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Multiomics reveals the molecular mechanism of unsaturated fatty acid-induced terpenoid biosynthesis in Sanghuangporus…

Abstract

Treatment with C18:1 and C18:2, but not C18:0, increased the triterpenoid content of the medicinal fungus Sanghuangporus lonicericola. We identified 413 terpenoids, including 210 volatile terpenoids. Eight upregulated terpenoids, including 3,13,15-trihydroxyoleanane-12-one, dulcioic acid and serrat-14-ene-3,20,24,29-tetrol, were shared between the C18:1 and C18:2 treatments but not the C18:0 treatment. The C18:1 and C18:2 treatments increased the levels of 12 and 7 odour-related terpenoids, respectively, and increased the level of alpha-farnesene (herbal odour). Gene set enrichment analysis revealed that compared with C18:0, C18:1 and C18:2 produced stronger activation of the terpenoid biosynthesis, fatty acid degradation, and MAPK signalling pathways and stronger inhibition of basal transcription factors at both the transcript and protein levels. Finally, two-way orthogonal partial least squares analysis revealed that gene and protein expression in the identified pathways was correlated with levels of unsaturated fatty acid-induced terpenoid metabolites. Together, our integrated multiomics data revealed the key pathways involved in unsaturated fatty acid-induced terpenoid biosynthesis in S. lonicericola.

Introduction

Species of the genus Sanghuangporus, also known as Phellinus or Inonotus, are edible mushrooms with a long history of use as medicines in East Asia1. Sanghuangporus is recognised as one of the most effective anticancer medicinal fungi and has antioxidant2,3, anti-inflammatory4, hypoglycaemic5 and hepatoprotective effects5,6. The major specialised metabolites with pharmacological activity in Sanghuangporus include terpenoids, polysaccharides, polyphenols, and flavonoids, all of which are present in fruiting bodies and mycelia7. Terpenoids, including sesquiterpenoids, diterpenoids and triterpenoids, are among the main medicinal components of Sanghuangporus8. The triterpenoid content is an important indicator of the medicinal efficacy and value of Sanghuangporus9. However, the terpenoid compounds present in Sanghuangporus are still unclear. Under natural conditions, terpenoid compounds, especially triterpenoids, are present at low levels in Sanghuangporus.

Several inducers can promote the expression of terpenoid biosynthesis and increase the yield of terpenoids through signal transduction pathways in Sanghuangporus. The plant hormones methyl jasmonate and salicylic acid can promote terpenoid biosynthesis in Sanghuangporus baumii10,11,12. Exogenous unsaturated fatty acids (UFAs), such as oleic acid (C18:1) and linoleic acid (C18:2), promote triterpenoid production during the submerged fermentation of S. baumii13. The effects of C18:2 on triterpenoid production are tightly correlated with substrate supply, changes in the cell membrane and metabolic regulation of the triterpenoid biosynthetic pathway in S. baumii14. In Ganoderma lingzhi, treatment with coix seed oil, in which C18:1 and C18:2 account for 49.06% and 27.03% of the total fatty acids, respectively, also induces significant triterpenoid accumulation15. The overexpression of delta-9 fatty acid desaturase genes leads to significant increases in C18:1 and C18:2 contents and triterpenoid biosynthesis in G. lucidum16. However, little is known regarding the mechanism by which UFAs induce terpenoid metabolism in filamentous fungi.

In this study, we investigated the effects of six different FAs, palmitic acid (C16:0), palmitoleic acid (C16:1), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), and arachidonic acid (C20:4), on mycelial growth and triterpenoid production in S. lonicericola. Furthermore, integrated analyses of metabolomics, transcriptomics and proteomics were performed to identify global differences in terpenoid metabolites, volatile metabolites, genes and proteins in S. lonicericola under C:18:0, C18:1 and C18:2 treatments. This study contributes to elucidating the molecular mechanism underlying UFA-induced terpenoid biosynthesis in Sanghuangporus and expands our knowledge of the metabolic processes by which filamentous fungi respond to UFAs.

Results

Effects of UFAs on mycelial growth and triterpenoid production

To investigate the effects of UFAs on mycelial growth and triterpenoid production in S. lonicericola, six different FAs with different chain lengths, including saturated FAs (C16:0 and C18:0), monounsaturated FAs (C16:1 and C18:1), and polyunsaturated FAs (C18:2 and C20:4), were separately added to the liquid fermentation media. The addition of C16:1, C18:1, C18:2, or C20:4 significantly (p < 0.01) promoted triterpenoid production, whereas the addition of C16:0 or C18:0 had no significant effects on triterpenoid production or mycelial growth (Fig. 1a). A significant increase in mycelial growth and triterpenoid content was observed after 3 d of cultivation in fermentation medium supplemented with C18:1 compared with those in the control medium. The mycelial dry weight and triterpenoid yield of the C18:1-treated samples on Day 7 were 22.77 g/L and 60.98 mg/g, respectively, which were 1.84- and 3.04-fold greater than those of the control group (Fig. 1b).

Fig. 1: Effect of oleic acid on mycelial growth and triterpenoid biosynthesis in S. lonicericola.

figure 1

The indicated fatty acid at 2% (v/v) was added to the liquid medium at the beginning of the culture. a Effects of different fatty acids on the dry weight and triterpenoid content of S. lonicericola on Day 7 of culture. No fatty acid treatment was used for the control group. The black and red asterisks indicate significant differences from the biomass control and triterpenoid content, respectively (**P < 0.01 by Student’s t-test). b Changes in the dry weight and triterpenoid content of S. lonicericola mycelia over time after C18:1 treatment. The values are presented as the mean ± SD of three independent experiments.

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Terpenoid metabolic changes in response to unsaturated fatty acids

To better understand how metabolic processes are modulated during the S. lonicericola response to UFAs and eliminate the interference of saturated FAs and on metabolism, targeted terpenoid metabolomics analyses were performed to further analyse the control (CK), FAs with same chain lengths that C18:0-, C18:1- and C18:2-treated samples (hereafter, CK, C18:0, C18:1, and C18:2). A total of 203 terpenoid metabolites were identified via UPLC‒ESI‒MS/MS, including 44 triterpenoids, 33 diterpenoids, 104 sesquiterpenoids, 21 monoterpenoids, and 1 terpene (Supplementary Data 1). The OPLS-DA results of the CK vs. C18:0, CK vs. C18:1, and CK vs. C18:2 comparisons were separately clustered into two groups (Supplementary Fig. 1a). The R2Y and Q2 values were greater than 0.99, indicating that the O2PLS model had good interpretability and predictive ability (Supplementary Fig. 1b). Principal component analysis (PCA) score plots revealed that the samples were clearly separated based on the CK, C18:0, C18:1 and C18:2 treatment samples (Fig. 2a).

Fig. 2: LC‒MS/MS targeting analysis of terpenoid metabolism.

figure 2

a Principal component analysis of the terpenoid metabolome data. b Venn diagrams of terpenoid metabolites upregulated in C18:0, C18:1 and C18:2 compared with CK. c Heatmap of metabolites upregulated in C18:0, C18:1 and C18:2 compared with CK. d Radar chart showing the top 10 terpenoid metabolites upregulated in C18:0, C18:1 and C18:2 compared with CK.

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Compared with the control, a total of 99, 130, and 128 terpenoid metabolites were differentially abundant in C18:0, C18:1 and C18:2, respectively (Supplementary Data 1). Venn diagram analysis revealed that 11, 4 and 32 upregulated terpenoid metabolites were specific to C18:0, C18:1 and C18:2, respectively. Two upregulated sesquiterpenoids, dehydroxyisocalamendiol and eupatorenone, were present in C18:0, C18:1 and C18:2 (Fig. 2b, c). Among the top 10 upregulated terpenoid metabolites, the diterpenoids 7β-hydroxydarutigenol, ent-16α,17-dihydroxykauran-2-one, and 8,11,13,15-tetraene-3α-hydroxy-abieta were specific to C18:0, C18:1 and C18:2, respectively (Fig. 2d). Notably, 8 upregulated terpenoid metabolites were shared by C18:1 and C18:2 but not C18:0, including triterpenoids 3,13,15-trihydroxyoleanane-12-one, dulcioic acid and serrat-14-ene-3,20,24,29-tetrol (Fig. 2b, c). These results indicate that the effects of C18:0, C18:1, and C18:2 on triterpenoid metabolism differ and that the changes in triterpenoid metabolism caused by C18:1 and C18:2 are similar, which is consistent with the results shown in Fig. 1.

Changes in volatile metabolism caused by unsaturated fatty acids

To gain a more comprehensive understanding of the impact of UFA on terpenoid metabolism, we further analysed the effects of UFAs on the modulation of volatile organic compounds (VOCs), which include terpenoids, phenylpropanoids/benzenoids, fatty acid derivatives, and amino acid derivatives, among others17,18. A total of 806 VOCs, including 210 terpenoids, 155 esters, 68 hydrocarbons, 65 ketones, 52 alcohols, 40 aromatics and 102 heterocyclic compounds, were identified by HS-SPME-GC‒MS in S. nicericola. Among these VOCs, 364 were odour related, including 94 terpenoids (Supplementary Data 2). The OPLS-DA, which has good interpretability and predictive ability, separated the C18:0, C18:1, and C18:2 groups from the CK group (Supplementary Fig. 3). The PCA score plots revealed that the CK, C18:0, C18:1 and C18:2 treatment samples were clearly separated (Fig. 3a). Compared with their levels in CK, a total of 1, 79 and 64 VOCs, including 0, 22 and 8 terpenoids, respectively, were upregulated in C18:0, C18:1 and C18:2 (Supplementary Data 2). In addition, the C18:1 and C18:2 treatments resulted in increases in the levels of 13 and 7 odour-related terpenoids, respectively, and both treatments increased the levels of α-farnesene, which has an herbal odour (Fig. 3b, d). However, 91, 30 and 56 odour-related terpenoids were downregulated in the C18:0, C18:1 and C18:2 groups, respectively, compared with the control group. Venn diagram analysis revealed that 24 downregulated terpenoids were odour related and were shared by C18:0, C18:1 and C18:2, including 1,6,10-dodecatrien-3-ol, 3,7,11-trimethyl and caryophyllene oxide (Fig. 3c, e). Odour characteristic analysis revealed that the upregulated and downregulated terpenoid metabolites are associated with wood, floral, and herbal odours (Fig. 3f, g). These results indicate that UFAs can affect the production of such odours by impacting the synthesis of volatile terpenoids in S. lonicericola.

Fig. 3: HS‒SPME‒GC‒MS targeting analysis of volatile metabolism.

figure 3

a Principal component analysis of the volatile metabolism data. Venn diagrams of upregulated (b) and downregulated (c) terpenoid odour metabolites in C18:0, C18:1 and C18:2 compared with CK. Heatmap of upregulated (d) and downregulated (e) terpenoid odour metabolites in C18:0, C18:1 and C18:2 compared with CK. f Radar chart showing the odour characteristics of terpenoid metabolites upregulated in C18:1 and C18:2 compared with CK. g Radar chart showing the odour characteristics of terpenoid metabolites downregulated in C18:0, C18:1 and C18:2 compared with CK.

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Overview of transcriptomics and proteomics analysis

We subsequently conducted transcriptomic and proteomic analyses to elucidate the molecular mechanisms underlying UFA-induced terpenoid metabolism in S. lonicericola (Supplementary Data 3 and 4). The PCA separated the samples into CK and C18:0 versus C18:1 and C18:2 at both the transcription and protein levels, indicating that the transcript and protein compositions of C18:1 and C18:2 were different from those of the control, whereas those of C18:0 were similar to the control (Fig. 4a). The Pearson correlation coefficients (R) of the transcriptome data between CK and C18:0, C18:1 and C18:2 were 96%, 91% and 88%, respectively. The correlations of the proteomic data from CK with those from C18:0, C18:1 and C18:2 were 96%, 95% and 88%, respectively (Fig. 4b). The Pearson correlation coefficient results indicate that C18:1 and C18:2 have greater effects on the expression levels of transcripts and proteins than does C18:0. A higher correlation between transcript and protein levels was detected in CK (R = 0.281) and C18:0 (R = 0.289) than in C18:1 (R = 0.279) and C18:2 (R = 0.262) (Fig. 4c).

Fig. 4: Changes in transcription and protein levels after C18:0, C18:1 and C18:2 treatments.

figure 4

a Principal component analysis of the transcriptome and proteome data. b Pearson’s correlation coefficient of the transcriptome and proteome data. c Scatter plot of transcript versus protein levels of the identified genes in the CK, C18:0, C18:1 and C18:2 treatments. Pearson’s correlation coefficient (R) is displayed in the upper left corner of the image.

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Characteristics of differentially expressed genes and proteins after UFA treatment

As shown in Fig. 5a, a total of 764, 3103 and 5740 differentially expressed genes (DEGs) were identified in C18:0, C18:1 and C18:2, respectively, compared with CK. A total of 410, 1003 and 1469 genes were upregulated, and 354, 2100, and 4271 genes were downregulated in response to the C18:0, C18:1 and C18:2 treatments, respectively. Similarly, proteomic data analysis revealed a total of 645, 1305 and 1995 differentially abundant proteins (DEPs) in C18:0, C18:1 and C18:2, respectively, compared with CK. A total of 235, 561 and 845 proteins were upregulated and 410, 744, and 1150 proteins were downregulated in response to the C18:0, C18:1 and C18:2 treatments, respectively (Fig. 5a). In addition, a higher correlation of the fold changes in DEGs and DEPs was found in C18:1 vs. CK (R = 0.072) and C18:2 vs. CK (R = 0.079) than in C18:0 vs. CK (R = 0.022) (Fig. 5b). These results indicate that the changes in transcript and protein profiles caused by C18:1 and C18:2 are more consistent than those caused by C18:0.

Fig. 5: Differentially expressed genes and proteins after C18:0, C18:1 and C18:2 treatments.

figure 5

a Volcano map of differentially expressed genes and proteins. b Scatter plot of differentially expressed genes versus proteins in the C18:0, C18:1 and C18:2 treatment groups. Pearson’s correlation coefficient (R) is displayed in the upper left corner of the image. c Heatmap of differentially expressed genes and proteins. The expression data were grouped via hierarchical clustering. The expression profiles (left) depict coexpressed genes and proteins. Some significantly enriched KEGG terms (right) are given for each cluster.

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The DEGs and DEPs were grouped into 6 clusters (C1–C6) according to their expression patterns, which presented distinct KEGG functional enrichment signatures, as shown in Fig. 5c. The fatty acid degradation, biosynthesis of secondary metabolites, terpenoid backbone biosynthesis, diterpenoid biosynthesis, biquinone and other terpenoid-quinone biosynthesis, the MAPK signalling pathway, and basal transcription factors were characteristic of the DEGs in Clusters 4 and 6 and DEPs in Clusters 2, 3 and 6, which are the genes and proteins that were significantly upregulated in C18:1 and C18:2. These results are consistent with the increase in terpenoid metabolism in C18:1 and C18:2.

To further elucidate the mechanism by which terpenoid synthesis is induced by UFAs, we conducted KEGG enrichment analysis of the DEGs and DEPs in both C18:1 and C18:2 and excluded those in C18:0. Venn diagram analysis revealed that 518 DEGs were upregulated in C18:1 and C18:2 but not in C18:0 (Fig. 6a). These 518 coupregulated DEGs were analysed via KEGG, and clusters related to the biosynthesis of secondary metabolites, fatty acid degradation, the MAPK signalling pathway, and basal transcription factors were found to be enriched (Fig. 6b). We also identified 282 DEPs that were upregulated in both C18:1 and C18:2 but not in C18:0 (Fig. 6a). These 282 coupregulated DEPs were enriched in fatty acid degradation, biosynthesis of secondary metabolites, and the MAPK signalling pathway according to KEGG enrichment analysis (Fig. 6b). In addition, Venn diagram analysis revealed that 1471 DEGs and 377 DEPs were codownregulated in C18:1 and C18:2 but not in C18:0 (Fig. 6c). These DEGs and DEPs were enriched in the biosynthesis of secondary metabolites, fatty acid degradation, MAPK signalling, and basal transcription factors according to KEGG enrichment analysis (Fig. 6d).

Fig. 6: KEGG enrichment analysis of genes and proteins differentially expressed in both C18:1 and C18:2 but not in C18:0.

figure 6

a Venn diagrams of genes and proteins upregulated in C18:0, C18:1 and C18:2 compared with CK. b KEGG enrichment analysis of genes and proteins upregulated in both C18:1 and C18:2 but not in C18:0. c Venn diagrams of genes and proteins downregulated in C18:0, C18:1 and C18:2 compared with CK. d KEGG enrichment analysis revealed that genes and proteins were downregulated in both C18:1 and C18:2 but not in C18:0.

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GSEA of the key pathways associated with UFA treatment

Gene set enrichment analysis (GSEA) of genes and proteins related to UFA treatment was conducted to obtain meaningful insights into the associated metabolic and signalling pathways. Four KEGG pathways, (terpenoid biosynthesis, fatty acid degradation, MAPK signalling, and basal transcription factor) were selected for GSEA. GSEA revealed that the enrichment scores of the terpenoid biosynthesis pathway (sesquiterpenoid, triterpenoid, ubiquinone and another terpenoid-quinone biosynthesis) at the transcript level increased from −0.290 for C18:0 vs. CK to 0.279 for C18:1 vs. CK and to 0.385 for C18:2 vs. CK (Fig. 7a). Similarly, the enrichment scores of the terpenoid biosynthesis pathway at the protein level increased from −0.426 for C18:0 vs. CK to 0.592 for C18:1 vs. CK and to 0.293 for C18:2 vs. CK (Fig. 7b). These results indicate that, compared with the C18:0 treatment, the C18:1 and C18:2 treatments increased the levels of transcripts and proteins related to terpenoid biosynthesis more strongly. GSEA also revealed that the enrichment scores of fatty acid degradation pathways at the protein level increased from 0.306 for C18:0 vs. CK to 0.501 for C18:1 vs. CK and to 0.460 for C18:2 vs. CK, indicating that C18:1 and C18:2 more strongly activate fatty acid degradation pathways than does C18:0.

Fig. 7 : GSEA of the key pathways.

figure 7

GSEA comparing the gene (a) and protein (b) expression of terpenoid biosynthesis, fatty acid degradation, MAPK signalling, and basal transcription factor DEGs for C18:0, C18:1 and C18:2 compared with CK. The enrichment scores are displayed near the curve and distinguished by colour.

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In addition, the enrichment scores of the proteins associated with the MAPK signalling pathway increased from 0.224 for C18:0 vs. CK to 0.281 for C18:1 vs. CK and to 0.269 for C18:2 vs. CK (Fig. 7b), indicating greater activation of the MAPK signalling pathway in C18:1 and C18:2 than in C18:0. In addition, the enrichment scores of basal transcription factors at the transcription level decreased from 0.210 for C18:0 vs. CK to 0.180 for C18:1 vs. CK and to −0.275 for C18:2 vs. CK (Fig. 7a). The enrichment scores of the basal transcription factors at the protein level also decreased from −0.220 for C18:0 vs. CK to −0.325 for C18:1 vs. CK and to −0.281 for C18:2 vs. CK (Fig. 7b). These results indicate greater inhibition of basal transcription factors by C18:1 and C18:2 than by C18:0.

O2PLS combined modelling of transcripts or proteins with metabolite data

Next, two-way orthogonal partial least squares (O2PLS) analysis was performed to investigate the relationships between the DEGs or DEPs and metabolic components. We conducted correlation analysis of the 6940 DEGs, 2601 DEPs and 203 terpenoid metabolites via O2PLS modelling (Supplementary Data 5). We selected 2 genes or proteins in each of the 4 pathways (the terpenoid biosynthesis pathway, fatty acid degradation pathway, MAPK signalling pathway, and basal transcription factor pathway) that were strongly correlated with the metabolite data and visualised their expression patterns via heatmaps (Fig. 8).

Fig. 8: O2PLS combined modelling of transcripts or proteins with terpenoid metabolite data.

figure 8

Correlation analysis between differentially expressed genes (a) or proteins (b) and differential terpenoid metabolites via O2PLS modelling. The heatmap shows the expression levels of 8 genes (a) or proteins (b) strongly related to 4 terpenoid metabolites. The colour coding indicates different functional categories of genes or proteins, as shown in the GSEA diagram in Fig. 7.

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Two genes, G27286 and G05249, encoding trichodiene synthase (K12249) and 4-hydroxybenzoate polyprenyltransferase (K06125), respectively, are involved in terpenoid biosynthesis, and their expression levels were positively correlated with 4 terpenoid compounds: pmp000436 (13-methyl-27-norolean-en-3-ol, a triterpenoid), Zbqn008291 (3,13,15-trihydroxyoleanane-12-one, a triterpenoid), Yalp012806 (neocembrene, a diterpenoid) and Jmcp010061 (costic acid, a sesquiterpenoid) (Fig. 8a). The levels of two fatty acid degradation genes, G12781 and G30602, encoding cytochrome P450 (K14338) and delta3-delta2-enoyl-coa isomerase (K07517), respectively, and two MAPK signalling genes, G18130 and G05508, encoding NADPH-dependent methylglyoxal reductase (K17741) and guanine nucleotide-binding protein alpha-1 subunit (K19860), respectively, were positively correlated with the 4 terpenoid compounds, as shown in Fig. 8a. In addition, the levels of two basal transcription factor genes, G21009 and G02118, which encode the transcription initiation factor TFIIE subunit beta (K03137) and the transcription initiation factor TFIID subunit 15 (K14651), respectively, were negatively correlated with levels of the 4 terpenoid compounds (Fig. 8a).

For 5 of the 8 genes mentioned above (G27286, G05249, G12781, G30602, and G18130), the levels of the corresponding proteins were also positively correlated with levels of the 4 terpenoid metabolites, which is consistent with results at the transcriptional level (Fig. 8b). For the remaining 3 proteins, the STE12 transcription factor in the MAPK signalling pathway (G31767, K11215) was positively correlated and the CDK-activating kinase assembly factor MAT1 (G35581, K10842) and the transcription initiation factor TFIID subunit 15 (G02119, K14651) were negatively correlated with the 4 terpenoid compounds (pmp000436, Zbqn008291, Yalp012806, Jmcp010061) (Fig. 8b).

We further conducted a correlation analysis between 6940 DEGs or 2601 DEPs and 806 VOCs via O2PLS modelling (Supplementary Data 6). As illustrated in Fig. 9a, the same 8 genes as in Fig. 8a, G27286, G05249, G12781, G30602, G18130, G05508, G21009, and G02118, were also correlated with the 5 VOCs WMW0052 (1-methyl-4-(6-methylhept-5-en-2-ylidene) cyclohex-1-ene, a terpenoid), KMW0613 (alpha-farnesene, a terpenoid), GMW0078 (beta-bisabolene, a terpenoid), NMW0240 (4-hydroxybenzoic acid) and KMW0572 (trans-cinnamic acid). Six genes, G27286, G05249, G12781, G30602, G18130 and G05508, were positively correlated with the 5 volatile metabolites, and 2 genes, G21009 and G02118, were negatively correlated with the 5 volatile metabolites (Fig. 9a).

Fig. 9 : O2PLS combined modelling of transcripts or proteins with volatile metabolite data.

figure 9

Correlation analysis between differentially expressed genes (a) or proteins (b) and differential volatile metabolites via O2PLS modelling. The heatmap shows the expression levels of 8 genes (a) or proteins (b) strongly related to 5 volatile metabolites. The colour coding indicates different functional categories of genes or proteins, as shown in the GSEA diagram in Fig. 7.

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In addition, 5 of the 8 genes mentioned above, G27286, G05249, G12781, G30602 and G18130, were positively correlated with the 5 VOCs at the protein level (Fig. 9b), which is consistent with the results at the transcript level (Fig. 9a). For the three proteins identical to those in Fig. 8b, G31767 was positively correlated and G35581 and G02119 were negatively correlated with the 5 volatile metabolites (WMW0052, KMW0613, GMW0078, NMW0240, and KMW0572) (Fig. 9b). Moreover, KMW0613 (alpha-farnesene, a terpenoid) and GMW0078 (beta-bisabolene, a terpenoid) have herbal and woody odour qualities, respectively (Fig. 3 and Supplementary Data 2).

Overall, C18:1 and C18:2, but not C18:0, promote the production of triterpenoids in S. lonicericola. Three triterpenoids, 3,13,15-trihydroxyoleanane-12-one, dulcioic acid and serrat-14-ene-3,20,24,29-tetrol, were upregulated in C18:1 and C18:2 but not in C18:0. C18:1 and C18:2, but not C18:0, produced increases in the levels of 12 and 7 odour-related terpenoids, respectively, and both treatments increased the levels of alpha-farnesene, which has an herbal odour. Additionally, C18:1 and C18:2 had greater effects on transcription and protein expression levels than did C18:0. Moreover, at both the transcript and protein levels, compared with C18:0, C18:1 and C18:2 resulted in greater activation of the terpenoid biosynthesis, fatty acid degradation, and MAPK signalling pathways and greater inhibition of basal transcription factors. Finally, the transcript levels of the terpenoid biosynthesis genes G27286 and G05249, the fatty acid degradation genes G12781 and G30602, and the MAPK signalling genes G18130 and G05508 were positively correlated with the levels of 4 terpenoids (13-methyl-27-norolean-en-3-ol, a triterpenoid; 3,13,15-trihydroxyoleanane-12-one, a triterpenoid; neocembrene, a diterpenoid; and costic acid, a sesquiterpenoid) and 3 volatile terpenoids (1-methyl-4-(6-methylhept-5-en-2-ylidene) cyclohex-1-ene, alpha-farnesene, and beta-bisabolene). The levels of the basal transcription factor genes G21009 and G02118 were negatively correlated with the levels of the 4 terpenoids and the 3 volatile terpenoids. Finally, the proteins encoded by 5 of the 8 genes mentioned above, G27286, G05249, G12781, G30602, and G18130, were also correlated with levels of the 4 terpenoids and the 3 volatile terpenoids. For three proteins, G31767 in the MAPK signalling pathway was positively correlated and the basal transcription factors G35581 and G02119 were negatively correlated with the 4 terpenoids and the 3 volatile terpenoids.

Discussion

Terpenoids are an important class of secondary metabolites that play important roles in food, agriculture, and medicine. The biosynthesis of Sanghuangporus terpenoids involves a complex metabolic network involving several key intermediates and a series of enzymatic reactions. A typical study might involve working with Sanghuangporus; performing transcriptomic, metabolomic, and proteomic analyses; synthesising multiple types of omics data to construct regulatory networks for terpenoid biosynthesis and UFA response; and identifying key genes, metabolites, and proteins. Here, we investigated the molecular mechanism by which UFAs enhance terpenoid biosynthesis by comparing the differences in the terpenoid metabolome, volatile metabolome, transcriptome, and proteome among CK and C18:0, C18:1 and C18:2 in S. lonicericola. Our integrated multiomics analysis revealed the crucial roles of genes and proteins in terpenoid biosynthesis, fatty acid degradation, MAPK signalling, and transcription factor pathways in UFA-induced terpenoid biosynthesis in S. lonicericola. Our report established a systemic link between the UFA response and secondary metabolism in filamentous fungi.

Accumulating evidence indicates that the addition of exogenous UFAs can increase the biosynthesis of target metabolites in some microorganisms. The addition of exogenous C18:2 increased carotenoid production in Saccharomyces cerevisiae19. In Inonotus obliquus, C18:2 increased total triterpenoid production20. Coix seed oil, composed mainly of C18:1 (49.06%) and C18:2 (27.03%), induced triterpenoid accumulation in G. lingzhi15. These results are similar to our findings that C18:1 and C18:2 induce triterpenoid accumulation in S. lonicericola. Transcriptomic analysis of the macrofungus Morchella esculenta revealed that C18:1 increased the production of triterpenoids by increasing the expression of triterpenoid synthase genes and that the associated DEGs were involved mainly in carbohydrate metabolism, lipid metabolism, and transport and catabolic processes21. The effects of exogenous C18:2 on triterpenoid production are tightly correlated with substrate supply, changes in the cell membrane and metabolic regulation of the triterpenoid biosynthetic pathway in S. baumii14. Similarly, our GSEA results further revealed that C18:1 and C18:2 treatment increased the transcript and protein levels of genes related to terpenoid biosynthesis to a greater degree than did C18:0. Compared with previous reports, we not only analysed the targeted terpenoid metabolome but also analysed the volatile terpenoids. In addition, our multiomics systematically analysed C18:0-, C18:1- and C18:2-treated samples which revealed the molecular mechanism of UFA-induced terpenoid biosynthesis eliminates the interference of saturated FAs.

Acetyl-CoA is an important cofactor that is involved in many metabolic pathways. It serves as a precursor for many compounds with interesting commercial applications such as terpenes, flavonoids and anthraquinones22. The terpenoid backbone biosynthesis (map00900) pathway starts with acetyl-CoA23, which can be produced through fatty acid degradation24. Engineering strategies developed to improve terpenoid production in the oleaginous yeast Yarrowia lipolytica have included increasing the catalytic efficiency of terpenoid synthases and increasing the supply of acetyl-CoA25. Our GSEA revealed that C18:1 and C18:2 more strongly activate fatty acid degradation pathways than does C18:0. A possible molecular mechanism by which C18:1 and C18:2 but not C18:0 might induce terpenoid biosynthesis involves increasing the supply of acetyl-CoA by more strongly intensifying fatty acid degradation in S. lonicericola. Moreover, as cell membrane components, UFAs play key roles in membrane fluidity26. Increasing lipid unsaturation can increase membrane fluidity in organisms ranging from bacteria to mammals27,28. A preliminary study revealed that membrane fluidity is involved in the regulation of triterpenoid biosynthesis in G. lucidum16. Therefore, UFA-induced terpenoid biosynthesis in S. lonicericola may occur via an increase in membrane fluidity.

A previous study revealed that MAPK/sucrose-nonfermenting serine‒threonine protein kinase 1-mediated metabolic remodelling is involved in triterpenoid biosynthesis in G. lucidum29. In the fungus, Colletotrichum fructicola, the transcription factor STE12 in the MAPK signalling pathway modulates the expression of genes related to melanin biosynthesis30. Correlation analyses between gene transcription and metabolite concentrations revealed that MYB transcription factors are strongly associated with lupane-type triterpenoid biosynthesis in apples31. The jasmonate-responsive bHLH transcription factor TaMYC2 positively regulates triterpenoid biosynthesis in the herb Taraxacum antungense32. A signalling pathway with the bHLH transcription factor SREBP acts downstream of a phosphoinositide 3-kinase-like protein kinase in Ganoderma triterpenoid biosynthesis33. Conversely, the DOF-type transcription factor DAG1 prevents the expression of triterpenoid pathway genes in Arabidopsis34. MYB44 negatively regulates the biosynthesis of the terpenoid citral by directly binding to the promoters of ADH-encoding genes35. The transcription initiation factor TFIID plays a central role in the initiation of RNA polymerase II-dependent transcription by nucleating preinitiation complex assembly at the core promoter and can interact with gene-specific activators and repressors36. Additionally, alpha-farnesene, a type of acyclic sesquiterpene, is an important raw material in agriculture, aircraft fuel, and the chemical industry. Overproduction of alpha-farnesene in S. cerevisiae can be achieved via farnesene synthase screening and metabolic engineering37,38. MdMYC2 and MdERF3 positively coregulate alpha-farnesene biosynthesis in apples39. The transcription factor MdLSD1 negatively regulates alpha-farnesene biosynthesis in the skin of apple fruits40. Our research provides a potential strategy and genetic metabolic engineering target for the overproduction of alpha-farnesene. Further research is needed on how MAPK signalling and specific transcription factors regulate terpenoid biosynthesis in S. lonicericola.

In summary, in this study, we performed integrated metabolomics, transcriptomics and proteomics analyses to examine the molecular mechanisms involved in the S. lonicericola response to UFAs. We successfully identified 203 terpenoid metabolites via UPLC‒ESI‒MS/MS and 806 VOCs, including 210 terpenoids, via HS‒SPME‒GC‒MS. Eight upregulated terpenoids, including the triterpenoids 3,13,15-trihydroxyoleanane-12-one, dulcioic acid and serrat-14-ene-3,20,24,29-tetrol, were identified in S. lonicericola after treatment with C18:1 or C18:2 but not C18:0. In addition, the C18:1 and C18:2 treatments increased the levels of 12 and 7 odour-related terpenoids, respectively, and both increased the levels of alpha-farnesene, which has a herbal odour. We further provided an in-depth analysis and discussion of the differentially expressed genes and proteins involved in terpenoid biosynthesis, fatty acid degradation, MAPK signalling, and basal transcription factor pathways. The GSEA results revealed stronger activation of the terpenoid biosynthesis pathway, fatty acid degradation pathway and MAPK signalling pathway and stronger inhibition of basal transcription factors in C18:1 and C18:2 than in C18:0. Moreover, we highlighted the correlations of differential gene and protein expression with UFA-induced terpenoid metabolites via O2PLS analysis. Our integrated multiomics data reveal the key pathways and genes involved in UFA-induced terpenoid biosynthesis and provide a valuable resource for further investigations of the molecular mechanisms of the UFA response and terpenoid biosynthesis in S. lonicericola.

Methods

Fungal strains and culture conditions

The S. lonicericola strains (Dai 15990) isolated and identified by Professor Dai Yucheng of Beijing Forestry University were used in this study. The strains were cultured on artificial media at 27 °C for 7 d with shaking at 180 rpm in the dark. The culture medium was composed of the following (g/L): corn flour (40), yeast extract (3), peptone (2), and KH2PO4 (1). Six different lipophilic FAs, palmitic acid (C16:0), palmitoleic acid (C16:1), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2), and arachidonic acid (C20:4), were directly added to the liquid medium at 2% (v/v) at the beginning of the culture period. No fatty acid treatment was used for the control group.

Determination of mycelial biomass and triterpenoid content

Biomass was obtained by centrifuging a sample at 4800 × g for 10 min and washing the precipitated cells three times with distilled water. The sample was air-dried on filter paper and then dried at 60 °C until a constant weight was reached; this value was recorded as the dry weight.

To determine the triterpenoid content, dried mycelia (0.1 g) were extracted with ethanol (40 mL) via ultrasonication (280 W). The supernatant was collected by centrifugation and dried under vacuum. The sample was dissolved in 0.4 mL of glacial acetic acid containing 4% vanillin. Next, 1 mL of perchloric acid was added, and the mixture was allowed to react at 60 °C for 15 min. Finally, 5 mL of glacial acetic acid was added at room temperature. The absorbance was measured at 551 nm with ursolic acid as the standard. The standard solution was diluted (0.001, 0.002, 0.004, 0.008, 0.016, and 0.032 mg/mL), and its absorbance was measured to create a calibration curve: Y(absorbance) = 18.439X(concentration) + 0.0044 (R2 = 0.9955).

Targeted terpenoid analysis

Terpenoid extraction: The biological samples were immediately placed in liquid nitrogen after harvesting and stored at −80 °C until use. The samples were vacuum freeze-dried, placed in a lyophilizer (Scientz-100F), and then ground (30 Hz, 1.5 min) to powder form using a grinder (MM 400, Retsch). Next, 50 mg of sample powder was weighed via an electronic balance (MS105DM) and dissolved in 1200 μL of 70% methanolic aqueous internal standard extract precooled at −20 °C (samples less than 50 mg were dissolved in a volume proportional to 1200 μL extractant per 50 mg sample). The mixture was vortexed 6 times for 30 s every 30 min. After centrifugation at 13,500 g for 3 min, the supernatant was recovered, and the sample was filtered through a 0.22 μm membrane and stored in an injection vial for UPLC‒MS/MS analysis.

UPLC conditions: The sample extracts were analysed via a UPLC‒ESI‒MS/MS system with an Agilent SB-C18 (1.8 µm, 2.1 mm × 100 mm) column. The mobile phase consisted of pure water with 0.1% formic acid as solvent A and acetonitrile with 0.1% formic acid as solvent B. Sample measurements were performed with the following gradient programme: starting conditions, 95% A, 5% B; a linear gradient to 5% A, 95% B over 9 min; maintenance, 5% A, 95% B for 1 min; restoration, 95% A, 5.0% B within 1.1 min; and maintenance, 95% A, 5.0% B for 2.9 min. The flow velocity was set as 0.35 mL per minute. The column oven was set to 40 °C, and the injection volume was 2 μL. Alternatively, the effluent was connected to an ESI-triple quadrupole-linear ion trap (QTRAP)-MS.

ESI-QTRAP-MS/MS: The ESI source operation parameters were as follows: source temperature, 500 °C; ion spray voltage (IS), 5500 V (positive ion mode)/−4500 V (negative ion mode); ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) at 50, 60, and 25 psi, respectively; and collision-activated dissociation (CAD) high. QQQ scans were acquired as MRM experiments with the collision gas (nitrogen) set to medium. DP (declustering potential) and CE (collision energy) for individual MRM transitions were performed with additional DP and CE optimisation. A specific set of MRM transitions was monitored for each period according to the metabolites eluted within this period.

Differentially abundant metabolite selection: Differentially abundant metabolites were identified as those with a predictive VIP > 1 and absolute Log2FC ≥ 1 (Sheets 2–4 of Supplementary Data 1 and Supplementary Fig. 2). VIP values were extracted from the OPLS-DA results, which also contained score plots and permutation plots generated via the R package MetaboAnalystR. R2Y and Q2 were used for OPLS-DA validation. The data were log-transformed (log2) and mean-centred prior to OPLS-DA. To check overfitting, a permutation test (200 permutations) was performed.

Volatilome analysis

Sample preparation and treatment: Materials were harvested, weighed, immediately frozen in liquid nitrogen, and stored at −80 °C until needed. The samples were ground to powder in liquid nitrogen. Five hundred milligrams of the powder were transferred immediately to a 20 mL headspace vial (Agilent, Palo Alto, CA, USA) containing saturated NaCl solution to inhibit any enzymatic reactions. The vials were sealed via crimp-top caps with TFE-silicone headspace septa (Agilent). At the time of SPME analysis, each vial was heated at 60 °C for 5 min, after which a 120 µm DVB/CWR/PDMS fibre (Agilent) was exposed to the headspace of the sample for 15 min at 60 °C.

GC‒MS conditions: After sampling, desorption of volatile organic compounds (VOCs) from the fibre was carried out in the injection port of the GC apparatus (Model 8890; Agilent) at 250 °C for 5 min in splitless mode. VOC identification and quantification were carried out via an Agilent Model 8890 GC and a 7000D mass spectrometer (Agilent) equipped with a 30 m × 0.25 mm × 0.25 μm DB-5MS (5% phenyl-polymethylsiloxane) capillary column. Helium was used as the carrier gas at a linear velocity of 1.2 mL/min. The injector temperature was maintained at 250 °C. The oven temperature was programmed from 40 °C (3.5 min), increased at 10 °C/min to 100 °C, increased at 7 °C/min to 180 °C, increased at 25 °C/min to 280 °C, and held for 5 min. Mass spectra were recorded in electron impact (EI) ionisation mode at 70 eV. The quadrupole mass detector, ion source and transfer line temperatures were set at 150, 230 and 280 °C, respectively. The retention time of the chromatographic column was calibrated with standard samples at intervals to correct the shift for data extraction. The GC chromatograms and peak identification did not reveal a shift in retention time among the samples (Supplementary Figs. 5 and 6).

MS in selected ion monitoring (SIM) mode was used for the identification and quantification of analytes41 via an MWGC database42. The MS data of the volatile metabolites in the samples were obtained and analysed by integrating peak areas. The retention times were calculated or calibrated using retention index calibration data. All ions were detected in different time periods according to the peak order. Some standard products have been used (Supplementary Data 7). The retention indices of 19 terpenoid odour metabolites were greater in C18:0, C18:1 and C18:2 than those in CK (Fig. 3d), as shown in Supplementary Data 8. If the retention time detected is consistent with the standard reference and all selected ions appear in the sample mass spectrum after background subtraction, it is determined to be the tentative substance.

The relative odour activity values (rOAVs) of the volatile compounds were calculated from the ratios of the relative concentrations. An rOAV ≥1 indicates that the compound has a direct contribution to the odour of the sample1) dominate the aroma of aged Chinese rice wine (Huangjiu) by molecular association. Food Chem. 383, 132370 (2022)." href="https://www.nature.com/articles/s41538-025-00407-w#ref-CR43" id="ref-link-section-d1178931e1563">43. The differential volatile metabolites selected were the same as the differential terpenoid metabolites. The VIP data of the differential volatile metabolites are shown in Sheets 2–4 of Supplementary Data 1 and in Supplementary Fig. 4.

Transcriptome sequencing analysis

Library preparation for transcriptome sequencing: A total of 1 μg of RNA per sample was used as input material for the RNA sample preparations. The sequencing libraries were generated via the NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer’s recommendations, and index codes were added to attribute the sequences to each sample. Library quality was assessed on an Agilent Bioanalyzer 2100 system. Clustering of the index-coded samples was performed on a cBot Cluster Generation System via the TruSeq PE Cluster Kit v3-cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced in 150-bp paired-end mode on an Illumina platform.

Data quality control and read mapping to the reference genome: fastp was used to filter the original data, mainly to remove reads with adaptors44. Paired reads were removed when the N content in any sequencing read exceeded 10% of the length of the read or when the number of low-quality (Q ≤ 20) bases contained in a read exceeded 50%. All subsequent analyses were performed with the clean reads only. The clean reads filtered from the raw reads were mapped to the S. lonicericola genome (PRJNA587366) via TopHat2 software45.

Gene differential expression analysis: StringTie was used to calculate the FPKM values46. DESeq2 was used to evaluate the differential expression between two groups, and P-values were corrected via the Benjamini–Hochberg method47. An FDR < 0.05 and a fold change ≥2 were set as the thresholds for significantly differential expression.

Quantitative proteomic analysis

Protein extraction: Proteins were extracted from the samples via the acetone precipitation method. Briefly, samples were ground into a fine powder in liquid nitrogen and homogenised with buffer (1% SDS, 100 mM Tris-HCl, 7 M urea, 2 M thiourea, 1 mM PMSF, and 2 mM EDTA). After shaking and mixing, the sample was ultrasonicated on ice for 10 min, and the supernatant was centrifuged to obtain the protein mixture. A 4× volume of frozen acetone was added to the protein mixture, the protein was allowed to precipitate at −20 °C overnight, and the mixture was subsequently centrifuged at 4 °C to retain the precipitate. The precipitate was washed with cold acetone and dissolved in 8 M urea. Finally, the protein concentration was determined with a BCA kit according to the manufacturer’s instructions.

Digestion and cleanup: Equal amounts of protein from each sample were subjected to tryptic digestion. First, 8 M urea was added to 200 μL of the supernatant, which was then reduced with 10 mM DTT for 45 min at 37 °C and alkylated with 50 mM iodoacetamide (IAM) for 15 min in the dark at room temperature. A 4× volume of chilled acetone was added, and the mixture was precipitated at −20 °C for 2 h. After centrifugation, the protein precipitate was air-dried, resuspended in 200 μL of 25 mM ammonium bicarbonate solution and 3 μL of trypsin (Promega) and digested overnight at 37 °C. After digestion, the peptides were desalted using a C18 cartridge, dried with a vacuum concentration metre, concentrated via vacuum centrifugation and redissolved in 0.1% (v/v) formic acid.

LC‒MS/MS analysis: Liquid chromatography (LC) was performed on a nanoElute UHPLC (Bruker Daltonics, Germany). Peptide samples (200 ng) were separated within 40 min at a flow rate of 0.3 μL/min on a commercially available reverse-phase C18 column with an integrated CaptiveSpray Emitter (25 cm × 75 μm ID, 1.6 μm, Aurora Series with CSI, IonOpticks, Australia). The separation temperature was maintained by an integrated Toaster column oven at 50 °C. Mobile phases A and B comprised 0.1% formic acid in water and 0.1% formic acid in ACN, respectively. Mobile phase B was increased from 2 to 22% over the first 25 min, increased to 35% over the next 5 min, further increased to 80% over the next 5 min, and then held at 80% for 5 min. The LC was coupled online to a hybrid timsTOF Pro2 (Bruker Daltonics, Germany) via a CaptiveSpray nanoelectrospray ion source (CSI). To establish the applicable acquisition windows for diaPASEF mode, the timsTOF Pro2 was operated in Data-Dependent Parallel Accumulation-Serial Fragmentation (PASEF) mode with 4 PASEF MS/MS frames in 1 complete frame. The capillary voltage was set to 1500 V, and the MS and MS/MS spectra were acquired from 100 to 1700 m/z. For the ion mobility range, 0.85–1.3 Vs/cm2 was used. The target value of 10,000 was applied to a repeated schedule, and the intensity threshold was set at 1500. The quadrupole isolation width was set to 2Th for m/z < 700 and 3Th for m/z > 800.

Database search and quantification: Raw MS data were analysed via DIA-NN using a library-free method48. A database (PRJNA587366) was used to create a spectral library. The MBR option was employed to create a spectral library from DIA data, which was then reannotated via this library. The FDRs of the search results were adjusted to <1% at both the protein and precursor ion levels, and the remaining identifications were used for further quantification analysis. Proteins with a *p*-value ≤ 0.05 and an absolute Log2FC ≥ 1.5 were defined as differentially expressed.

Gene set enrichment analysis and two-way orthogonal partial least squares

To identify the associations between the signature genes and signalling pathways, we performed gene set enrichment analysis (GSEA) on different subgroups with an adjusted p < 0.05 to determine whether an a priori-defined set of genes was significantly differentially expressed between two biological states49. The associations between genes or proteins and terpenoids or volatile metabolites were investigated via the two-way orthogonal partial least squares (O2PLS) model50. O2PLS models were generated via R (OmicsPLS v2.0.2). Parameters: a = 1:5, ax = 1:10, ay = 1:10, nr_folds = 5. Adjusted cross-validation procedure for O2PLS: crossval_o2m_adjR2. All differential terpenoids or volatile metabolites (X dataset) and differential genes or proteins (Y dataset) were used to construct O2PLS models to assess the overall correlation and independence between the two datasets based on R-squared values. The variables in each dataset and their loading values in the joint dataset were subsequently selected to create loading plots, which identify important variables influencing the other omics datasets.

Data availability

RNA seq data underlying the findings described in the manuscript are fully available without restriction from the NCBI BioProject: PRJNA1095236. All supporting data have been included in the article’s Supplementary Information.

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (32071673, and 32471816), the Science and Technology Innovation Program of Hunan Province (2023RC3157 and 2021RC4063), the Natural Science Foundation of Hunan Province (2024JJ5629 and 2025JJ70353), the Scientific Research Fund of Hunan Provincial Education Department, China (No. 22A0188 and 23A0229), the Natural Science Foundation of Changsha (Science and Technology Plan Project of Changsha, kq2402252).

Author information

Authors and Affiliations

Hunan Provincial Key Laboratory of Forestry Biotechnology and International Cooperation Base of Science and Technology Innovation on Forest Resource Biotechnology, Central South University of Forestry and Technology, Changsha, China

Lu Guo, Yuan-Gen Liu, Ying-Wen Fu, Yu-Yan Wang, Hao-Jin Wang, Shu-Mei Zhu, Qi-Zhi He, Dong-Xue Zhang, Shan-Shan Zhu, Si-Xian Wang, Tian Tong, Xu-Jie Dong, Xiao-Ling Wang, Yong-Nan Liu & Gao-Qiang Liu

Hunan University of Humanities, Science and Technology, Loudi, China

Lu Guo

Laboratory of Yuelushan Seed Industry, Changsha, China

Lu Guo, Yuan-Gen Liu, Ying-Wen Fu, Yu-Yan Wang, Hao-Jin Wang, Shu-Mei Zhu, Qi-Zhi He, Dong-Xue Zhang, Shan-Shan Zhu, Si-Xian Wang, Tian Tong, Xu-Jie Dong, Xiao-Ling Wang, Yong-Nan Liu & Gao-Qiang Liu

School of Basic Medical Science, Changsha Medical University, Changsha, China

Qi-Zhi He

Authors

Lu Guo

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