The regular pattern of metabolite changes in mushroom Inonotus hispidus in different growth periods and exploration of their indicator compounds

Inonotus hispidus is a valuable and rare edible and medicinal mushroom with extremely high nutritional and medicinal value. However, there is no holistic insight to elucidate the molecular basis of the differentiated usage and accurate annotation of physiological maturity to fluctuating yields and quality. This study aimed to figure out the fruiting bodies' metabolites change regulation and potential maturating indicators to distinguish different quality I. hispidus. We applied non-targeted ultra-high performance liquid chromatography and high-resolution mass spectrometry combined and with multivariate analysis and analyzed cultivated and wild mushroom I. hispidus in different growth periods (budding, mature and aging). With the fruiting bodies maturating, 1358 metabolites were annotated, 822 and 833 metabolites abundances changed greater than or equal to 1 time from the budding period to the aging period in abundance in cultivated and wild, the total polysaccharides, crude fat, total flavonoids, and total terpenes increased at first and then decreased. Total amino acids, crude protein, and total polyphenols decreased, while the total steroids increased linearly. The change of metabolites showed certain regularity. Metabolic pathways enrichment analysis showed that these metabolites are involved in glycolysis, biosynthesis of amino acids, organic acid metabolism, glycine-serine-and-threonine metabolism, tricarboxylic acid cycle, purine metabolism, and pyrimidine metabolism. In addition, ergosterol peroxide and (22E)-ergosta-4,6,8(14),22-tetraen-3-one can be used as indicator compounds, and their contents increase linearly with the fruiting bodies of I. hispidus’ physiological maturation. This comprehensive analysis will help to evaluate the edible values and facilitate exploitation in mushroom I. hispidus.

overall distribution among all samples and the stability of the entire analysis process. As shown in (Fig. 1B), all the quality control samples are clustered together, showing good analytical stability and experimental reproducibility. In our principal component analysis model, 6 groups of samples are well separated. Among them, the samples of wild I. hispidus in the budding period (WB), wild I. hispidus in the mature period (WM), artificial cultivated I. hispidus in the budding period (AB), and cultivated I. hispidus in the mature period (AM) are clustered together closely, while the samples of wild I. hispidus in aging period (WA) and cultivated I. hispidus in aging period (AA) deviate slightly. These results are consistent with our previous conclusion that there are great differences in the types of metabolites in different growth periods of the fruiting bodies of I. hispidus, and there are great differences in the abundance of metabolites between the fruiting bodies in the aging period and the other two periods. The wild and cultivated I. hispidus mushroom in different growth periods as shown in Fig. 1A.
Partial least-squares discriminant analysis. To determine the budding, mature, and aging fruiting bodies of cultivated and wild I. hispidus, and the metabolic differences among 6 groups of samples, the supervised partial least square discriminant analysis model was used to further optimize the population separation of fruiting bodies. The comparison between paired AB, AM, and AA samples of Partial Least-Squares Discriminant Analysis (PLS-DA) fruiting bodies showed that there were significant differences in metabolism among different categories in each pairwise comparison of the first component (Fig. 1C). The partial least squares model has high R2Y and Q2 values, which indicated an excellent fit and satisfactory predictive power. A permutation test was used to evaluate the possible overfitting of the PLS-DA model. In this study, a 200-time permutation test was performed. R2 intercept of fruiting body AB and WB samples is 0.90 and R2 intercept of WM samples is 0.94. R2 intercept of AA and WA samples is 0.90, while Q2 intercept is − 0.72, − 0.70, and − 0.76, respectively. Figure 1C indicates that the PLS-DA model showed no overfitting and was credible. The annotation of differential metabolites between pairs of samples was performed using VIP values and confirmed by the non-parametric Mann-Whitney U test.
Comparative analysis of differential metabolites. With the fruiting bodies maturating, 1358 metabolites were annotated, 822 and 833 metabolites' abundances changed greater than or equal to 1 time from the budding period to the aging period in abundance in cultivated and wild (as shown in Supplementary Materials S1). Taking the expression of all metabolites annotated as data, the samples were compared by single-factor analysis of variance (one-way ANOVA). Corrected by the Benjamini-Hochberg method, the metabolites were screened for differential expression with P-value 0.05 as the threshold, and the metabolites were classified according to the expression between samples. The heat-map of different metabolites of cultivated and wild mushroom I. hispidus www.nature.com/scientificreports/ can be seen in ( Fig. 2A,B). Vertical clustering is the clustering of samples, and horizontal clustering is the clustering of metabolites. The shorter the clustering branches are, the higher the similarity is. Through horizontal comparison, we can see the relationship of metabolite abundance clustering between groups. The results of the heat map showed that the abundance of metabolites varied greatly from the budding period to the mature period and then to the aging period, no matter whether in cultivated or wild ones. Because the abundance of metabolites is different in different growth periods, the pharmacological activity and value of I. hispidus are different, which shows that it is necessary to use scientific methods to accurately judge the best harvest time. In addition, the volcano plot of differential metabolites showed that there were great differences in metabolites between cultivated and wild mushroom I. hispidus in the same period. Volcano plot indicating upregulated and downregulated ( Fig. 2C,D). In different growth periods (budding, mature and aging), the Venn diagram of the differential metabolites in the fruiting bodies of cultivated and wild I. hispidus showed the overlapping and differential parts of the metabolites. In this study, the Venn diagram analysis of the differential metabolites of the three comparative combinations is shown in Fig. 3 (Venn diagram in positive ion mode as shown in Supplementary Materials S2, and in negative ion mode as shown in Supplementary Materials S3).
Sugars, amino acids metabolites, and oligopeptides were annotated in Inonotus hispidus. In this study, 20 sugars,12 amino acids with different configurations (including 4 essential amino acids and 8 nonessential amino acids), and 24 oligopeptides were annotated in mushroom I. hispidus. With the physiological maturity of the fruiting body, most of the sugars and amino acid content of cultivated I. hispidus decreased. The abundance of arginine increased at first and then decreased. The abundance of proline decreased at first and then increased. However, with the physiological maturity of the fruiting body, the abundance of phenylalanine, proline, aspartic acid, and tyrosine increased at first and then decreased, and the abundance of other amino acids decreased in the wild I. hispidus. These changes are worth thinking about and studying, and the collection of these substances is not only the characteristic components of I. hispidus, but also the material basis to play a unique biological function. The detailed changes of amino acids and oligopeptides are shown in Table 1. www.nature.com/scientificreports/ ological maturity. However, the abundance of ergosterol peroxide of cultivated I. hispidus decreased in the whole period. The abundance of ouabain of wild I. hispidus increased at first and then decreased, but in wild samples, it decreased first and then increased. These changes will directly affect the quality of I. hispidus in different growth periods. Detailed changes about sterols, phenols, flavonoids, and terpenoids metabolites in mushroom I. hispidus are shown in Table 2.

Sterols, phenols, flavonoids, and terpenoids metabolites were annotated in
Changes of total polysaccharides, total amino acids, crude protein, crude fat, total sterols, total polyphenols, total flavonoids, and total terpenes in fruiting bodies of Inonotus hispidus in different growth periods. With the physiological maturity of the I. hispidus fruiting body, the metabolites showed a regular change trend, and this changing trend occurred not only in cultivated I. hispidus, but also in wild. The results showed that the fruiting body of I. hispidus is in the process from the budding period to the mature period and then to the aging period, the abundance of total polysaccharides, crude fat, total flavonoids, www.nature.com/scientificreports/ and total terpenes increased at first and then decreased. Interestingly, total amino acids, crude protein, and total polyphenols decreased during the growth periods, while the total steroids increased linearly. The analysis of the content and change regular of the metabolites showed distinct changes in abundance in cultivated and wild. From an overall point of view, the changes of metabolites in cultivated were more stable and the content of metabolites higher than the wild ones. Therefore, we speculate that the cultivated I. hispidus may be more suitable for the function food or medicines. The 8 kinds of primary and secondary metabolites changed trend were shown in Fig. 4.

Metabolic pathway analysis.
In order to further understand the differences in metabolic networks among samples, the annotated differential metabolites were submitted to the KEGG website for metabolic pathway enrichment analysis. The enrichment pathway of differential metabolites between cultivated and wild I. hispidus in the bud stage, mature stage, and aging stage (Fig. 5). It mainly includes eight main metabolic pathways, such as steroid biosynthesis, biosynthesis of amino acids, organic acid metabolism, glycine, serine and threonine metabolism, tricarboxylic acid cycle, glycolysis, purine metabolism, and pyrimidine metabolism, as well as some secondary metabolic pathways. Although artificial cultivation and wild I. hispidus have the same kinds of metabolites, the enrichment degree of metabolites is different in different physiological maturity stages.

Ergosterol peroxide and (22E)-ergosta-4,6,8(14),22-tetraen-3-one content in Inonotus hispidus samples collected at different physiological maturity stages.
Among the annotated metabolites, ergosterol peroxide attracted our attention because of the high fold-change difference in its abundance in different grow period samples. The abundance of ergosterol peroxide increased significantly, which prompted us to consider whether this metabolite can be used as an indicator of the physiological maturity of fruiting bodies. Therefore, the fruiting body samples were collected on days 10, 20, 30, 40, and 50, and the contents of ergosterol peroxide were determined by High Performance Liquid Chromatography (HPLC) according to the method reported in the literature. During the period of day 1 to day 20, the fruiting body of I. hispidus was golden in the budding period, mature on days 20 to day 50, yellowish-brown in the fruiting body, and gray-black in the aging period after day 50. During the whole process of growth and development, the metabolites with continuous decrease or increase in the abundance of metabolites may become an indicator of fruiting body maturity. It was found that the content of ergosterol peroxide was low in the budding period and could hardly be detected, but it was higher in the aged fruiting body. It is speculated that ergosterol peroxide can be used as a potential indicator to identify whether the fruiting body has reached physiological maturity. The content of ergosterol peroxide was 0.008% on days 10 of the fruiting body of cultivated I. hispidus, 0.024% on day 30 of maturity, 0.048% on day 50 of aging, and with the extension of the fruiting body growth period, the content of ergosterol peroxide increased and showed an upward trend (Fig. 6A).

Discussion
This work investigated the fruiting bodies' metabolites change regulation and potential maturating indicators to distinguish different quality I. hispidus. The results showed that there were significant differences in the composition of metabolites in the fruiting bodies of I. hispidus in different growth periods, which would lead to different medicinal values. Therefore, different growth periods have a significant impact on the quality of it, and it is necessary to further study and clarify the differences. In our analysis, a total of 1358 metabolites were annotated from I. hispidus fruiting bodies. Based on the metabolic profile analysis of the KEGG database (https:// www. genome. jp/ kegg/ pathw ay. html) and LIPIDMaps database (http:// www. lipid maps. org/), including 20 sugars, 12 amino acids, 24 oligopeptides, 23 steroids, 31 phenols, 12 flavones, 4 isoflavones, and 10 flavonoids and 11 terpenes were annotated. There are significant differences in metabolites in the process of physiological maturation of mushroom I. hispidus, which is mainly reflected in the change of abundance rather than in kinds. Among the metabolites of I. hispidus, sugars, amino acids, oligopeptides, steroids, polyphenols, flavonoids, and terpenoids have been reported to have pharmacology activity. Therefore, these metabolites are mainly listed in this paper. Sugars are one of the effective components in I. hispidus, modern pharmacological studies have shown that I. hispidus sugars have protective effects on acute alcoholic liver injury in mice 1 . Amino acids as one of the important indicators of food nutritional value evaluation 28,29 , and have many pharmacological activities, such as L-leucine is used in the clinical treatment of liver disease 30 . Modern pharmacological studies show that serine plays a role in relieving chronic low-back and knee pain in adults 31 , leucine can by promoting insulin and Glucagon-Like Peptide 1 (GLP-1) secretion. With physiological maturity, the relative abundance of amino acids such as lysine, threonine, asparagine, serine and glutamic acid and histidine in the fruiting body decreased gradually. The oligopeptide is a biochemical substance between amino acid and protein, which is composed of 2 to 15 amino acids. In this study, it is found that the content of oligopeptides is abundant, especially with the physiological maturity of the fruiting body, the abundance of Glu-Val-Phe increases significantly. On the contrary, the abundance of Tyr-Tyr decreases significantly with the physiological maturity of the fruiting body. Steroids and phenols in I. hispidus are important active components, which have antioxidants 2 , anti-inflammatory 6 , and bacteriostatic activities 6 . In recent years, flavonoids and terpenoids as two important kinds of metabolites in I. hispidus have been widely concerned and studied 2 . Modern pharmacological studies have shown that flavonoids www.nature.com/scientificreports/ have antioxidant potential and antimicrobial activity 32 . Some terpenoids have the effects of antioxidation, liver protection, immunity enhancement, anti-tumor, and so on. Such as betulin, which has anti-inflammatory 33 , antiviral 34 , and other activities 35 . With physiological maturity, the relative abundance of betulin in the fruiting body decreased gradually. These findings suggest that the abundance of metabolites showed some regular changes with the fruiting bodies maturating of I. hispidus. When using these fruiting bodies as raw materials for functional foods and drugs, special attention should be paid to the changes of the abundance of metabolites in the fruiting bodies of I. hispidus. From the perspective of primary metabolites and secondary metabolites, the changes of eight main metabolites of wild and cultivated I. hispidus mushroom during different growth stages were compared and analyzed. The result showed that the abundance of total polysaccharides, crude fat, total flavonoids, and total terpenes increased at first and then decreased. Interestingly, total amino acids, crude protein, and total polyphenols decreased during the growth periods, while the total steroids increased linearly. In this study, it is worth noting that the content of 8 kinds of primary and secondary metabolites of cultivated I. hispidus mushroom is significantly higher than that of wild, which may be related to the fact that water, temperature, nutrients, and other factors in the artificial cultivation environment are more suitable for the growth of it. Compared with the growth environment conditions of wild I. hispidus, cultivated has more stable growth environment conditions, and is more stable and unified in soil nutritional status, temperature, humidity, precipitation, and so on. These may be some important reasons for the high quality of cultivated. Metabolite profiles are highly affected by environmental conditions, but it needs to be explained by further research. On the one hand, the changes of metabolites in cultivated were more stable and the content of metabolites higher than the wild ones, which is more suitable for the function of food and medicine. On the other hand, cultivated I. hispidus is easier to control the growth environment conditions, standardized production, and easy to obtain high yield. These results suggested that cultivated I. hispidus may Table 2. Summary of sterols, phenols, flavonoids, and terpenoids metabolites were annotated in Inonotus hispidus. WM/WB: The ratio of the mature period to budding period of wild Inonotus hispidus. WA/WB: The ratio of the mature period to budding period of wild Inonotus hispidus. WA/WM: The ratio of the aging period to mature period of wild Inonotus hispidus. AM/AB: The ratio of the mature period to budding period of artificially cultivated Inonotus hispidus. AA/AB: The ratio of the aging period to budding period of artificially cultivated Inonotus hispidus. AA/AM: The ratio of the aging period to mature period of artificially cultivated Inonotus hispidus. www.nature.com/scientificreports/ be more suitable for the needs of industrial products. From the perspective of primary metabolites and secondary metabolites, the relative contents of total amino acids and total polysaccharides decrease gradually with the physiological maturity of fruiting bodies, if the sugars or amino acids in I. hispidus are used in industry, then the maximum benefit may be obtained in the bud stage, of course, the yield needs to be considered. If steroids are used in industry, and the content of steroids increases with the physiological maturity of the I. hispidus fruiting bodies, then the aging I. hispidus fruiting bodies may be more suitable. This study showed that there were significant differences in the relative abundance of primary and secondary metabolites in the fruiting bodies of I. hispidus in different growth periods. This study provides a scientific basis for selecting the best I. hispidus fruiting bodies in the growing period according to the demand. In addition, a lack of measurable indicators that can characterize the physiological maturity of I. hispidus, leads to the subjective evaluation of the physiological maturity and the fluctuation of the yield and quality of I. hispidus and the difference of production efficiency of different producers. Therefore, there is an urgent need for an indicator of fruiting body maturation, to provide a basis for production and research. In our study, the content of ergosterol peroxide and (22E)-ergosta-4,6,8(14),22-tetraen-3-one in fruiting bodies showed a regular change during physiological maturation. Almost no ergosterol peroxide and (22E)-ergosta-4,6,8(14),22-tetraen-3-one were detected in fruiting bodies collected on day 0 of the wild and cultivated I. hispidus, but its content was extremely high in physiologically mature fruiting bodies. We deduced that ergosterol peroxide and (22E)-ergosta-4,6,8(14),22-tetraen-3-one may be used as potential indicators to identify whether fruiting bodies have reached physiological maturity. However, the utility of ergosterol peroxide and (22E)-ergosta-4,6,8(14),22-tetraen-3-one content as an indicator of the physiological maturation of mycelia was not determined, and this question merits additional fruiting experiments.
In summary, this study showed that with the fruiting bodies maturating, the abundance of metabolites showed some regular changes. The cultivated and wild I. hispidus mushroom has the same 1358 metabolites, but the metabolites showed distinct changes in abundance in cultivated and wild. It is worth noting that mass spectrometry can't detect all the metabolites, not because the mass spectrometry is not sensitive enough, but because mass spectrometry can only detect ionized substances, but some metabolites can't 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 [36][37][38] . Through the metabonomic and natural product research combined, the content of ergosterol peroxide and (22E)-ergosta-4,6,8(14),22-tetraen-3-one showed a linear trend increase, which indicated the feasibility of their use as an indicator for fruiting body maturation, but whether this indicator can be applied for the commercial production of mushroom I. hispidus requires the verification of large-scale fruiting experiences. The metabolites of I. hispidus fruiting bodies were different in different growth stages, so the quality was different. However, the maturity of the fruiting body can be determined by peroxidation of ergosterol and (22E)-ergosta-4, 6,8(14),22-tetraen-3-one, and the quality of the fruiting body can be screened indirectly. Our research provided an atheoretical basis for quality evaluation and comprehensive utilization of the mushroom I. hispidus from cultivated and wild.

Materials and methods
Chemicals. All chemicals used in this study were of chromatographic grade for liquid chromatography. Acetonitrile, methanol, and formic acid were purchased from Merck (Darmstadt, Germany). Acetic acid and methyl alcohol were from Tedia (Tedia Co., Ohio, USA). Deionized water was purified by a Milli-Q water purification system (Millipore, Billerica, MA, USA). Total polysaccharides, total amino acids, crude proteins, total sterols, total polyphenols, total flavonoids, terpenes, and crude fat were determined in fruiting bodies of Inonotus hispidus during different growth periods. Through the analysis of the metabolic profiling of the fruiting bodies of wild and cultivated I. hispidus in different growth periods, the complex chemical composition and its change trend line were shown, but the practical application may involve a macroscopic overall quantitative analysis. Therefore, the contents of total polysaccharides, total amino acids, crude protein, total steroids, polyphenols, total flavonoids, terpenoids, and crude fat in wild and cultivated I. hispidus fruiting bodies were analyzed in this study.
Data processing and metabolite annotation. Data processing and metabolite annotation refer to previously methods 23 . The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1, Thermo Fisher, USA) to perform peak alignment, peak picking, and quantitation for each metabolite. The main parameters were set as follows: retention time tolerance, 0.2 min; actual mass tolerance, 5 ppm; signal intensity tolerance, 30%; signal/noise ratio, 3; and minimum intensity, 100,000. After that, peak intensities were normalized to the total spectral intensity. According to the formal definition of metabolites  www.nature.com/scientificreports/ annotation and annotation developed by the Chemical Analysis Working Group of the Metabolomics Standards Initiative (MSI) working Group on Chemical Analysis 41 , this study annotates metabolites detected by nontargeted metabonomic scans. The normalized data were used to predict the molecular formula based on additive ions, molecular ion peaks, and fragment ions. And then peaks were matched with the mzCloud (https:// www. mzclo ud. org/), mzVault, and MassList database to obtain the accurate qualitative and relative quantitative results. Statistical analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version), and CentOS (CentOS release 6.6), When data were not normally distributed, normal transformations were attempted using of area normalization method.
Metabonomic data analysis. Data were normalized for internal consistency by processing the constant weight of the solvent per volume of each sample. The internal consistency of the data is normalized data were scaled to the median value for each compound, then missing values were imputed with the minimum detected value for that compound. Moreover, a variety of curation procedures were carried out to ensure that a high-quality data set was made available for statistical analysis and data interpretation. The QC and curation processes were designed to ensure accurate and consistent annotation of true chemical entities and to remove those representing system artifacts, misassignments, and background noise. Library matches for each compound were checked for each sample and corrected if necessary. Metabonomic data analysis refers to previously methods 24,26,[42][43][44] . These metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https:// www. genome. jp/ kegg/ pathw ay. html), Human Metabolome Database (HMDB) (https:// hmdb. ca/ metab olites), and LIPIDMaps database (http:// www. lipid maps. org/). The data analysis of PCA and PLS-DA were performed using software python (Python-3.5.0), and the charts were drawn by software R (R-3.4.3). We applied univariate analysis (t-test) to calculate the statistical significance (P-value). The metabolites with VIP > 1 and P-value < 0.05 and fold change ≥ 2 or FC ≤ 0.5 were considered to be differential metabolites. Volcano plots were used to filter metabolites of interest-based on log2(FoldChange) and -log10(p-value) of metabolites.
For clustering heat maps, the data were normalized using z-scores of the intensity areas of differential metabolites and were plotted by the Pheatmap package in R language. A statistically significant correlation between differential metabolites was calculated by R language. P-value < 0.05 was considered as statistically significant and correlation plots were plotted by corrplot package in R language. The metabolic pathways enrichment of differential metabolites was performed, when the ratio was satisfied by x/n > y/N, metabolic pathways were considered as enrichment, when the P-value of metabolic pathway < 0.05, metabolic pathways were considered as statistically significant enrichment. For line charts and tables, the data were normalized using Microsoft Office Excel 2019 software, and the chemical structure formula was made by ChemDraw 15 software. www.nature.com/scientificreports/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.