and significance was represented with VIP ≥ 1 (Fig. 7D).

Fig. 7
Fig. 7The alternative text for this image may have been generated using AI.
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Differential metabolite analysis. (A) Venn diagram represents the common and differential metabolites of different morphological group comparisons. (B) The volcano plot represents the differential expression of metabolites. Here, Log2FC is the statistical test value of the difference in the expression of metabolites of -log10 (p value). Each dot represents a specific metabolite, where blue dots represent significant down regulated metabolites, red dot represents significantly upregulated metabolites and gray dots represent non-significant differential metabolites and the size of the dot represents its VIP value. (C) The metabolite hierarchical heat maps represent the differentially expressed metabolites in different groups. Here, each column represents sample and each row represents metabolite and as per the color scale the redder color represents the upregulation of metabolites and bluer represents the downregulation of metabolites and the tree map represents the significant hierarchical relationship between the metabolite’s expression. (D) VIP bubble chart represents the expression pattern of each metabolite. Here, Y- axis represents the metabolites and X- axis represents the VIP values. The red color represents the upregulation and blue color represents the down regulation of metabolite in a particular morphological stage.

KEGG pathway enrichment analysis

The differentially expressed metabolites of group SP vs. MU, SP vs. MY and MY vs. MU possess a specific function. KEGG pathway enrichment analysis represents the functions of the metabolites and their specific pathway. Here, SP vs. MU and SP vs. MY represented the highest number of metabolites, 23 and 31 involved in biosynthesis of co factors, whereas MY vs. MU represented highest number of metabolites involved in diterpenoid biosynthesis (Fig. 8A). Furthermore, the network between these metabolites involved in KEGG pathways has been represented in (Fig. 8B).

Fig. 8
Fig. 8The alternative text for this image may have been generated using AI.
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KEGG enrichment pathway analysis and network of metabolites. (A) KEGG pathway enrichment analysis represents the number of metabolites involved in a group in a particular pathway. Here, the size of the bubble represents the number of metabolites involved and the color represents their significance by p value. (B) KEGG enrichment network diagram represents the pathways and the metabolites involved in specific pathway and their interconnection with other pathways. Here, the green squares represent metabolites and orange circles represent pathways, the more metabolites involved in a pathway the more is the size of the circle26,27,28.

Discussion

T. sanguinea possesses several valuable medicinal properties and is an important species used in traditional Chinese medicine29. However, the research on the spores of T. sanguinea is very little and its viability analysis was not studied. To our knowledge, this study provides the first insights into the T. sanguinea spore identification and collection through a comprehensive optimization approach and study of differentially expressed metabolites in morphological stages of T. sanguinea ZHSJ such as spore, mycelium and fruiting body through LC-MS based untargeted metabolomics study. The current study revealed that there are 4098 common metabolites among all the compared groups, whereas there were 124 unique metabolites present only in spores and 154 unique metabolites present only in mycelium and 252 metabolites present only in fruiting body of T. sanguinea ZHSJ.

According to previous studies, temperature and pH are key factors in fungal growth; hence, growth optimization of the isolated T. sanguinea ZHSJ was carried out in the present research30. The growth condition of fungi was investigated under different pH conditions, and the growth rate and hyphal state were greatly related to the type of fungi and the pH value of 4.5–5.5 was the most suitable for the production of enzyme such as laccase by fungal fermentation, which consistent with many literature reports, and the change of pH value was related to the nature of the substrate and the reaction mechanism31,32,33. It is unlikely that all fungi lack growth at higher culture temperatures because of the low moisture levels, as water evaporates during the growth of fungi34. The data on temperature are consistent with the results of most wood-rotting basidiomycetes which the optimum growth temperature reported is between 20 ℃ and 30 ℃, and no fungi can grow at 40 ℃, only five species being able to grow at 35 ℃35. In temperate regions, the optimum growth temperature for wood-rot fungi is between 25 ℃ and 30 ℃36,37. The T. sanguinea ZHSJ used in this study was collected from Shandong Province, China, which is in the temperate zone. According to relevant experimental reports, fungi prefer glucose over other carbon compounds because glucose can be rapidly metabolized to produce cellular energy38 and the nitrogen source is peptone39,40. Most of the research on the carbon and nitrogen sources of T. sanguinea ZHSJ focuses on the production and activity of laccase. For example, T. versicolor showed the highest laccase activity after the addition of glycerol, followed by sucrose and glucose41. When corn steep liquor was used as a nitrogen source to produce laccases by T. versicolor, the laccase activity increased by 1.96 times compared to the control medium lacking corn steep liquor42. Whereas, for T. pubescens, the carbon source that reaches the maximum yield of laccase is maltose, and the nitrogen source is yeast extract, the carbon-to-nitrogen ratio is 5.15:1, which is consistent with the culture results of T. sanguinea ZHSJ43.

Trametes species possess several medicinal important compounds that possess several activities such as antimicrobial, antitumor, antioxidant, anti-inflammatory and immunomodulatory effects44. Further, our recent research article on whole genome sequencing of T. sanguinea ZHSJ of fruiting body represented the annotation and transcriptomic analysis, provided a base study for the current metabolomic study of T. sanguinea ZHSJ45. However, metabolomics study of co-cultured Trametes versicolor and Ganoderma applanatum represented varied production of metabolites when compared to individual culturing46. Metabolomic differential analysis of interspecific interactions of Trametes versicolor, Pleurotus ostreatus and Dichomitus squalens represented differential metabolites in the interaction zone and metabolites such as carnitine, lipid, ethylene and trehalose were significantly upregulated47. Moreover, metabolomics and molecular network analysis of co-cultured Trametes robiniophila Murr and Pleurotus ostreatus revealed 44 features that were newly produced and significantly high and among them 6 features belong to new family of novel and unusual linear sesterterpenes which possess high antifungal activity48. Moreover, metabolomic analysis of brown rot fungi (Rhodonia placenta and Gloeophylum trabeum) and white rot fungi (Trametes versicolor and Pleurotus ostreatus) represented significant metabolite variations and white rot fungi has a potential biomarker compound galactitol was identified and white rot fungi were efficient at catabolizing phenolic compounds of wood49. However, the current study revealed the metabolic variations of three morphological stages (spores, mycelium and fruiting body) of T. sanguinea ZHSJ and achieved spore isolation by utilizing several optimization conditions. Further, studies on these differentially expressed metabolites of spores would lead to identification of potential bioactive compound.

Conclusion

In conclusion, we have achieved isolation of T. sanguinea ZHSJ spores by optimizing its growth conditions and then we identified several metabolites that are differentially expressed in spores when compared to mycelium and fruiting body. It was identified that there are 124 metabolites that were peculiar in spore extraction and among them significant number of metabolites belong to organic acids and derivatives, lipids and lipids like molecules and organoheterocyclic compounds. Hence, future studies on biological activities such as antitumor, antioxidant, antimicrobial and anti-inflammatory activities of these metabolites from spores would benefit in the development of novel drugs.