Identification of Metabolites
The mechanism how fungal elicitors affect the metabolites of the elicitor treatment (ET) and without elicitor treatment (WET) groups was investigated by performing a metabolome analysis. The results showed a large overlap of the curves of the total ion flow for metabolite detection. The maintenance time and peak strength were steady, which showed that the mass spectrum signal remained consistent, and the same sample was tested at different times. The homogeneous and stable sample quality met the criteria for conducting follow-up experiments (Figure S1). Principal component analysis (PCA) was performed on the S. sanghuang samples from ET and WET. Principal component 1 (PC1) and principal component 2 (PC2) were 71.46% and 5.73%, respectively, as shown in Figure 1A. Then, an orthogonal partial least squares discriminant analysis (OPLS-DA) of the metabolite profiles of S. sanghuang was performed. The results showed that R2X, R2Y, and Q2 were 0.766, 0.999, and 0.995, respectively (Table 1 and Figure 1B), indicating that the model of OPLS-DA was stable and credible at Q2 > 0.9. The PCA and OPLS-DA score plots exhibit a clear separation between ET and WET. These suggest that polysaccharide elicitors do affect the metabolism in S. sanghuang.
In ET and WET metabolite identification of a total of 128 of known structure, conducted with the mass of the validation, each of these metabolites was biologically analyzed in triplicate. The main substances were phenolic acids (42.18%), alkaloids
(18.75%), flavonoids (26.56%), lignans and coumarins (4.69%), terpenoids (8.8%), tannins (1.56%), quinones (0.78%), and others (1.56%).
Filtering and Verification of Various Metabolites
Based on the OPLS-DA results, metabolites of different species or tissues were initially screened for multivariate analysis from the variable importance in prediction (VIP) obtained from the OPLS-DA model. The P value or fold change can be combined with the univariate analysis to further screen differentially accumulated metabolites (DAMs). A combination of fold change ≥2 and fold change ≤0.5, and a value of VIP ≥1 of the OPLS-DA model were taken to screen DAMs. Of the total 128 DAMs detected, 48 were differential secondary metabolites; 25 of these 48 were upregulated and 23 were downregulated (Figure 2A). To be able to observe the pattern of metabolite changes, we normalized metabolites with significant differences using the unit variance scaling method (UV) and drafting heat maps using the R software pheatmap package, as shown in Figure 2B. The main substances were phenolic acids, alkaloids, lignans and coumarins, flavonoids, and terpenoids; among these, flavonoids (24.4%), terpenoids (8.8%), and lignans and coumarins (11%) showed significant upregulation compared with the control group. To provide further insight into the function of DAMs and the connected biological processes they are involved in, we performed an enrichment analysis of DAMs on KEGG. The results indicated that DAMs were primarily enriched in the “biological synthesis of secondary metabolites,” “ubiquinone and other terpenoid–quinone biosynthesis,” “phenylpropanoid metabolism,” “tryptophan metabolism,” and “isoquinoline alkaloid biosynthesis” (Figure 2C).
Furthermore, based on the UPLC-MS/MS test platform and database, 128 metabolites were detected. There were 48 DAMs, predominantly phenolic acids, lignans and coumarins, flavonoids, and terpenoids. Among them, flavonoids (24.4%), terpenoids (8.8%), and coumarin lignans (11%) showed significant upregulation. Three biphenylquinone and other terpenoid-quinone bioactivity-related triterpenoids were detected, such as betulinol, betulinic acid, and 2-hydroxyoleanolic acid. They were obviously different between ET and WET.
Table 1
Results of differential metabolite filtering
Index
|
Compounds
|
Type
|
Lmbn002644
|
4-Methylbenzaldehyde
|
down
|
mws0628
|
4-Hydroxybenzaldehyde
|
down
|
Lmbn001981
|
2,5-Dihydroxybenzaldehyde
|
down
|
mws0749
|
4-Hydroxybenzoic acid
|
down
|
Hmgn001653
|
Protocatechualdehyde
|
down
|
Lmgn001670
|
Salicylic acid
|
up
|
mws0103
|
Indole-3-carboxaldehyde
|
down
|
mws2213
|
Cinnamic acid
|
down
|
mws0458
|
Vanillin
|
down
|
mws0098
|
Indole-2-carboxylic acid
|
down
|
Variations in Differentially Accumulated Metabolites of Triterpenes
To compare the differences in the content of individual metabolites in different samples, the mass spectral peaks of the metabolites detected in different samples were calibrated according to their holding times and peak shape variations to ensure accurate qualitative and quantitative analyses. As shown in Figure 3, three differential triterpenoids were detected, including betulinic alcohol, betulinic acid, and 2-hydroxyoleanolic acid. After elicitor treatment (ET), the level of betulinic acid reached 2.62 times that in the without elicitor treatment (WET) and the level of 2-hydroxyoleanolic acid reached 114.67 times that in the WET, which was significantly higher than that in the control (Additional Files 2, Table S2). In sum, ET could appreciably impact the addition of different triterpenoids in S. sanghuang.
Transcriptomic Analysis of S. sanghuang from ET and WET
The data obtained were evaluated by sequencing the samples of elicitor treatment (ET) and without elicitor treatment (WET) using RNA-seq. The percentage of Q30 bases ranged from 94.69% and 95.66% (Table 1). The GC levels of WET and ET ranged from 52.52%-52.63% and 52.34%-52.56%, respectively (Table 2).
During the detection of differentially expressed transcripts (DETs), 97 between the control and stimulated groups were filtered for fold change ≥2 and FDR <0.01; of these, 57 were upregulated and 40 were downregulated. GO was performed for further explanation and enrichment of information on the function of DETs and their role in relevant biological processes.
The GO classification statistics of DETs and the classification and enrichment analysis of GO functions of DETs are shown in Figure 4A. The results revealed 112 DETs in WET and ET. A comparison of the DETs in the WET and ET groups showed that 40 DETs in biological processes were significantly enriched in “metabolic processes,” “cellular processes,” “reproductive processes,” “signaling,” “multicellular biological processes,” “developmental processes,” “growth,” “stress response,” and “subcellular localization.” Among the molecular functional categories, 30 DETs were significantly enriched in “nucleic acid binding,” “transcription factor activity,” “catalytic activity,” “signal transduction,” “structural molecules,” “transport,” “binding,” “electron carriers,” “antioxidant activity,” “protein labeling,” “translation regulation,” and “nutrition.” About 41 DETs were significantly enriched in “cells,” “cell membranes,” “macromolecular complex,” and “organelles” in cellular components.
Thus, the transcriptional analysis also indicated that the transcriptional profile of ET significantly affected the metabolic pathway of S. sanghuang. As shown in Figure 4B, DETs were annotated to the COG database and categorized, showing that “carbohydrate transport and metabolism” (7, 26.9%) accounted for the largest proportion of the 26 COG categories, followed by “defense mechanisms” (3, 11.5%) and “secondary metabolite biosynthesis, transport, and catabolism” (3, 11.5%). A large proportion of the remaining genes were annotated to “signal transduction mechanisms” (2, 7.7%); “intracellular transport,” “secretion and vesicular transport” (2, 7.7%), and “inorganic ion transport and metabolism” (2, 7.7%); and only general function prediction (2, 7.7%). Only a small fraction (less than 4%) of DETs were assigned to “cell motility” (1, 3.8%), “post-translational modifications, protein turnover, molecular chaperones” (1, 3.8%), “energy production and conversion” (1, 3.8%), and “coenzyme transport and metabolism” (1, 3.8%).
To explore the differential transcript protein annotation information, DETs were annotated to the egg NOG database (Fig 4C), and the 47 functional groups were categorized. DETs were enriched in “carbohydrate transport and metabolism” (8, 17.0%), followed by “signal transduction mechanisms” (3, 6.4%), “secondary metabolite biosynthesis, transport, and catabolism” (3, 6.4%). A large proportion of the remaining DETs were annotated to “replication, recombination, and repair” (2, 4.2%), “cytoskeleton” (2, 4.2%), “energy production and conversion” (2, 4.2%), and “inorganic ion transport and metabolism” (2, 4.2%). Only a little fraction of DETs were annotated to “transcription” (1, 2.1%).
From the results of differential metabolism gene annotation and enrichment analysis, the fungal elicitors could effectively promote the enrichment of differential genes in terpene and sesquiterpene metabolic pathways and triterpene synthesis pathways. They may assume a significant part in the secondary metabolic pathways and defense gene activation of S. sanghuang. Differential transcript information is presented in Additional Files 2, Table S1.
Table 2
Analysis of transcriptome sequences of S. sanghuang from ET and WET
Sample
|
Read Number
|
Base Number
|
GC Content
|
% ≥Q30
|
WET1
|
25,246,027
|
7,542,093,106
|
52.63
|
94.69
|
WET2
|
21,712,576
|
6,500,736,324
|
52.62
|
95.57
|
WET3
|
21,352,924
|
6,380,883,072
|
52.52
|
95.58
|
ET1
|
23,375,056
|
6,979,323,872
|
52.34
|
95.41
|
ET2
|
22,783,195
|
6,808,055,780
|
52.56
|
95.66
|
ET3
|
25,204,949
|
7,526,855,660
|
52.53
|
95.66
|
Verification of Differential Expressed Transcripts (DETs) by qRT-PCR
As shown in Figure 5, there was a highly significant difference between ET and WET, with ET exerting a facilitative effect on DETs. Four transcripts related to secondary metabolism include defense mechanisms (Transcript_20259 and Transcript_41678: cytochrome P450 [Sanghuangporus baumii]), and signaling pathways (Transcript_20207: hypothetical protein A7U60_g2886 [Sanghuangporus baumii]) Transcript_4013: sulfate anion transporter [Sanghuangporus baumii]) were analyzed by qRT-PCR according to the designed primers. The discoveries were compatible with the results of transcriptome analysis, which verified the authenticity of the transcriptome data. The amplification and lysis curves are shown in Additional Files 1, Figure S1, Figure S2, Figure S3, Figure S4, Figure S5.
Correlation Analysis between DAMs and DETs
Relevance analyses were performed for differentially accumulated metabolites (DAMs) and differentially expressed transcripts (DETs). The metabolites and the corresponding genes were screened by the correlation coefficient (CC) >0.80 and P value of correlation <0.05. As shown in Figure 6, differentially accumulated metabolites and differentially expressed genes shared a positive relationship in the third and seventh quadrants. We connected diverse gene clusters with differential triterpene metabolites all found in the third quadrant, indicating that after exciton, signal transduction mechanisms; biosynthesis, transport, and catabolism of secondary metabolites; and defense mechanisms are closely related to the production of pentacyclic triterpene metabolites 2-hydroxyoleanolic acid and betulinic acid.
Effect of Mevalonate pathways from elicitors treated
Mevalonate pathway (MVA pathway), one of the metabolic pathways of terpenoids, forms isopentene pyrophosphate (IPP) in the cytoplasm with the glycolytic product acetyl CoA molecule as the primary donor. The transcriptome results showed that elicitors treated produced upregulation of the enzymes in the MVA pathway process and promoted the accumulation of terpenoids shown in Figure 7. Annotation revealed that six of the seven enzymes in this pathway were affected, except MVK, and that multiple enzymes were affected by different transcripts. As a key rate-limiting enzyme of the MVA pathway, HMGR was found to be up-regulated by seven genes in transcriptional results. It catalyzes the synthesis of mevalonate by acetyl CoA, which in turn synthesizes isopentene pyrophosphate (IPP) by further action of the enzyme, which in turn generates dimethylallyl pyrophosphate (DMAPP), another precursor of terpenoids. Similarly, the elicitors treated promotes the synthesis of GPP and FPP, important precursors of terpenoids, which undoubtedly has a positive effect on the synthesis of monoterpenes, diterpenes and triterpenes later on.
Mevalonate kinase (MVK) is one of the rate-limiting enzymes of MVA, and the magnitude of mevalonate kinase activity plays an important role in the rate of terpenoid synthesis and has an important effect on the yield of terpenoids. Unfortunately, the effect of exciton on its transcriptional results was not found in this experiment.
Detection of Total Triterpene Content
To further verify the triterpene changes of S. sanghuang after the addition of elicitors, we measured the total triterpene content, and the results are as follows. During the whole fermentation process, the triterpene content with fungal polysaccharide elicitors of S. sanghuang showed a general trend of increase, decrease, and then increase. The total triterpene accumulation reached a maximum during the whole fermentation process on day 5, the trend is shown in Figure 8. The highest total triterpene content was 1.95% compared with that in the without elicitor treatment group, which is an increase of 37.11%. The results demonstrated that the addition of elicitors could effectively increase the yield of total triterpenes.
Detection of NOS Activity
Nitric oxide synthase (NOS) is reported to be a key enzyme in the defense mechanism. To verify whether this defense mechanism is effective, we performed a NOS activity assay in the elicitor treatment (ET) group. The results are shown in Figure 9. On day 4-7 of incubation, the NOS content in the ET group was more stable compared with that in the without elicitor treatment (WET) group, with a general trend of first decreasing and then increasing to reach a maximum value of 0.92 u/mgprot on day 4 of incubation, which was 61.4% higher than that in the WET group. In conclusion, it was demonstrated that the addition of elicitors could cause the onset of defense reactions in S. sanghuang.
Detection of NO Content
Nitric oxide (NO) plays a vital role in the triterpene signaling pathway. To verify whether the addition of elicitor treatment affects the signaling path of S. sanghuang and the authenticity of the transcriptome results, we measured the NO content (Figure 10). On the fourth day of addition, the NO content was distinctly higher than that in the without elicitor treatment group. The results demonstrated that the elicitor promoted the production of the signaling molecule NO in S. sanghuang, thus verifying the authenticity of the transcriptome data at the molecular level.