Comparative Analysis of Metabolic Compositions and Trace Elements of Inonotus hispidus Mushroom Grown on Five Different Tree Species.

Inonotus hispidus is a popular edible and medicinal mushroom widely used in China. I. hispidus mushroom mainly grows on five different tree species (Morus alba L., Ulmus macrocarpa Hance, Fraxinus mandshurica Rupr., Ziziphus jujuba Mill., and Malus pumila Mill.), and their fruiting bodies were all separately used in the market. However, there is no holistic insight to elucidate the molecular basis of the differentiated usage. This study aimed to investigate and compare the metabolite compositions and trace elements in I. hispidus grown on five different tree species. The metabolomic data, 8 kinds of principal components and 12 kinds of trace elements, were analyzed in this study. The results showed that the same 1353 metabolites were identified in I. hispidus grown on five different tree species, but the relative abundance was different. The principal components and trace elements contents are different, for example, polysaccharides, phenol metabolites, Ca, Na, Mg, Fe, and Mn were enriched in I. hispidus grown on M. alba, the flavonoids were enriched in Z. jujuba samples, and the steroids, terpenoids, and Zn were enriched in M. pumila samples. Further, the KEGG enrichment pathway and metabolic models were established. These findings provide a molecular basis for the unique use of the I. hispidus mushroom grown on different tree species.

Introduction

Edible and medicinal mushrooms, in the human diet and traditional medicine, have a long history.[1]Inonotus hispidus (Bull.: Fr.) P. Karst. is an edible and medicinal mushroom, which is widely used as a health care product and ancient medicinal material in east Asian countries, especially China.[2] Previous chemical studies showed that it produced a series of active compounds such as polysaccharides, polyphenols, triterpenes, sterols, and melanin.[1,3,4] Recent investigations have revealed that fruiting body extracts of I. hispidus have antitumor,[5,6] antiviral,[7] immunomodulatory,[8] antioxidant,[9] and hypolipidemic activities.[10] The I. hispidus mushroom has great application value and development potential in the field of functional food and medicine in the future. Through investigation, we find out that I. hispidus mushroom mainly grows on Morus alba L., Ulmus macrocarpa Hance, Fraxinus mandshurica Rupr., Ziziphus jujuba Mill., and Malus pumila Mill. The appearance of these fruiting bodies on different tree species looks similar, so it is not easy to distinguish them by macroscopic morphological features.

So far, researchers have predominantly focused on quantitative and qualitative analysis of steroids’ and phenols’ chemical components and antitumor effects of the fruiting bodies.[1] There are no holistic insights into the molecular basis of the differentiated chemical components and usage of I. hispidus grown on different tree species, and producers, traders, consumers, researchers, and regulators have all confused it. We hypothesized that the metabonomic characteristics of different tree species of I. hispidus were different and could be distinguished by their chemical composition. Therefore, it is urgent work to identify the types and enrichment degree of chemical metabolites in the fruiting bodies on different tree species. This study was also carried out and implemented for this purpose.

The primary metabolites of fungi refer to the substances produced by fungi through metabolic activities, which are necessary for their growth and reproduction, such as sugars, amino acids, common fatty acids, nucleic acids, and polymers formed by them. The secondary metabolites of fungi refer to all kinds of compounds with complex structures synthesized by the complex secondary metabolic pathway before and after the growth of some fungi to the stable stage, whereas the primary metabolites refer to compounds with a simple structure, a clear metabolic pathway, and high yield as precursors. There are many kinds of secondary metabolites, which are closely related to human medical products and health care, such as peptides, steroids, alkaloids, and so on. In recent years, the identification and analysis of primary and secondary metabolites of life by means of metabolomics have been widely used. Metabolomics is a new discipline that has developed rapidly, following genomics, transcriptomics, and proteomics, and has been widely used in many fields, such as animal and plant metabolism, microbial metabolism, disease diagnosis, and drug development.[11−13] In recent years, metabolomics technology has been gradually applied to the field of edible and medicinal mushrooms to study metabolic profiling.[14,15] It was reported that nontargeted metabonomic methods have been used to distinguish the metabolite composition of different parts of Ganoderma lucidum and different growth and development stages of Pleurotus tuoliensis.[16] The available software, algorithms, and experimental methods have now made metabonomics a good tool for comparing the chemical metabolites of fruiting bodies on different tree species.

I. hispidus has great development potential as edible and medicine mushroom resources in the future, and this study aims to figure out the differences between its chemical composition when grown on five species of different trees using the nontargeted metabolomics method for analysis. In addition, the chemometrics data of eight kinds of principal components and 12 kinds of trace elements of different tree species were analyzed, and the fruiting bodies of edible mushroom I. hispidus on different tree species were specifically identified. These metabolites may provide new ideas for comprehensive evaluation of the medicinal value and provide important theoretical support for the development of functional products and elucidation of their different pharmacological activities.

Results

Principal Component Analysis of Different Metabolites in I. hispidus Grown on Five Different Tree Species

In this study, we collected I. hispidus from five different tree species from Shandong, Shanxi, and Jilin provinces in China. As can be seen from Figure , the contents of chemical metabolite composition in I. hispidus grown on five different tree species were analyzed using ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC–MS/MS) technique and the nontargeted metabolomics method (the total ion flow diagram of the sample under the negative and positive ion modes is shown in Tables S2–S72). The fruiting bodies of I. hispidus mushroom grown on five different tree species are shown in Figure A. The results showed that a total of 1353 chemical metabolites have been identified (as shown in Supporting Information Tables S73–S98), and there are no specific chemical metabolites in I. hispidus grown on M. alba L. (MA), I. hispidus grown on U. macrocarpa var. mongolica (UM), I. hispidus grown on F. mandshurica (FM), I. hispidus grown on Z. jujuba (ZJ), and I. hispidus grown on M. pumila (MP) (Figure B). To characterize the overall metabolic differences among the different samples, principal component analysis (PCA) was used to study the metabolic differences among I. hispidus fruiting bodies of different tree species. First of all, unsupervised PCA is used to evaluate the overall distribution of all samples and the stability of the whole analysis process. As shown in (Figure C,D), all the quality control samples are clustered together, showing good analytical stability and experimental reproducibility. It is worth noting that UM and FM samples are relatively close on the PCA map, indicating that the difference of metabolites between UM and FM is relatively small. Similarly, ZJ and MP samples are relatively close on the PCA map, indicating that the difference in metabolites between ZJ and MP samples is relatively small. However, the metabolites of MA were quite different from those of the other four kinds of I. hispidus samples. The differences in their metabolites are mainly reflected in abundance rather than species.

Figure 1

Map of China with indicated sampling areas of I. hispidus grown on five different tree species. The triangles of different colors represent different locations of the sample collection.

Figure 2

I. hispidus on five different tree species and PCA score chart of metabolite distribution. (A) I. hispidus grown on U. macrocarpa var. mongolica (UM), I. hispidus grown on F. mandshurica (FM), I. hispidus grown on Z. jujuba (ZJ), I. hispidus grown on M. pumila (MP), and I. hispidus grown on M. alba L. (MA). (B) Venn diagrams for comparison of metabolites in I. hispidus on five different tree species. (C,D) PCA score chart in the positive ion mode and negative ion mode. Quality control samples (QC).

Partial Least Square Discriminant Analysis

To determine the metabolic differences among I. hispidus samples grown on five different tree species, the supervised partial least square discriminant analysis (PLS-DA) model was used to further optimize the population separation of fruiting bodies. The comparison between paired MA and UM fruiting bodies samples using PLS-DA showed that there were significant differences in the metabolism among different categories in each pairwise comparison of the first component (Figure A,C,E,G). The partial least squares model has high R2Y and Q2 values, a good fitting degree, and satisfactory prediction ability. The R2 intercept of fruiting bodies UM and MA samples is 0.87, the R2 intercept of FM and MA samples is 0.89, the R2 intercept of ZJ and MA samples is 0.95, and the R2 intercept of MP and MA samples is 0.96, while the Q2 intercepts are −0.71, −0.70, −0.67, and −0.66, respectively (Figure B,D,F,H). It shows that the partial least squares model is credible without overfitting. Variable importance in projection (VIP) values were used to identify the differential metabolites between samples and were confirmed by a nonparametric Mann–Whitney U test.

Figure 3

Score plots for the metabolites of UHPLC–MS/MS data. (A) PLS-DA score plots from metabolite profiles for the fruiting bodies of UM and MA samples. (B) 200 times permutation test of PLS-DA models for (A). (C) PLS-DA score plots from metabolite profiles for the fruiting bodies of FM and MA samples. (D) 200 times permutation test of PLS-DA models for (C). (E) PLS-DA score plots from metabolite profiles for the fruiting bodies of ZJ and MA samples. (F) 200 times permutation test of PLS-DA models for (E). (G) PLS-DA score plots from metabolite profiles for the fruiting bodies of MP and MA samples. (H) 200 times permutation test of PLS-DA models for (G).

Comparative Analysis of Differential Metabolites

The results showed that a total of 1353 metabolites were identified in the fruiting bodies of I. hispidus grown on five different tree species, and the concentration of metabolites significantly changed. The expression profiling changes of MA, MU, FM, ZJ, and MP samples are shown in Figure . Compared with MA samples, 49 metabolites in UM samples were downregulated and 149 metabolites were upregulated. Compared with MA samples, 327 metabolites in FM samples were downregulated and 107 metabolites were upregulated. Compared with MA samples, 164 metabolites were downregulated and 133 metabolites were upregulated in ZJ samples. Compared with MA samples, 156 metabolites in MP samples were downregulated and 92 metabolites were upregulated (as shown in Table ). Through the Venn diagram analysis of different groups of differential metabolites, the overlap and unique differential metabolites between different groups were intuitively compared and are presented, showing the relationship between multiple groups of differential metabolites. In this study, the Venn diagram analysis of the differential metabolites of the four comparative combinations is shown in Figure . The data include UM versus MA (see Supporting Information Tables S99–S107), FM versus MA (see Supporting Information Tables S108–S116), ZJ versus MA (see Supporting Information Tables S117–S122), and MP versus MA (see Supporting Information Tables S123–S127). In addition, the chemical metabolism profiles of different samples (MA, UM, FM, ZJ, and MP) were analyzed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and lipid database (LIPID MAPS) in this study. A total of 187 chemical metabolites were identified and classified, including 19 sugars, 42 glycosides, 12 amino acids, 8 steroids, 22 phenols, 24 flavonoids, 11 terpenes, 30 nucleotides, 7 pyrimidines, and 12 purines. The chemical metabolites listed above are the main pharmacological active compounds in I. hispidus. Therefore, this paper mainly discusses these metabolites.

Figure 4

Expression profiling changes of MA, MU, FM, ZJ, and MP samples. (A,B) Volcano plot indicating upregulated and downregulated metabolites in the positive ion mode and negative ion mode. (C,D) Heat map showing hierarchical clustering of MA, MU, FM, ZJ, and MP samples in the positive ion mode and negative ion mode. Each fruiting bodies sample is visualized in a single column, and each metabolite is represented by a single row. Blue colors indicate lower metabolite concentration, while red colors show enhanced metabolite levels.

Table 1

Identification Results of Differential Metabolites

	UM vs MA	FM vs MA	ZJ vs MA	MP vs MA
upregulated	125	107	133	92
downregulated	322	327	164	156

Figure 5

Venn diagram analysis of the differential metabolites of the four comparative combinations (A) under the positive ion mode and (B) under the negative ion mode.

Analysis of the Characteristics of Sugars and Glycosides in I. hispidus Grown on Five Different Trees Species

Recent investigations have revealed that sugars and glycosides are important bioactive components in mushrooms, such as G. lucidum polysaccharides having antibacterial,[17] antitumor,[18] immunomodulatory,[19] and inflammation effects.[20] In this study, 19 sugars and 42 glycoside metabolites were identified in I. hispidus grown on five different tree species (Table ). The contents of sugars differ greatly among them, and the contents of cyclic adenosine diphosphate (ADP)-ribose, trehalose, fucose, and iditol in MA were higher than that in other I. hispidus samples. The contents of fructose and fructose 6-phosphate in UM were higher than those in other I. hispidus samples. The contents of lactose, maltotriose, glucosamine, lyxose, deoxyribose 5-phosphate, fructose, and arabinose in FM were higher than those in others. The contents of fructose and fructose 6-phosphate in ZJ samples were higher than those in other samples. The contents of arabitol, mannitol, threose, trehalose-6-phosphate, and xylitol were higher than those in the other samples. 42 glycosides and their derivatives were isolated among them, including deoxyguanosine, methyl-β-d-galactopyranoside, soyasaponin I, guanosine, pyridoxine O-glucoside, and so on. The results showed that the glycosides were different in the samples grown on five different tree species and that this may be related to parasitic tree species, but the cause and mechanism of these differences are not clear.

Table 2

Summary of Sugars and Glycosides Identified in I. hispidusa

compound class	no.	name	molecular mass	RT [min]	m/z	MA/MA	UM/MA	FM/MA	ZJ/MA	MP/MA
sugars	1	arabitol	152.07	1.31	153.08	1.00	1.34	3.41	1.25	9.85
	2	mannitol	182.08	1.28	181.07	1.00	0.05	0.73	1.05	2.74
	3	threose	120.04	7.89	119.03	1.00	1.04	1.08	1.16	2.24
	4	tagatose	180.06	16.29	181.07	1.00	0.81	0.76	2.69	1.91
	5	lactose	342.12	1.52	343.12	1.00	5.64	6.28	0.91	1.84
	6	maltotriose	504.17	1.33	505.18	1.00	0.98	2.11	0.93	1.76
	7	trehalose-6-phosphate	422.08	1.52	423.09	1.00	0.00	0.00	0.94	1.41
	8	xylitol	152.07	0.39	151.06	1.00	0.29	0.14	0.34	1.09
	9	glucosamine	179.08	1.32	214.05	1.00	0.92	5.91	0.80	0.92
	10	fructose 1,6-bisphosphate	340.00	1.17	338.99	1.00	2.05	1.49	1.12	0.82
	11	fructose 6-phosphate	260.03	1.33	261.04	1.00	6.94	1.81	1.14	0.78
	12	lyxose	150.05	1.29	149.05	1.00	1.22	1.36	1.06	0.73
	13	deoxyribose 5-phosphate	214.02	1.43	215.03	1.00	2.13	6.35	0.21	0.67
	14	cyclic ADP-ribose	541.06	1.73	542.07	1.00	0.36	0.26	0.68	0.61
	15	trehalose	342.12	1.26	341.11	1.00	0.81	0.60	0.96	0.48
	16	fucose	164.07	1.30	165.08	1.00	0.24	0.45	0.25	0.33
	17	fructose	180.06	1.32	179.06	1.00	0.21	1.09	0.47	0.31
	18	arabinose	150.05	1.58	151.06	1.00	0.50	1.07	0.80	0.28
	19	iditol	182.08	1.30	183.09	1.00	0.00	0.00	0.02	0.26
glycosides	20	thymidine	242.09	5.55	241.08	1.00	1.01	0.90	0.62	49.16
	21	inosine	268.08	1.39	307.04	1.00	0.76	1.04	1.72	9.36
	22	deoxyguanosine	267.10	9.62	268.10	1.00	1.53	0.42	2.38	5.55
	23	nicotinate ribonucleoside	255.07	1.25	300.07	1.00	1.52	3.02	1.45	3.42
	24	pyridoxine O-glucoside	331.13	1.43	330.12	1.00	0.62	0.59	1.56	1.94
	25	δ-ribono-1,4-lactone	148.04	1.25	147.03	1.00	6.92	3.41	0.75	1.74
	26	2′-O-methylguanosine	297.11	5.61	296.10	1.00	1.46	2.85	0.78	1.70
	27	deoxyinosine	252.08	1.30	297.08	1.00	1.22	0.59	12.79	1.61
	28	N7-methylguanosine	299.12	1.34	298.11	1.00	0.81	0.54	0.69	1.59
	29	N6-succinyl adenosine	383.11	5.92	384.12	1.00	1.79	1.30	0.87	1.58
	30	kinetin 9-riboside	347.12	1.44	346.11	1.00	0.90	0.54	21.42	1.49
	31	orotidine	288.06	1.32	287.05	1.00	0.87	0.88	0.91	1.40
	32	2′-deoxyadenosine	251.10	3.01	252.11	1.00	2.04	2.02	1.43	1.37
	33	N2,N2-dimethylguanosine	311.12	6.20	312.13	1.00	1.15	1.08	1.11	1.22
	34	4-acetyl-3-hydroxy-5-methylphenyl β-d-glucopyranoside	328.12	1.43	327.11	1.00	1.10	1.18	1.04	1.21
	35	1,5,8-trihydroxy-9-oxo-9H-xanthen-3-yl β-d-glucopyranoside	422.08	1.26	421.08	1.00	0.55	0.42	1.57	1.16
	36	guanosine	283.09	1.48	282.08	1.00	2.24	0.04	2.76	1.12
	37	N-2-hydroxycyclopentyladenosine	351.15	7.94	352.16	1.00	3.77	2.18	1.02	1.03
	38	2-(dimethylamino)guanosine	311.12	1.35	310.11	1.00	1.97	1.44	1.01	1.02
	39	N6-isopentenyladenosine	335.16	9.94	336.17	1.00	75.32	26.43	1.17	1.01
	40	adenosine	267.10	2.78	268.10	1.00	0.75	0.23	0.81	0.86
	41	5′-S-methyl-5′-thioadenosine	297.09	6.79	298.10	1.00	0.86	1.53	1.45	0.80
	42	cytidine	243.09	1.34	244.09	1.00	4.04	2.73	1.05	0.79
	43	lactitol	344.13	1.32	345.14	1.00	1.14	0.99	0.86	0.78
	44	prunasin	295.11	8.89	296.11	1.00	1.83	6.58	1.23	0.69
	45	xanthosine	284.07	1.25	283.07	1.00	0.96	1.80	1.64	0.68
	46	2-hydroxy-1-(4-methoxyphenyl)propyl hexopyranoside	344.15	9.30	343.14	1.00	33.10	1.78	0.73	0.67
	47	N4-acetylcytidine	285.10	1.43	284.09	1.00	0.39	0.35	0.89	0.63
	48	5-methyl-2′-deoxycytidine	241.11	2.02	242.11	1.00	1.32	1.56	0.69	0.59
	49	deoxyadenosine	251.10	1.52	252.11	1.00	2.38	1.13	2.18	0.59
	50	salicin	286.11	2.88	285.10	1.00	1.01	1.01	0.58	0.53
	51	8-bromoguanosine	361.00	12.89	360.00	1.00	0.47	0.33	0.80	0.48
	52	deoxycytidine	227.09	1.34	228.10	1.00	7.15	1.80	0.90	0.43
	53	uracil 1-β-d-arabinofuranoside	244.07	7.03	245.08	1.00	1.44	1.43	0.50	0.43
	54	soyasaponin I	942.52	12.46	941.51	1.00	0.48	0.38	0.48	0.41
	55	2′-O-methyladenosine	281.11	5.18	282.12	1.00	3.29	1.96	0.38	0.36
	56	2′-deoxyuridine	228.07	1.40	227.07	1.00	1.11	1.76	0.54	0.34
	57	methyl β-d-galactopyranoside	194.08	1.57	195.09	1.00	0.94	0.07	2.57	0.34
	58	2-deoxyuridine	228.07	6.49	229.08	1.00	0.15	0.19	0.14	0.22
	59	1-methylguanosine	297.11	1.48	298.11	1.00	0.19	0.22	0.28	0.22
	60	uridine	244.07	2.25	243.06	1.00	0.11	0.09	0.18	0.17
	61	cyclocytidine	261.05	14.66	260.04	1.00	1.17	7.07	-	-

-: ratio is less than 0.01; MA/MA: ratio of MA to MA, UM/MA: ratio of UM to MA, FM/MA: ratio of FM to MA, ZJ/MA: ratio of ZJ to MA, and MP/MA: ratio of MP to MA.

Analysis of the Characteristics of Amino Acids and Nucleotides in I. hispidus Samples Grown on Five Different Tree Species

Amino acids and nucleotides play an important role in the human body. Amino acids play an important role in the physiological function, especially the essential amino acids, which have extremely important physiological and nutritional significance in the human body and are also one of the important indicators of food nutritional value evaluation.[21] Nucleotides have many important biological functions, such as adenosine triphosphate related to the energy metabolism. In this study, 12 kinds of amino acids and 30 nucleotides were identified in five different I. hispidus mushroom samples (Table ). The contents of threonine, aspartic acid, and serine in MA were higher than those of others. The contents of methionine, phenylalanine, lysine, lysine, and phenylalanine in the fruiting bodies of UM were higher than those of others. The contents of serine in FM were higher than those of others. The contents of arginine, asparagine, tyrosine, proline and glutamic acid were enriched in ZJ samples. To sum up, there are great differences in the amino acid content of the samples, and the utilization of amino acids in them needs to be further studied. In addition, the contents of these chemical metabolites were significantly different in the fruiting bodies (P < 0.05), such as the contents of guanosine monophosphate (GMP), cyclic guanosine monophosphate (cGMP), riboflavin-5-phosphate, adenylosuccinic acid, thymidine 5′-diphosphate, uridine monophosphate, and uridylic acid (UMP) of MA being significantly higher than those of others.

Table 3

Summary of Amino Acid and Nucleotide Metabolites Identified in I. hispidusa

compound class	no.	name	molecular mass	RT [min]	m/z	MA/MA	UM/MA	FM/MA	ZJ/MA	MP/MA
amino acids	1	threonine	119.06	1.52	118.05	1.00	0.49	0.49	0.75	0.89
	2	arginine	174.11	1.41	175.12	1.00	0.26	0.73	1.21	1.04
	3	asparagine	132.05	1.31	133.06	1.00	0.64	0.46	3.79	0.56
	4	tyrosine	181.07	2.10	182.08	1.00	0.23	0.27	1.23	0.97
	5	aspartic acid	133.04	1.28	134.04	1.00	0.48	0.29	0.94	0.64
	6	serine	105.04	1.45	104.04	1.00	0.02	0.01	0.31	0.23
	7	methionine	149.05	1.50	150.06	1.00	7.66	6.34	0.49	0.19
	8	histidine	155.07	1.40	154.06	1.00	0.60	0.44	0.92	1.10
	9	proline	115.06	1.50	116.07	1.00	2.89	0.70	6.45	1.02
	10	phenylalanine	165.08	8.46	166.09	1.00	1.31	1.22	0.72	0.75
	11	glutamic acid	147.05	1.49	148.06	1.00	0.91	0.85	7.73	3.20
	12	lysine	146.11	1.34	147.11	1.00	3.00	0.71	1.14	0.84
nucleotides	13	GDP	443.02	2.19	444.03	1.00	2.23	0.46	20.20	3.60
	14	dAMP	331.07	1.37	330.06	1.00	1.13	0.60	13.81	1.44
	15	uridine 5′-diphospho-N-acetylgalactosamine	607.08	8.80	608.09	1.00	0.89	1.26	1.06	1.38
	16	uridine 5′-monophosphate	324.04	1.73	325.04	1.00	1.16	1.07	1.01	1.31
	17	ADP-ribose	559.07	1.42	558.07	1.00	0.81	0.78	0.86	1.25
	18	guanosine monophosphate (GMP)	363.06	1.39	362.05	1.00	0.72	0.71	0.72	0.90
	19	inosine 5′-monophosphate	348.05	7.57	349.06	1.00	1.33	1.45	0.87	0.90
	20	guanosine monophosphate	363.06	1.35	364.07	1.00	1.24	1.39	12.76	0.86
	21	cGMP	345.05	1.38	346.05	1.00	0.18	0.24	0.72	0.83
	22	riboflavin-5-phosphate	456.11	10.37	457.11	1.00	0.19	0.17	0.67	0.83
	23	cytidine-5′-monophosphate	323.05	1.35	324.06	1.00	0.96	1.97	1.03	0.79
	24	adenylosuccinic acid	463.07	5.88	464.08	1.00	0.63	0.68	0.71	0.78
	25	nicotinamide adenine dinucleotide (NAD+)	663.11	1.73	664.12	1.00	2.58	1.06	1.75	0.78
	26	adenosine diphosphate (ADP)	427.03	1.37	426.02	1.00	1.34	1.85	0.76	0.77
	27	β-nicotinamide mononucleotide	334.06	9.22	333.05	1.00	0.82	0.33	3.49	0.68
	28	flavin adenine dinucleotide (FAD)	785.16	6.86	784.15	1.00	110.81	4.61	0.95	0.68
	29	UDP-N-acetylglucosamine	607.08	1.28	606.08	1.00	1.74	1.67	0.91	0.67
	30	citicoline	488.11	9.86	489.12	1.00	1.27	1.07	10.43	0.66
	31	dUMP	308.04	16.56	309.05	1.00	1.34	1.20	0.90	0.66
	32	flavin adenine dinucleotide	785.15	7.63	786.16	1.00	52.76	4.40	0.82	0.61
	33	thymidine 5′-diphosphate	402.02	1.25	401.01	1.00	0.19	0.18	0.49	0.56
	34	3′-dephospho-CoA	687.15	6.01	342.57	1.00	1.30	1.87	0.46	0.52
	35	adenosine 5′-monophosphate	347.06	1.65	348.07	1.00	1.61	1.15	0.83	0.47
	36	uridine 5′-diphosphogalactose	566.06	1.27	565.05	1.00	0.48	0.46	1.68	0.46
	37	cAMP	329.05	5.81	328.05	1.00	4.93	3.26	0.47	0.41
	38	XMP	362.03	1.27	343.01	1.00	1.56	1.50	0.57	0.37
	39	riboflavin	376.14	8.18	377.15	1.00	5.94	3.75	0.97	0.34
	40	uridine monophosphate (UMP)	324.04	1.33	323.03	1.00	0.05	0.03	0.17	0.19
	41	nicotinamide adenine dinucleotide	663.11	2.02	662.10	1.00	0.46	1.03	0.29	0.19
	42	UMP	324.04	1.34	325.04	1.00	0.82	0.81	0.22	0.19

MA/MA: ratio of MA to MA, UM/MA: ratio of UM to MA, FM/MA: ratio of FM to MA, ZJ/MA: ratio of ZJ to MA, and MP/MA: ratio of MP to MA.

Analysis of the Characteristics of Pyrimidines and Purines in I. hispidus Grown on Five Different Tree Species

The study of pyrimidine as a cancer locator and diagnosis and treatment drug has long been of great interest to chemists, medical scientists, and biologists as 5-fluorouracil (5-FU) is an antitumor medicine widely used in the clinic.[22] Purine is a substance in the body, mainly in the form of purine nucleotides, which play a very important role in the energy supply, metabolic regulation, and coenzyme composition.[23] In clinical application, cytosine and thymine are mainly used to treat a variety of disease symptoms caused by a fungal infection and have high antibacterial activity. In this study, 7 pyrimidines and 12 purines (as shown in Table ) were identified in I. hispidus samples grown on five tree species. The contents of these chemical metabolites were significantly different in the fruiting bodies (P < 0.05), for example, the content of cytosine in UM was 660 times higher than that of MA and the content of thymine in FM was 422 times higher than that of MA. Analysis of purine metabolites of five different I. hispidus mushroom samples show that the content of 1-methyluric acid and uric acid were enriched in MA samples. The contents of 2-hydroxy-6-aminopurine and guanine were enriched in UM and FM samples. These differences are extremely significant (P < 0.01), and they show that MA and FM have advantages in pyrimidine research.

Table 4

Summary of Pyrimidine and Purine Metabolites Identified in I. hispidusa

compound class	no.	name	molecular mass	RT [min]	m/z	MA/MA	UM/MA	FM/MA	ZJ/MA	MP/MA
pyrimidine	1	cytosine	111.04	1.34	112.05	1.00	660.77	2.40	1.80	2.08
	2	uracil	112.03	2.19	113.03	1.00	1.47	1.89	1.69	1.61
	3	thymine	126.04	5.49	127.05	1.00	35.22	422.82	3.60	1.51
	4	1,3-dimethyluracil	140.06	1.41	139.05	1.00	1.03	0.00	1.34	1.14
	5	5-hydroxymethyluracil	142.04	1.41	141.03	1.00	1.48	0.75	0.89	0.72
	6	5-methylcytosine	125.06	1.82	126.07	1.00	1.33	1.15	0.89	0.70
	7	TPP	460.01	1.14	459.00	1.00	0.24	0.20	1.23	0.21
purine	8	isopentenyladenine	203.12	10.58	202.11	1.00	1.44	0.99	0.57	3.33
	9	hypoxanthine	136.04	3.35	137.05	1.00	0.72	0.36	1.21	1.90
	10	7-methylguanine	165.07	1.85	166.07	1.00	4.42	1.71	7.05	1.85
	11	2,6-dihydroxypurine	152.03	1.48	151.03	1.00	0.32	0.45	1.39	1.22
	12	triacanthine	203.12	10.86	204.12	1.00	0.98	0.99	0.82	1.07
	13	2-hydroxy-6-aminopurine	151.05	13.60	152.06	1.00	1.46	3.00	0.75	1.05
	14	1-methyluric acid	182.04	1.05	183.05	1.00	0.82	0.74	0.95	0.85
	15	trans-zeatin	219.11	6.66	220.12	1.00	0.46	7.04	3.38	0.78
	16	guanine	151.05	3.25	152.06	1.00	16.05	10.30	1.00	0.69
	17	caffeine	194.08	7.72	195.09	1.00	0.89	1.09	0.68	0.67
	18	uric acid	168.03	1.42	167.02	1.00	0.76	0.63	0.21	0.20
	19	xanthine	152.03	2.08	153.04	1.00	0.55	2.42	0.24	0.16

MA/MA: ratio of MA to MA, UM/MA: ratio of UM to MA, FM/MA: ratio of FM to MA, ZJ/MA: ratio of ZJ to MA, and MP/MA: ratio of MP to MA.

Analysis of the Characteristics of Steroids and Phenols in I. hispidus Grown on Five Different Tree Species

Modern pharmacological studies have shown that steroids and polyphenols have antitumor,[24,25] antioxidant,[26] and other pharmacological activities.[27] The results showed that there were differences in the richness of sterol metabolites in I. hispidus grown on five different tree species, and the content of these active metabolites directly affected the medicinal value. According to their metabolic profile analysis, eight kinds of steroids were identified, including ergosta-5,7,9(11),22-tetraen-3-β-ol, ergosterol peroxide, strophanthidin, 2,3,14,20,22-pentahydroxyergost-7-en-6-one, tetrahydroaldosterone, ouabain, boldione, and guggulsterone. Among them, ergosta-5,7,9(11),22-tetraen-3-β-ol and ergosterol peroxide were significantly enriched in MP samples. Metabolite tetrahydroaldosterone was significantly enriched in MA samples. What is interesting is that ouabain, boldione, and guggulsterone were significantly enriched in UM and FM samples. There are 22 phenolic metabolites identified in I. hispidus grown on five different tree species. Among them, the contents of isorhapontigenin, metanephrine, and o-desmethylnaproxen were significantly enriched in MA samples. In addition, the contents of δ-tocopherol and 3-methoxytyramine were significantly enriched in UM samples. Further detailed information on steroids and phenolic metabolites is shown in Table .

Table 5

Summary of Sterol and Phenol Metabolites Identified in I. hispidusa

compound class	no.	name	molecular mass	RT [min]	m/z	MA/MA	UM/MA	FM/MA	ZJ/MA	MP/MA
sterols	1	ergosta-5,7,9(11),22-tetraen-3-β-ol	394.32	13.55	395.33	1.00	0.50	0.85	1.62	24.17
	2	ergosterol peroxide	428.33	14.66	429.34	1.00	1.29	1.53	1.18	18.56
	3	strophanthidin	404.22	10.15	405.23	1.00	0.24	0.16	0.59	1.11
	4	2,3,14,20,22-pentahydroxyergost-7-en-6-one	478.33	15.67	479.33	1.00	0.85	0.89	1.86	0.80
	5	tetrahydroaldosterone	364.23	11.55	363.22	1.00	0.76	0.37	0.52	0.80
	6	ouabain	584.28	13.89	583.27	1.00	7.84	4.78	0.53	0.43
	7	boldione	284.18	11.06	285.18	1.00	2.40	2.94	1.31	1.13
	8	guggulsterone	312.21	15.04	313.22	1.00	1.16	1.32	0.20	0.95
phenols	9	2-naphthol	144.06	7.41	143.05	1.00	2.02	1.44	1.10	1.81
	10	alternariol	258.05	9.27	259.06	1.00	0.98	1.30	0.62	1.37
	11	dithranol	226.06	8.83	451.12	1.00	1.01	2.60	1.86	1.20
	12	d-δ-tocopherol	402.35	13.77	403.36	1.00	0.17	0.42	0.89	1.15
	13	δ-tocopherol	402.35	15.58	403.36	1.00	10.94	5.16	1.64	1.15
	14	o-cresol	108.06	7.06	109.06	1.00	0.62	1.24	0.64	1.12
	15	3-[2-(3-hydroxyphenyl)ethyl]-5-methoxyphenol	244.11	8.83	245.12	1.00	7.50	30.25	2.79	1.08
	16	pyrogallol	126.03	0.10	125.02	1.00	5.22	1.80	2.21	1.06
	17	2-phenylphenol	170.07	10.02	169.07	1.00	1.19	2.11	1.68	1.05
	18	hematoxylin	302.08	8.99	301.07	1.00	0.11	0.09	0.65	1.00
	19	isorhapontigenin	258.09	11.41	259.10	1.00	0.58	0.79	0.72	0.90
	20	homovanillic acid	182.06	8.37	181.05	1.00	0.57	2.40	0.57	0.88
	21	metanephrine	197.11	10.73	198.11	1.00	0.21	0.48	0.51	0.78
	22	3-methoxytyramine	167.09	9.85	168.10	1.00	1.74	1.49	0.77	0.76
	23	bisphenol A	228.12	8.85	229.12	1.00	6.79	6.53	0.91	0.71
	24	O-desmethylnaproxen	216.08	11.23	217.09	1.00	0.50	0.61	0.27	0.68
	25	γ-tocopherol	416.37	11.95	417.37	1.00	1.48	0.99	1.30	0.67
	26	flavin mononucleotide (FMN)	456.11	7.35	455.10	1.00	1.06	1.05	0.81	0.66
	27	phloroglucinol	126.03	6.95	125.02	1.00	0.55	0.51	1.04	0.61
	28	4-methylphenol	108.06	7.53	107.05	1.00	1.15	2.87	0.61	0.46
	29	2-methoxyresorcinol	140.05	5.43	141.05	1.00	0.64	0.68	0.39	0.40
	30	4-butylresorcinol	166.10	12.31	165.09	1.00	0.39	0.76	1.12	0.25

MA/MA: ratio of MA to MA, UM/MA: ratio of UM to MA, FM/MA: ratio of FM to MA, ZJ/MA: ratio of ZJ to MA, and MP/MA: ratio of MP to MA.

Analysis of the Characteristics of Flavonoids and Terpenoids in I. hispidus Grown on Five Different Tree Species

In this study, there are 24 flavonoids and 11 terpenoids identified in I. hispidus fruiting bodies from five different tree species. The results show that the abundance of flavonoid metabolites on different tree species is quite different. The contents of pinocembrin, kaempferol, rotenone, purpurin, hesperetin, and daidzein were significantly enriched in MA samples. The contents of isorhamnetin, catechin, and sakuranetin were significantly enriched in UM samples. The contents of puerarin, quercetin, and apigenin were significantly enriched in FM. The contents of galangin and myricetin were significantly enriched in MP samples. In addition, terpenoids are one of the important metabolites in I. hispidus mushroom. The content of perillartine, α-farnesene, carvone, and linalool were significantly enriched in MA samples. The contents of limonin and ursolic acid in MP were significantly higher than those of others. Detailed information on flavonoid and terpenoid metabolites is shown in Table .

Table 6

Summary of Flavonoid and Terpenoid Metabolites Identified in I. hispidusa

compound class	no.	name	molecular mass	RT [min]	m/z	MA/MA	UM/MA	FM/MA	ZJ/MA	MP/MA
flavonoids	1	isorhamnetin	316.06	8.30	315.05	1.00	4.72	3.35	0.89	0.89
	2	puerarin	416.11	9.70	417.12	1.00	2.75	5.52	0.77	0.88
	3	quercetin	302.04	8.95	301.04	1.00	2.33	3.59	0.47	0.50
	4	catechin	290.08	9.77	289.07	1.00	2.22	2.08	0.77	0.64
	5	sakuranetin	286.08	11.98	287.09	1.00	1.83	1.12	0.45	0.93
	6	3′,5,7-trihydroxy-4′-methoxyflavanone	302.08	8.51	303.09	1.00	1.66	1.52	0.96	1.09
	7	glycitein	284.07	12.26	283.06	1.00	1.47	0.98	31.26	6.34
	8	7-hydroxy-3-(4-methoxyphenyl)-4H-chromen-4-one	268.07	13.94	269.08	1.00	1.36	1.13	0.43	0.28
	9	apigenin	270.05	11.15	269.05	1.00	1.04	1.08	0.70	0.73
	10	luteolin	286.05	9.62	285.04	1.00	0.81	0.63	1.46	1.33
	11	3,5,7-trihydroxy-2-phenyl-4H-chromen-4-one	270.05	10.51	271.06	1.00	0.76	0.37	1.22	3.38
	12	2-(2,4-dihydroxyphenyl)-3,5,7-trihydroxy-4H-chromen-4-one	302.04	9.90	303.05	1.00	0.74	0.46	0.56	2.01
	13	pinocembrin	256.07	10.16	257.08	1.00	0.53	0.50	0.40	0.56
	14	galangin	270.05	9.26	271.06	1.00	0.52	0.26	0.51	2.81
	15	kaempferol	286.05	10.09	287.06	1.00	0.45	0.52	0.93	0.28
	16	rotenone	394.14	8.08	393.13	1.00	0.35	0.54	0.62	0.47
	17	equol	242.09	9.17	241.09	1.00	0.20	0.25	6.62	3.19
	18	purpurin	256.04	9.03	257.04	1.00	0.20	0.12	0.64	0.61
	19	myricetin	318.04	8.42	317.03	1.00	0.20	0.36	2.04	1.12
	20	hesperetin	302.08	10.78	303.09	1.00	0.16	0.13	0.42	0.63
	21	daidzein	254.06	13.13	255.07	1.00	0.14	0.34	0.21	0.20
	22	4′,7-dihydroxyflavanone	256.07	8.62	257.08	1.00	0.11	0.63	1.11	1.39
	23	eriodictyol	288.06	9.47	287.06	1.00	0.05	0.06	1.72	0.43
	24	wogonin	284.07	8.37	285.08	1.00	0.03	0.08	7.08	2.87
terpenoids	25	d-(+)-camphor	152.12	10.14	153.13	1.00	7.10	2.03	11.10	2.19
	26	limonin	470.20	9.77	471.20	1.00	0.47	0.58	1.05	1.83
	27	ursolic acid	456.36	14.51	455.35	1.00	0.57	0.20	0.19	1.45
	28	2,6-di-tert-butyl-1,4-benzoquinone	220.15	11.00	221.15	1.00	0.56	0.84	1.16	0.90
	29	p-mentha-1,3,8-triene	134.11	14.21	135.12	1.00	1.72	1.83	0.74	0.73
	30	perillartine	165.12	6.11	166.12	1.00	0.04	0.06	0.37	0.69
	31	betulin	442.38	13.38	443.39	1.00	3.25	1.97	0.76	0.69
	32	(+)-ar-turmerone	216.15	12.47	217.16	1.00	3.81	1.16	0.55	0.65
	33	α-farnesene	204.19	13.60	205.20	1.00	0.29	0.39	0.56	0.40
	34	carvone	150.10	13.21	151.11	1.00	0.81	0.80	0.69	0.37
	35	linalool	154.14	12.91	155.14	1.00	0.29	0.25	0.33	0.35

MA/MA: ratio of MA to MA, UM/MA: ratio of UM to MA, FM/MA: ratio of FM to MA, ZJ/MA: ratio of ZJ to MA, and MP/MA: ratio of MP to MA.

Contents of Total Polysaccharides, Total Amino Acids, Crude Protein, Crude Fat, Total Sterols, Total Polyphenols, Total Flavonoids, and Total Terpenes in I. hispidus Grown on Five Different Tree Species

At present, in China, the MA mushroom in the market has become more popular than that grown on other tree species. In terms of edible and medicinal value, whether other tree species can replace MA has become one of the urgent problems to be answered. Therefore, it is necessary to study eight kinds of principal components to guide the market production order scientifically. The contents of total polysaccharides, total amino acids, crude protein, crude fat, total sterols, total polyphenols, total flavonoids, and total terpenes in I. hispidus grown on five different tree species are shown in Figure .

Figure 6

8 Line diagram of abundance changes of total polysaccharides, total amino acids, total proteins, crude fats, total steroids, total polyphenols, total flavonoids, and total terpenes in I. hispidus fruiting bodies grown on five different tree species. *(P < 0.05) Compared with the MA group, there was a significant difference. **(P < 0.01) Compared with the MA group, the difference was extremely significant.

The results showed that the contents of the eight kinds of principal components in them were different. The contents of total polysaccharides and total polyphenols in MA samples were significantly enriched, reaching 1.61 and 1.26%, respectively. The contents of total amino acids, total proteins and total flavonoids in ZJ samples were significantly enriched, reaching 8.92, 12.5, and 3.88%, respectively. It is worth noting that the content of total flavonoids in ZJ is significantly higher than that in other samples. The content of crude fat in FM is the highest, reaching 7.67%. The contents of total steroids and total terpenes in apples were the highest, reaching 0.43 and 0.31%, respectively. Modern pharmacological studies have shown that steroids and phenols have significant antitumor activity. Therefore, our results suggest that MA and MP are more suitable for the study of antitumor drugs.

Analysis of 12 Elements in the Fruiting Bodies of I. hispidus Grown on Five Different Tree Species

The contents of 12 elements including kalium (K), calcium (Ca), natrium (Na), magnesium (Mg), zincum (Zn), ferrum (Fe), manganum (Mn), cuprum (Cu), arsenium (As), cadmium (Cd), hydrargyrum (Hg), and plumbum (Pb) were determined using atomic absorption spectrometry. The contents of Ca, Na, Mg, Fe, and Mn were the highest in MA samples, reaching 193.41, 157.45, 525.77, 287.49, and 12.88 mg/kg, respectively. The contents of Zn in MA and MP samples were the highest, reaching 7.45 and 8.42 mg/kg, respectively. The content of K in UM was the highest, reaching 59.05 mg/kg. In addition, the contents of pollutants such as Cu, As, Cd, Hg, and Pb are in line with China’s national food safety standards, as detailed in Figure .

Figure 7

12 trace elements contents of I. hispidus grown on 5 different tree species. #(P < 0.05) Compared with the MA group, there was a significant difference. ##(P < 0.01) Compared with the MA group, the difference was extremely significant.

Among them, K, Ca, Na, Mg, Zn, Fe, and Mn are seven kinds of trace elements necessary for the human body. Although they are very small in the human body, they are closely related to human survival and health and play a vital role in human life. The lack of these essential trace elements in the human body can result in disease and even be life-threatening. The lack of calcium can cause bone dysplasia and a short stature. Iron deficiency can cause diseases such as iron deficiency anemia. However, not all metal elements are beneficial to people. Heavy metal elements such as Cu, As, Cd, Hg, and Pb accumulate in the human body and can cause some diseases, for example, excessive content of Hg in the human body will lead to Minamata disease, excessive Cd content will lead to pain, and so on. Based on the above information, our results suggested that MA and MP are more suitable for the study of functional foods and drugs related to trace elements.

Discussion

Medicinal and edible mushrooms, in the human diet and traditional medicine, have a long history. Mushroom I. hispidus is an edible and medicinal fungus described in ancient Chinese Materia Medica books in China, such as Shennong’s Classic of Materia Medica and Compendium of Materia Medica. At present, functional foods such as “Sanghuang tea” made of I. hispidus have entered people’s lives and are deeply loved by the Chinese. However, there is no holistic insight to elucidate the molecular basis for the unique use of mushroom I. hispidus grown on different tree species. At present, the bottleneck of the development of it is that the research on the types and changes of metabolites in different tree species is not deep enough, and the quality of the fruiting bodies is not controlled uniformly. Metabonomics provides an effective method for studying the metabolic characteristics of I. hispidus grown on different tree species and explains the metabolic components as a whole from the macroscopic point of view. In our study, UHPLC–MS/MS-based metabonomics methods were used to screen metabolites with significant changes in the fruiting bodies of I. hispidus grown on five different tree species to investigate and compare the metabolites compositions in them. Multivariate PCA and orthogonal projections to PLS-DA confirmed the inherent variation of metabolites and the stability of the whole analysis process. MA samples were collected in Shandong province, China, with the temperate monsoon climate, four distinct seasons, high-temperature and rainy summer, and cold and dry winter. ZJ and MP samples are collected in Shanxi province, China, with temperate continental climate, cold winters, hot summers, drought, and little rain. UM and FM samples were collected in Jilin province in northern China, with short summers and cold and long winters (for sample collection sites, see Figure ). PCA showed that the distributions of UM and FM samples on the map were similar, indicating that the difference of metabolites was small. The distributions of ZJ and MP samples on the map are similar, which indicates that there is little difference in their metabolites. They are far away from MA, indicating that there are great differences between MA and other metabolites. However, the reasons for the differences in chemical composition are not clear, which may be related to different tree species or different geographical environments. We speculate that the geographical environment may be one of the important factors affecting the metabolic components of I. hispidus mushroom. However, this needs to be confirmed by further in-depth study.

In this study, 1353 species of metabolites were identified, including 19 sugars, 42 glycosides, 12 amino acids, 8 steroids, 22 phenols, 26 flavonoids, 11 terpenes, 30 nucleotides, 7 pyrimidines, 12 purines, and so on. It is worth noting that flavonoids have been detected in fungi and have significant pharmacological activities.[28,29] Studies have reported that no relevant enzymes for synthesizing flavonoid components can be found in fungi, so there will be no flavonoid components in fungi.[30] However, some studies have shown that fungi contain genes related to the flavonoid biosynthetic pathway.[31] The study of flavonoids in fungi needs to be further studied in the future. In organisms, different metabolites coordinate with each other to exercise their biological functions, and pathway-based analysis is helpful for further understanding their biological functions. KEGG, whose full name is Kyoto Encyclopedia of Genes and Genomes, is the main public database about pathways (http://www.genome.jp/kegg/). The most important biochemical metabolic pathways and signal transduction pathways involved in metabolites can be determined by pathway analysis. Metabolic pathway analysis was used to further understand the differences of metabolic networks among I. hispidus grown on five different tree species, and the differential metabolites were submitted to the KEGG website for metabolic pathway enrichment analysis. The results showed that there were great differences in the abundance of metabolites among them, and this difference was shown in many metabolic pathways. It is particularly important to distinguish their chemical composition. Figure shows the enrichment pathway of differential metabolites among them. It includes six main metabolic pathways, including biosynthesis of the amino acid metabolism, organic acid metabolism, carbon metabolism, glutathione metabolism, citrate cycle (TCA cycle), ABC transporters, and some secondary metabolic pathways, such as phenylalanine metabolism, biosynthesis of antibiotics, nicotinate, and nicotinamide metabolism, ascorbate and aldarate metabolism, C5-branched dibasic acid metabolism, purine metabolism, pyrimidine metabolism, and so on. In addition, it is worth considering that the samples have different collection sites, and different longitudes and latitudes, temperature, humidity, and other environmental factors may have effects on the chemical composition metabolism. Further research and discussion are needed to clarify the effects of these comprehensive factors on the chemical composition metabolism.

Figure 8

KEGG enrichment bubble diagram of metabolites of I. hispidus grown on five different tree species. (A) UM vs MA under the positive ion mode. (B) UM vs MA under the negative ion mode. (C) FM vs MA under the positive ion mode. (D) FM vs MA under the negative ion mode. (E) ZJ vs MA under the positive ion mode. (F) ZJ vs MA under the negative ion mode. (G) MP vs MA under the positive ion mode. (H) MP vs MA under the negative ion mode. The abscissa in the picture is x/y (the number of differential metabolites in the corresponding metabolic pathway/the total number of metabolites identified in this pathway). The higher the value, the higher the degree of enrichment of differential metabolites in this pathway. The color of the point represents the p-value of the hypergeometric test, and the smaller the value is, the greater the reliability of the test is, and the more statistically significant it is. The size of the point represents the number of differential metabolites in the corresponding pathway, and the larger the number of differential metabolites in the pathway, the more the differential metabolites in the pathway.

In summary, the metabolism profiling of I. hispidus grown on five different tree species is described comprehensively, and 1353 chemical metabolites are identified. The chemical metabolites in I. hispidus grown on five different tree species are the same, but the contents of chemical metabolites are quite different. The contents of eight kinds of principal components in the samples were studied, and the results showed that total phenols and total steroids with significant antitumor effects were enriched in mushrooms MA and MP, respectively. The contents of trace elements were studied, and the results show that Ca, Na, Mg, Fe, Mn, and Zn were the elements with the highest content in mushrooms MA and MP. In addition, what is worth considering is that for metabolites, it is not just the mass spectrum peak. Furthermore, mass spectrometry cannot detect all the metabolites, not because the mass spectrometry is not sensitive enough, but because mass spectrometry can only detect ionized substances and some metabolites cannot be ionized in the mass spectrometer. Nuclear magnetic resonance (NMR) is needed to make up for the lack of chromatography in the future, and further research is needed.

Conclusions

The authors acknowledge the financial supports from the National Natural Science Foundation of China (no. 32070021). Also grateful for Baoyu Yang, Junju Geng and Zhengkuo Li of Xiajin DeBai Technology Institute of Ancient Mulberry Co., Ltd., Dezhou city, Shandong province, and Zhirong Liang of Muye Institute of Edible Fungi in Xinzhou city, Shanxi province, for their help in the process of samples collection; grateful for Engineering Research Center of Chinese Ministry of Education for Edible and Medicinal Fungi providing administrative and technical support.

Experimental Section

Chemicals

All chemicals used in this study were of chromatographic grade for LC. Acetonitrile, methanol, and formic acid were purchased from Merck (Darmstadt, Germany). Acetic acid and methyl alcohol were obtained from Tedia (Tedia Co., Ohio, USA). Deionized water was purified by using a Milli-Q water purification system (Millipore, Billerica, MA, USA).

Sample Collection

In this study, the fruiting body samples of five species of I. hispidus mushroom were collected from five different tree species in the wild. All samples were identified as I. hispidus by DNA molecular ITS sequencing, and the raw DNA molecular sequences can be found in Supporting Information Table S128. UM samples were grown on U. macrocarpa var. mongolica in Xianghai Nature Reserve and were collected on August 10, 2020. Six fruiting bodies with mature growth stage (the color of fruiting body was brown) were randomly selected. They were collected from six trees of U. macrocarpa var. mongolica. Similarly, FM samples were collected from F. mandshurica in Jingyuetan National Scenic Area on August 10, 2020. ZJ and MP samples were collected from Z. jujuba and M. pumila in Xinzhou city, Shanxi province on August 11, 2020. Similarly, MA samples were collected from M. alba Linn. in Xiajin County, Shandong province on August 11, 2020. All samples of I. hispidus mushroom samples have six duplicates. All samples were stored at −80 °C until metabolomic analysis. The voucher specimens are deposited in the Key Laboratory of Medicinal Fungal Resources and Development and Utilization, Jilin Agricultural University: MA under no. 58767, UM under no. 58768, FM under no. 58769, ZJ under no. 58770, and MP under no. 58771. Detailed sample information is shown in Table .

Table 7

Information for I. hispidus Samplesa

index	sampling position	collection place	collection time
UM	middle part	Xianghai Nature Reserve, Jilin province (45°02′N, 122°30′E)	2020-8-10
FM	middle part	Jingyuetan National Scenic Area, Jilin province (43°79′N, 125°46′E)	2020-8-10
ZJ	middle part	Dingxiang County, Xinzhou City, Shanxi province (38°37′N, 112°59′E)	2020-8-11
MP	middle part	Dingxiang County, Xinzhou City, Shanxi province (38°37′N, 112°59′E)	2020-8-11
MA	middle part	Xiajin County, Dezhou City, Shandong province (36°59′N, 115°11′E)	2020-8-11

UM: I. hispidus on U. macrocarpa var. mongolica, FM: I. hispidus on F. mandshurica, ZJ: I. hispidus on Z. jujuba, MP: I. hispidus on M. pumila, and MA: I. hispidus on M. alba Linn.

Analysis of Metabolites of I. hispidus Grown on Five Different Tree Species Using UHPLC–MS/MS

Metabolite Extraction

Tissues (100 mg) were placed in Eppendorf tubes and resuspended with prechilled 80% methanol and 0.1% formic acid by vortexing. The samples were incubated on ice for 5 min and then were centrifuged at 15,000g, 4 °C for 20 min. Some supernatant was diluted to the final concentration containing 53% methanol with LC–MS grade water. The samples were subsequently transferred to a fresh Eppendorf tube and then were centrifuged at 15,000g, 4 °C for 20 min. Finally, the supernatant was injected into the LC–MS/MS system for analysis.

UHPLC–MS/MS Analysis

UHPLC–MS/MS analyses were performed using a Vanquish UHPLC system (Thermo Fisher, Germany) coupled with an Orbitrap Q Exactive HF mass spectrometer (Thermo Fisher, Germany). Samples (injection volume is 7 μL in both positive and negative ion modes) were injected onto a Hypesil Gold column (100 × 2.1 mm, 1.9 μm) using a 17 min linear gradient at a flow rate of 0.2 mL/min. The solvent gradient was set as follows: 2% B MeOH, 1.5 min; 2–100% B MeOH, 12.0 min; 100% B MeOH, 14.0 min; 100–2% B MeOH, 14.1 min; and 2% B MeOH, 17 min. The positive mode mobile phase is 0.1% formic acid; the negative mode mobile phase is A: 5 mmol/L ammonium acetate; and the pH is 9.0. The Q Exactive HF mass spectrometer was operated in the positive/negative polarity mode with a spray voltage of 3.2 kV, a capillary temperature of 320 °C, a sheath gas flow rate of 40 arb, and an aux gas flow rate of 10 arb.

Data Processing and Metabolite Identification

The raw data files generated by UHPLC–MS/MS were processed using Compound Discoverer 3.1 (CD3.1, Thermo Fisher, USA) to perform peak alignment, peak picking, and quantitation for each metabolite. The offline data (.raw) file was imported into CD-search software (Compound Discoverer 3.1, Thermo Scientific, USA). The parameters such as retention time and mass-to-charge ratio were screened, and then, the peaks of different samples were aligned according to the retention time deviation of 0.2 min and the mass deviation of 5 ppm to make the identification more accurate. Then, the peaks were extracted according to the set quality deviation of 5 ppm, signal strength deviation of 30%, signal-to-noise ratio of 3, minimum signal strength of 100,000, additive ion, and other information, and the peak area is quantified at the same time, and then, the target ion is integrated. The molecular formula was predicted and compared with mzCloud database. Blank samples were used to remove background ions; QC samples were used to standardize the quantitative results; and finally, the data identification and quantitative results were obtained. Peaks were matched with the mzCloud (https://www.mzcloud.org/), mzVault, and MassList databases to obtain accurate qualitative and relative quantitative results. Statistical analyses were performed using statistical software R (R version R-3.4.3), Python (Python 2.7.6 version), and CentOS (CentOS release 6.6), and when data were not normally distributed, normal transformations were attempted using the area normalization method.

Metabonomic Data Analysis

These metabolites were annotated using the KEGG database (https://www.genome.jp/kegg/pathway.html) and LIPIDMaps database (http://www.lipidmaps.org/). PCA and PLS-DA were performed at metaX (a flexible and comprehensive software package for processing metabolomics data). We applied univariate analysis (t-test) to calculate the statistical significance (P-value). The metabolites with VIP > 1, P-value < 0.05, and fold change ≥ 2 or fold change ≤ 0.5 were considered to be differential metabolites.

The functions of these metabolites and metabolic pathways were studied using the KEGG database. Metabolic pathway enrichment of differential metabolites was performed, where when the ratio was satisfied by x/n > y/N, metabolic pathways were considered as enriched, and when the P-value of metabolic pathway <0.05, the metabolic pathway was considered as statistically significant enriched.

Total Polysaccharides, Total Amino Acids, Crude Protein, Crude Fat, Total Sterols, Total Polyphenols, Total Flavonoids, and Terpenoids in the I. hispidus Fruiting Bodies from Five Different Tree Species

Through the metabolic profiling analysis of the fruiting bodies of I. hispidus grown on five different tree species, the relative contents of their complex chemical metabolites were determined, but a comprehensive macroquantitative analysis may be involved in practical application. Therefore, the contents of total polysaccharides, total amino acids, crude protein, crude fat, total sterols, total polyphenols, total flavonoids, and terpenoids were analyzed. Microsoft Excel 2019 and IBM SPSS21.0 were used for data processing and mapping.

Analysis of the Contents of Trace Elements in the Fruiting Bodies of I. hispidus Grown on Five Different Tree Species

The dried fruiting bodies of I. hispidus grown on five different tree species were crushed, and the quantitative powders were weighed and placed in a tetrafluoroethylene tube. Then, the mixture of 10 mL of nitric acid and perchloric acid (4:1) was added and digested in an electrothermal digester. After cooking at 170 °C for 3 h, the volume was increased to 50 mL. The contents of K, Ca, Na, Mg, Zn, Fe, Mn, Cu, As, Cd, Hg, and Pb were determined using atomic absorption spectrometry. Each sample was analzed three times in parallel. Microsoft Excel 2019 and IBM SPSS21.0 were used for data processing and mapping.