Skip to main content
Download PDF

A conserved terpene cyclase gene in Sanghuangporus for abscisic acid-related sesquiterpenoid biosynthesis

BMC Genomics volume 26, Article number: 378 (2025) Cite this article

Abstract

Background

The medicinal mushroom Sanghuangporus is renowned in East Asia for its potent therapeutic properties, attributed in part to its bioactive sesquiterpenoids. However, despite their recognized medicinal potential, the biosynthetic pathways and specific enzymes responsible for sesquiterpenoid production in Sanghuangporus remain unexplored, limiting opportunities to optimize their medicinal applications.

Results

Sesquiterpenoids from four Sanghuangporus species were extracted through targeted isolation using mass spectrometry (MS)-based metabolomics, resulting in the discovery of six known abscisic acid-related compounds and one new compound, whose structure was determined through spectroscopic and computational analysis. We employed a natural product genome mining approach to identify a putative biosynthetic gene cluster (BGC) containing a sesquiterpene synthase gene, ancA, associated with the detected compounds. Biosynthetic pathways for these compounds were proposed based on an integrative approach combining BGC analysis and MS2 fragment-based dereplication. Further analyses revealed that the gene content and synteny of the ancA BGC are relatively well-conserved across Sanghuangporus species but less so outside the genus.

Conclusions

A sesquiterpene synthase gene, its associated BGC, and the biosynthetic pathway for a group of detected abscisic acid-related sesquiterpenoids in Sanghuangporus were predicted through genomic and metabolic data analyses. This study addresses a critical gap in understanding the genetic basis of sesquiterpenoid production in Sanghuangporus and offers insights for future research on engineering metabolic pathways to enhance sesquiterpenoid production for medicinal use.

Peer Review reports

Background

Sanghuangporus (Hymenochaetales, Basidiomycota) is a genus of pharmaceutical mushrooms, esteemed in oriental medicine for its various properties. Before the systematic revision [1], Sanghuangporus species were referred to by various names. The type species S. sanghuang, commonly known as ‘sanghwang’ in Korea, ‘sanghuang’ in China, and ‘meshimakobu’ in Japan, has been called Inonotus sanghuang, Phellinus igniarius, and Phellinus linteus [2,3,4,5]. As such, chemicals derived from Sanghuangporus mushrooms were termed, for example, inoscavin, phelligrins, and phellilins, after their species names [6]. Currently, there are 20 accepted Sanghuangporus species, and of all, S. baumii, S. sanghuang, and S. vaninii are most commonly studied for their remedial effects. These species have reported antitumor, antioxidant, and anti-inflammatory properties [7,8,9], derived from secondary metabolites such as polysaccharides, polyphenols, and terpenoids. Among these diverse classes of compounds, sesquiterpenoids are frequently reported for Sanghuangporus, such as phelligridins, phellinene acids, and phellinulins [10,11,12].

Sesquiterpenoids are among the most commonly documented classes of terpenoids in basidiomycetes [13]. They participate in chemical defense against microbes and predators, such as insects [14,15,16]. In Schizophyllum commune, sesquiterpenoids are differentially expressed during the life cycle, suggesting their role in mating [17]. The biosynthesis of sesquiterpenoids, along with all other terpene derivatives, is generally initiated from farnesyl pyrophosphate (FPP; also known as farnesyl diphosphate, FDP). Transcripts and enzymes participating in the mevalonate (MVA) pathway that generates FPP in fungi have been well-defined and applied for Sanghuangporus [18, 19]. However, diverging downstream biochemical processes remain largely obscure despite the diversity of sesquiterpenoids produced. Only one sesquiterpene synthase (STS) gene, SbTps1, has been characterized in S. baumii [20], and its cyclization mechanism remains undetermined. Nonetheless, Sanghuangporus species produce various sesquiterpenoids, including cyclofarnesane derivatives resembling abscisic acid (ABA) [21, 22].

ABA and related compounds are produced by plants, fungi, and, to a limited extent, by bacteria. Nearly all higher plants synthesize ABA from FPP via the carotenoid pathway, which utilizes β-carotene produced through the methylerythritol phosphate pathway [23]. β-carotene is converted into zeaxanthin, which then undergoes a series of enzymatic reactions to form abscisic aldehyde and ultimately ABA. In bacteria, Azospirillum brasilense has been reported to produce ABA from malic acid [24], although the underlying biosynthetic mechanism remains unexplored. In fungi, ABA synthesis occurs via a direct pathway involving FPP cyclization to produce ionylideneethanol, followed by subsequent oxidation steps. Two variations of this pathway exist, depending on whether the species produce α-ionylideneethanol or γ-ionylideneethanol. The α-ionylideneethanol pathway is well-studied in Botrytis cinerea, where the bcABA1 to bcABA4 gene cluster governs ABA biosynthesis [25]. In contrast, the γ-ionylideneethanol pathway has primarily been investigated in Cercospora spp. [26, 27], though no specific genes or terpene cyclase enzymes have been identified for this route.

Natural product genome mining has been used to explore the conservation and evolution of biosynthetic gene clusters (BGCs) in microorganisms [28]. This approach has revealed new classes of secondary metabolites, such as enediyne and isocyanide compounds, across various fungal lineages [29, 30]. In this study, natural product genome mining was employed to identify a potential STS gene and associated BGC in Sanghuangporus. Genomic and sesquiterpenoid metabolomic data from mycelial cultures of four species (S. baumii, S. sanghuang, S. vaninii, and S. weigelae) were analyzed. These species were selected for their availability, medicinal properties, and broad phylogenetic representation within the genus [31, 32]. Based on genomic and metabolomic data, we i) predicted the biosynthetic pathway for each sesquiterpenoid and the corresponding BGC, and ii) compared the BGC synteny among Sanghuangporus species and evaluated the conservation of the STS in basidiomycetes.

Methods

Study materials

Four Sanghuangporus species were collected between 2014 and 2022 in the Republic of Korea (Supplementary Material 3: Table S1). Cultural isolates were obtained from the fresh fruiting bodies using potato dextrose agar (PDA; Difco, Sparks, MD, USA) at 26℃. The identities of the fruiting bodies and the cultures were validated based on phylogenetic analysis (Supplementary Material 1: Supplementary Method 1; Supplementary Material 2: Fig. S1). Representative strains of four species were used in this study: S. baumii (KMRB15090420), S. sanghuang (KMRB14110725), S. vaninii (KMRB16060213), and S. weigelae (KMRB22010601). The specimen vouchers and culture strains are deposited as dried specimens and stocks (20% glycerol and distilled water), respectively, at the Seoul National University Fungus Collection (SFC).

Chemical profiling using HR-ESI–MS

An analysis of extracts from four Sanghuangporus species was conducted using high-resolution electrospray ionization mass spectrometry (HR-ESI–MS). The HR-ESI–MS and UPLC-MS/MS experiments were carried out on an Orbitrap Exploris 120 mass spectrometer, which was connected to a Vanquish UHPLC system (ThermoFisher Scientific, Waltham, MA, USA). Chromatographic separation was achieved using a YMC Triart C18 column (100 × 2.1 mm, 1.9 μm) with a flow rate of 0.3 mL/min and maintained at 30 °C. The mobile phases consisted of water with 0.1% formic acid (A) and acetonitrile with 0.1% formic acid (B), and the elution was performed with a linear gradient ranging from 10 to 100% B over 10 min. Mass detection covered an m/z range of 200–2000, with the Orbitrap analyzer set to a resolution of 60,000 for full MS scans and 15,000 for data-dependent MSn scans. During mass spectrometry, the parameters included a spray voltage of 3.5/2.5 kV for positive ion modes, an ion transfer tube temperature of 320 °C, a vaporizer temperature of 275 °C for the HESI probe, and an RF lens setting of 70%. Ultrapure nitrogen (> 99.999%) served as the sheath and auxiliary gas for the HESI probe, set at 50 and 15 arbitrary units, respectively. The collision of ions in the Orbitrap detector was performed using a normalized higher-energy collision dissociation energy of 30%. MS/MS fragmentation data were obtained in data-dependent MSn mode, targeting the four most intense ions and employing a dynamic exclusion filter to prevent repeated ion fragmentation within 2.5 s of acquiring the MS2 spectrum.

Feature-based molecular networking and Sirius analysis

The four raw data files were imported to MZmine 4.1.0 [33]. MS level (1) mass detection settings were performed by centroid setting the noise level at 8.0E5. Mass detection level (2) was set by centroid setting noise level of 1.0E1. Chromatogram builder settings included a minimum group size in the number of scans to be 4, minimum intensity for consecutive scans of 8.0E5, a minimum absolute height of 9.0E5, and scan-to-scan accuracy (m/z) at 1 ppm, 0.05 (m/z). Resolving was achieved using a retention time tolerance of 0.04 min, the minimum absolute height at 9.0E5, chromatographic threshold at 0.8. Minimum ratio of peak top/edge at 1.7, and Min number of data points of 4. 13 C isotope filtering parameters included m/z tolerance of 5.0 ppm, retention time tolerance of 0.03 min, and the most intense representative isotope to be considered as representative isotope. Isotopic peaks finder set parameters as chemical elements H, C, N, O, Cl, Br, P, and the m/z tolerance was 10.0 ppm, maximum charge of isotope m/z of 1. Aligning of feature list step set parameters as m/z tolerance at 5.0 ppm, retention time tolerance at 0.1 min, weight for RT at 2.0, and mobility weight at 1.0. Feature list filtering settings included minimum features in a row 1.0, minimum features in an isotope pattern 2, and m/z tolerance at 5.0 ppm. The parameters set in gap filling step included intensity tolerance at 0.2, m/z tolerance at 5.0 ppm, retention time tolerance at 0.1 min, and minimum data points at 2. Correlation grouping was performed by minimum feature height of 3.0E3, intensity threshold for correlation of 3.0E3, with feature correlation grouping. Ion identity networking setting included m/z tolerance of 10.0 ppm, min height of 1.0E3. The parameters set in MS/MS spectral networking included m/z tolerance at 5 ppm, minimum matched signals at 6, minimum cosine similarity 0.7. [34]. The feature list was exported to Sirius 6.0.0, where MS2 fragmentation analysis was performed to identify the molecular formula of the [M + H]+ adduct. Additionally, fingerprint prediction and structure database search were employed to enhance the accuracy of compound identification [35].

Extraction, isolation, and structure determination of secondary metabolites

Sanghuangporus cultures were cultivated in PDA media (BD Difco, Sparks, MD, USA) and YEME solid media at room temperature. After four weeks, the strain cultures were extracted with ethyl acetate three times for chemical profiling. Among them, S. weigelae was selected for large-scale cultivation as the largest number of compounds were detected for this species. The culture was cultivated in YEME solid media at room temperature for the isolation of compounds. After 36 days, the cultures were extracted with ethyl acetate three times. Sanghuangporus weigelae extraction (2.5 g) was fractionated by medium pressure column chromatography (MPLC) over silica by a stepwise gradient of n-hexane-dichloromethane-MeOH (from 100:0:0, 50:50:0, 0:100:0, 0:99:1, 0:98:2, 0:97:3, 0:95:5, 0:90:10, 0:50:50 to 0:0:100) to obtain thirteen fractions (A ~ M fractions). Additional MPLC of isocratic method of CH2Cl2:MeOH (30:70) afforded compound 7 (803 mg). D fraction was subjected to reverse phase HPLC (Phenomenex, Luna Phenyl-Hexyl, 5 µ, 250 × 10.0 mm, isocratic aqueous 45% CH3CN, 2.0 mL/min, UV 210, 254, 280, and 365 nm) to obtain compound 6 (26.0 mg). E fraction obtained from the elution solvents 0:99:1 was subjected to reverse phase HPLC (Phenomenex, Luna C18, 5 µ, 250 × 10.0 mm, isocratic aqueous 45% CH3CN, 2.0 mL/min, UV 210, 254, 280, and 365 nm) to obtain compound 1 (1.6 mg), 2 (1.6 mg), 4 (0.7 mg). F fraction was subjected to reverse phase HPLC (Phenomenex, Luna C18, 5 µ, 250 × 10.0 mm, isocratic aqueous 50% CH3CN, 2.0 mL/min, UV 210, 254, 280, and 365 nm) to obtain compound 5 (23.1 mg). F fraction was subjected to reverse phase MPLC by a stepwise gradient of DW-MeOH (from 10:90 to 0:100) to obtain compound 3 (7.0 mg).

SHA- 1 (compound 1): white solid; [α]d20 = − 77.1 (c = 0.1, MeOH); UV (MeOH) λmax (log ε) = 260 (4.10) nm; CD (MeOH) λmax (Δε) 253 (− 35.49), 315 (7.74) nm;; for 1H NMR (400 MHz, CDCl3) δH 6.08 (1H, d, J = 15.5 Hz, H- 8), 5.72 (1H, br s, H- 10), 5.67 (dd, J = 15.5 Hz, 10.5, H- 7), 2.92 (1H, dq, J = 6.9, 6.6 Hz, H- 5), 2.52 (1H, m, H2− 3a), 2.32 (1H, m, H- 6), 2.29 (1H, m, H2− 3b), 2.21 (3H, d, J = 1.1 Hz, H3− 12), 1.82 (1H, td, J = 14.0, 5.2 Hz, H2− 2a), 1.64 (1H, ddt, J = 14.0, 6.8, 2.2 Hz, H2− 2b), 1.34 (3H, s, H3− 15), 0.87 (3H, s, H3− 14), 0.83 (3H, d, J = 6.8 Hz, H3− 13); 13C NMR (100 MHz, CDCl3) δC 213.10 (C- 4), 171.65 (C- 11), 154.05 (C- 9), 137.58 (C- 8), 134.78 (C- 7), 118.05 (C- 10), 59.56 (C- 6), 43.66 (C- 5), 38.25 (C- 3), 36.02 (C- 2), 34.29 (C- 1), 29.75 (C- 14), 26.88 (C- 15), 14.28 (C- 12), 13.12 (C- 13); COSYs (CDCl3, H-# ↔ H-#) H- 2 ↔ H- 3, H- 5 ↔ H3− 13, H- 5 ↔ H- 6, H- 6 ↔ H- 7, H- 7 ↔ H- 8; HMBCs (CDCl3, H-# → C-#) Ha− 2 → C- 1 and C- 15; Hb− 3 → C- 2 and C- 4; H- 5 → C- 4, C- 6, C- 7 and C- 13; H- 6 → C- 1, C- 5 and C- 7; H- 7 → C- 5, C- 6 and C- 9; H- 8 → C- 6, C- 9, C- 10 and C- 14; H- 10 → C- 8 and C- 14; H3− 12 → C- 8, C- 9, C- 10 and C- 11; H3− 13 → C- 4, C- 5 and C- 6; H3− 14 → C- 1, C- 2, C- 6 and C- 15; H3− 15 → C- 1, C- 2, C- 6 and C- 15; (+)-HR-ESI–MS m/z 251.1648 [M + H]+, calc for C15H23O3, 251.1642.

High molecular weight genomic DNA extraction

Sanghuangporus cultures were grown in 150 ml static or shaking (150 min−1) potato dextrose broth (PDB; Difco, Sparks, MD, USA) at 26℃ for 5–10 days for the genomic DNA extraction. The grown mycelia were dehydrated using a GAST vacuum pump (Michigan, USA), and high-molecular-weight DNA was extracted using the HMW DNA extraction kit (Wizard, Promega, WI, USA) or through a modified cetyltrimethylammonium bromide (CTAB) protocol (Supplementary Material 1: Supplementary Method 2). The extracted genomic DNA was quantified using Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and Quantus Fluorometer (Promega, Madison, WI, USA).

Library preparation and DNA sequencing

Library preparation and sequencing were conducted at the National Instrumentation Center for Environmental Management (NICEM), Seoul National University (Seoul, Rep. of Korea) for Oxford Nanopore Technologies (ONT), CJ Bioscience (Seoul, Rep. of Korea) for PacBio sequencing, and Macrogen (Seoul, Rep. of Korea) for Illumina sequencing.

ONT libraries were constructed using the ONT ligation sequencing kit and Circulomics Short Read Eliminator (SRE) Kit. ONT sequencing was performed on PromethION P24 using a R9.4.1 cell and MinKnoW 21.11.7 software for S. baumii and S. sanghuang, and R10.4.1 cell for S. vaninii. Genomic data for S. weigelae was achieved through library preparation (5 kb) and PacBio sequencing (Sequel II system). Different sequencing platforms were used based on resource availability. Illumina NovaSeq 6000 platform was used for polishing the four long-read genomic data. Paired-end sequencing was performed using 2 × 150 bp libraries.

Genome assembly and annotation

Raw nanopore reads from R9.4.1 cell were basecalled by Guppy 6.1.3 (https://community.nanoporetech.com), and reads from R10.4.1 cell were basecalled by Dorado 0.4.1. (https://github.com/nanoporetech/dorado), both with super accurate model. Hifi reads were generated by PacBio CCS (https://github.com/PacificBiosciences/ccs) for PacBio sequencing. Adapters were trimmed by porechop 0.2.4 (https://github.com/rrwick/Porechop) for nanopore reads, HiFiAdapterFilt 2.0.1 [36] for PacBio reads, and fastp 0.23.2 [37] for Illumina reads (Supplementary Material 3: Table S2).

Nuclear genome assembly was performed with NextDenovo 2.5.0 [38] for nanopore sequence data, PECAT 0.0.3 [39] when high heterogeneity was found, and hifiasm 0.19.5 [40] for PacBio data. Polishing was performed with four rounds of Racon 1.5.0 [41] for long reads mapped by minimap2 2.24 [42], one round of medaka 1.6.0 (https://github.com/nanoporetech/medaka) (only for nanopore sequencing), and four rounds of Hapo-G 1.3.1 [43] for Illumina reads. Jellyfish k-mer 2.3.0 [44] and GenomeScope 2.0 [45] were performed to construct a k-mer histogram and evaluate diploidy of the genomes. When diploids were observed in the k-mer plot, haplotypes were merged with Purge Haplotigs 1.1.2 [46]. The quality of the genomes was assessed with Tapestry 1.0.0 [47] using telomere “CTGGTG”, and UFCG 1.0.5 [48]. Low-depth contigs or contigs with low GC contents were regarded as non-nuclear genomic data and filtered.

RepeatModeler 2.0.4 [49] and RepeatMasker 4.1.2 [50] were performed to mask repeats of the genomes before annotation. Structural annotation was conducted with BRAKER3 3.0.3 [51] with --fungus option. For non-coding RNA sequences, tRNAs were found using tRNAscan-SE 2.0.9 [52], and rRNAs were annotated using Infernal 1.1.4 [53] with Rfam 14.8 [54] database. For functional annotations, we employed EggNOG-mapper 2.1.8 [55] using EggNOG 5.0 database [56] and MMseqs2 13.45111 [57] option, searching for e-value of 1E-5 or lower. Then we used Funannotate 1.8.15 (https://github.com/nextgenusfs/funannotate) through “iprscan” (InterProScan5 [58] wrapper) and UPIMAPI 1.12.2 [59]. All results were then merged with an “annotate” function in Funannotate. The final genome sequences have been deposited in NCBI under the accessions JBKPUN000000000 (S. baumii), JBKPUO000000000 (S. sanghuang), JBKPUM000000000 (S. vaninii), and JBKPUL000000000 (S. weigelae).

Biosynthetic gene cluster and pathway prediction

A BLASTp 2.15.0 [60] search was conducted to find orthologs of the core STS gene, ancA, from Antrodia cinnamomea [61] in the S. weigelae genome. Additionally, BGCs producing secondary metabolites were predicted in the S. weigelae genome using the full-featured antiSMASH 7.0.0 [62] with --taxon fungi option. An additional database-independent BGC search was performed with DeepBGC 0.1.31 [63] to find putative BGCs. Of all the BGCs identified, two included the core STS gene. One BGC was selected based on its DeepBGC score, predicted biological activity, and the types of secondary metabolites it was predicted to produce. The genes in the selected BGC (hereafter referred to as the ancA BGC) were transcribed in silico into protein sequences and folded using the Alphafold 3 server [64]. For the AncA protein, an additional structure incorporating an Mg2+ ion was also predicted. A structural similarity search was performed for all proteins using Foldseek 7.04e0ec8 [65], against Alphafold Swiss Prot, PDB [66], and UniProt 50 [67] databases.

Based on our metabolomic profiles of Sanghuangporus, a putative biosynthetic pathway was constructed. Some steps were verified through reference searches, while the others were predicted based on possible biochemical reactions. From the ancA BGC, each predicted protein was tested in silico for its binding affinity against all metabolites in the putative pathway using Vina-GPU 2.1 [68]. All SMILES formulas of metabolites in the pathway were extracted using ChemSketch (https://www.acdlabs.com/resources/free-chemistry-software-apps/chemsketch-freeware/) and converted into 3-dimensional structures using RDKit (https://www.rdkit.org/) and Open Babel [69]. Molecular docking was performed using 64 spatial sections to cover the entire protein. The section resulting in the lowest binding energy was identified. The terpene cyclase domain of the AncA protein (the primary STS) and FPP was visualized using PyMOL software (version 3.1.1, Schrödinger, LLC, Portland, U.S.A.) 3.1.1 [70]. Similarly, the phosphatase domain of Mg2+-postulated AncA and the diphosphate-attached compound 9 (intermediate) were visualized using the same method. A heatmap depicting the lowest binding energy for all tested proteins was generated using the python seaborn library.

Conservation analysis of AncA and the BGC

The top 100 hits of orthologous sequences of the AncA amino acid sequence were collected from the NCBI BLASTp results using Sanghuangporus AncA sequences as the query. After a preliminary round of phylogenetic analysis (Supplementary Material 2: Fig. S2), only the orthologous protein sequences were selected for the final analysis. For the reference NCBI Sanghuangporus genomic data, AncA sequences that were not available in the BLASTp database were manually included for the final analysis. All reference and query sequences were aligned using MAFFT version 7 [71] in Geneious Prime 2024.0.5 (https://www.geneious.com) with the BLOSUM62 substitution matrix and a gap open penalty of 3. After a model test of the alignment using ModelTest-NG [72], the JTT-DCMUT + I + G4 model was selected for the phylogenetic analysis. A maximum likelihood tree was inferred using RAxML v. 8.2.12 [73] with 1,000 bootstrap replicates.

Species phylogenomic trees were inferred to analyze the phylogenetic relationship among studied strains. Fungal core genes indicated in the UFCG database were extracted from genomes and aligned using the UFCG pipeline [48]. Core gene alignments were concatenated with SuperCRUNCH 1.3.2 [74], and the phylogenomic tree was built using IQTREE2 [75] with 1,000 ultrafast bootstraps [76] and the ModelFinder [77] option. For the synteny analysis of the ancA BGC across Hymenochaetaceae, reference genomic data assembled into fewer scaffolds were selectively collected from the NCBI database. From these reference data, the ancA BGC was located using cblaster 1.3.19 [78]. Clinker [79] was used to visualize the synteny of each strain.

Results

Isolation and structural elucidation

Feature-based molecular networking (Fig. 1A), combined with Sirius MS2 fragmentation base compound class annotation (Fig. 1B), effectively identified the target cluster of sesquiterpenoids. Targeted isolation of Sanghuangporus spp. extracts afforded seven compounds, and their structures were determined by comparing their spectroscopic data (Fig. 2) with those in the references [80,81,82].

Fig. 1
figure 1

A Full molecular network highlighting the target ABA-related compound cluster. B MS2 fragmentation of the nodes within the target cluster was analyzed, with black arrows indicating matched fragments. This analysis was based on molecular formula annotation (M + H).+ and substructure annotation using Sirius 6.0.0 (11–13)

Full size image
Fig. 2
figure 2

Sesquiterpenoids isolated from Sanghuangporus species

Full size image

The new sesquiterpenoid (1), named as SHA- 1, had a molecular formula of C15H22O3, based on the (+)HRESIMS, with 5 degrees of unsaturation. Its 1H NMR spectrum (Table 1) revealed three aliphatic protons [δH 6.08 (d, J = 15.5 Hz), 5.72 (br s), 5.67 (dd, J = 15.5, 10.5 Hz)]. The 13C NMR spectrum indicated the presence of one carboxylic acid (δC 171.6), one ketone (δC 213.0), and two double bonds (δC 154.0, 137.6, 134.8, and 118.0). Therefore, the presence of one ring in the structure was evident to account for the unsaturation degree. Interpretation of 1H-1H COSY spectra afforded two substructures, one from H2− 2 (δH 1.82 and 1.64) to H2− 3 (δH 2.52 and 2.29) and the other from H3− 13 (δH 0.83) to H- 8 (δH 6.08) through H- 5 (δH 2.92), H- 6 (δH 2.32), and H- 7 (δH 5.67), consecutively. The connection of two substructures and positions of the functional groups were confirmed by analysis of HMBC spectrum. The strong HMBC correlations of H3− 12 (δH 2.20) with C- 8 (δC 137.6), C- 9 (δC 154.0), C- 10 (δC 118.0), and C- 11 (δC 171.6) indicated the presence of a methyl-diene chain which is further connected with the carboxylic acid. Further correlations of two tertiary methyls (δH 1.34 and 0.87) with C- 2 (δC 34.3) and C- 6 (δC 59.6), along with those of H3− 13 (δH 0.83) with the ketone carbon and C- 6, confirmed that it had a 4-dimethyl- 1-methyl cyclohexanone with the 3-methyl-pentaenoyl attachment. The geometry of the two double bonds in the aliphatic chain was determined to be both trans, based on their coupling constants (J = 15.5 Hz) and NOESY correlations: H- 7 with H3− 12 and H- 8 and H- 10. The absolute stereochemistry of C- 5 and C- 6 was determined by a quantum-mechanic based molecular calculation method such as ECD calculation, DP4 calculations after establishment of relative one by NOESY analysis.

Table 1 1H and 13C NMR data of compound 1
Full size table

In the NOESY interpretation, it was noted that the axial or pseudo-axial positions of H3− 15 (δH 1.34) and H3− 14 (δH 0.87) were assigned based on the comparison of 1H NMR data with that of a structurally similar reference compound [83]. The analysis of NOESY correlations established the spatial proximity between H- 5 and H3− 15, as well as H- 6 and H3− 14, while no correlation between H- 6 and H3− 15 was observed. This indicates that the methyl group at C- 5 and the 3-methyl-pentaenoyl group at C- 6 are on the opposite face of the molecule. However, the J-value of 6.9 Hz between H- 5 and H- 6 is a bit small for this. To resolve the ambiguity, DP4 probability analysis was performed on four possible stereoisomers (5R*,6R*; 5R*,6S*; 5S*,6R*; 5S*,6S*). The analysis revealed that the isomer with the 5S*,6S* configuration had a 100.0% probability, confirming the relative configuration established by NOESY. To further establish the absolute configuration, its experimental ECD spectrum was compared with those of calculated ECD for the two possible isomers (5R,6R and 5S,6S), confirming the final configuration. As a result, negative Cotton effects at 253 nm together with positive Cotton effects 315 nm in the experimental ECD spectrum agreed with that of the isomer with 5S,6S (Fig. 3A). Thus, the structure of the new compound (1) including stereochemistry was completely established (Fig. 3B; Supplementary Material 1: Supplementary Methods 3–4).

Fig. 3
figure 3

A Calculated ECD data for compound 1 (SHA- 1). B Key 1H-1H COSY (bold lines) and HMBC (red arrows) correlations. C Key NOESY correlations (indicated by blue arrows)

Full size image

Genome assembly

High-quality genomic data were obtained for four Sanghuangporus species. The genome size ranged from 31.9 to 36.2 Mb, and the GC content ranged from 47.80% to 48.11%. The genomes showed BUSCO completeness ranging from 91.0% to 93.7%, and all 55 nuclear UFCG genes were identified. Indices indicating genome contiguity were as follows: N50, 2.52–3.02 Mb; 17–25 contigs; and telomeres present in 23.5% to 73.5% of the genome. Diploidy was also well solved, and read depth, calculated from tapestry, corresponded to the contigs. Across the genomes, 9,664 to 10,126 protein-coding genes were annotated.

Predicted BGCs of Sanghuangporus

The genomic data of S. weigelae were selected as a representative for all subsequent analyses. In the core STS analysis, the sequence of ACEPAI_7410 and ACEPAI_4311 exhibited 54.1% and 43.2% protein identity, respectively, to the AncA of Antrodia cinnamomea (= Taiwanofungus camphoratus) V7 (GCA_022598595), which was the first species to report AncA as an STS. Other hits showed very short alignments (less than 100 bases) and were, therefore, considered false-positive matches.

An antiSMASH 7.0.0 search of the four Sanghuangporus genomes resulted in 19 BGCs for S. sanghuang, 19 for S. baumii, 20 for S. weigelae, and 25 for S. vaninii. Gene ACEPAI_4311 was included in one of the BGCs predicted by antiSMASH 7.0.0, but gene ACEPAI_7410 was not found. On the other hand, a DeepBGC search of the S. weigelae genomic data predicted 373 BGCs. Among them, two gene clusters included ACEPAI_4311 and ACEPAI_7410, respectively (Table 2). The putative BGC containing ACEPAI_7410 had a DeepBGC score of 0.94022 with 0.44 terpene prediction, while the putative BGC containing ACEPAI_4311 had a DeepBGC score of 0.58855 with a 0.7 polyketide prediction. Thus, we concluded that ACEPAI_7410 and the putative BGC containing it are the actual putative BGC producing compounds found in the HPLC analysis. The putative BGC was 112 kbp in length, and contained 172 genes, included a STS gene, ancA. The predicted product type for the ancA BGC was highest for terpene (0.44), compared to alkaloid (0.2), NRP (0.02), polypeptide (0.1), RiPP (0.07), saccharide (0.07), and others (0.12). The predicted product activity of the ancA BGC was highest for antibacterial (0.61), compared to cytotoxic (0.1), inhibitor (0.34), and antifungal (0.07). The initial 172 genes were reduced to 41 genes after wrapping the annotation result from BRAKER3.

Table 2 DeepBGC results for predicted AncA-based sesquiterpenoid synthesis in Sanghuangporus
Full size table

Functions of genes in ancA BGC

Among the 41 genes included in the putative ancA BGC, some were further predicted to participate in the synthesis of ABA-related sesquiterpenoids detected from the metabolomic profiles of Sanghuangporus (Fig. 4). The functions of the genes in the ancA BGC were assessed through sequence similarity searches (Fig. 5A). The similarity search annotated meaningful functions for 31 of the 41 genes, while the remaining 10 genes (ACEPAI_7412, ACEPAI_7416, ACEPAI_7418, ACEPAI_7424, ACEPAI_7428, ACEPAI_7430, ACEPAI_7433, ACEPAI_7435, ACEPAI_7439, and ACEPAI_7442) were predicted to encode hypothetical proteins. Further analysis using Foldseek predicted the functions of all 10 proteins for the corresponding genes (Supplementary Material 3: Tables S3–S5). Additionally, another possible STS was identified using Foldseek; the protein of ACEPAI_7418 showed 19.3% structure identity and 3.223E-9 e-value with terpene synthase DEP1 (Supplementary Material 3: Table S3).

Fig. 4
figure 4

Putative biosynthetic pathway of ABA-related sesquiterpenoids in Sanghuangporus. Metabolomic data is from S. weigelae (KMRB22010601). Intermediate compounds 9, 14, and 15 were not detected

Full size image
Fig. 5
figure 5

Protein–ligand binding of enzymes derived from ancA BGC and detected metabolites in Sanghuangporus. A Heatmap for the best (minimum) binding energy values (ΔG, kcal/mol) for protein-metabolite pairs. Protein functions were predicted based on the Swiss Prot database. B Predicted structure of AncA. The terpene cyclase and pyrophosphatase domains are indicated as ‘TC’ and ‘PP’, respectively. Chelating metal ion Mg2+ is shown in pink for the pyrophosphatase domain. C Predicted FPP binding at the terpene cyclase domain of AncA. D Predicted FPP binding at the pyrophosphatase domain of AncA. Chelating metal ion Mg2+ is shown in pink

Full size image

Sesquiterpenoid biosynthesis pathway prediction with metabolomic analyses

The biosynthesis pathway of the isolated compounds was proposed based on the genomic data and the chemistry of the isolated compounds (Fig. 4). Although not all compounds presented in the proposed pathway were isolated in this investigation, metabolites 10–13 were identified to be present in the extracts and assigned using the feature-based molecular networking data with a minimum cosine similarity of 0.7 and Sirius 6.0.0 compound annotation based on MS2 fragment analysis (Fig. 1).

Compound 8 (FPP), generated by the MVA pathway, is converted by the enzyme AncA into compound 9. This intermediate is then dehydrogenated and subsequently oxidized to yield compound 7, which undergoes further modifications catalyzed by oxidase (P450) and epoxidase, leading to the formation of compounds 3, 4, and 6. Compound 6 (phellidene E) is further reduced by a reductase to produce compound 5 (phellinulin K). Oxidation of the cyclohexane ring in compound 3 generates the β-methyl-α, β-unsaturated carbonyl group in compound 2 (1'-deoxyabscisic acid). Subsequent cis–trans isomerization leads to the generation of compound 13 (cis-ABA). Additionally, compound 11, identified through metabolic analysis, is predicted to be produced from compound 8 (FPP) by an unknown terpene cyclase. Compounds 12 and 13 are proposed to be produced by an unknown enzyme and ABA4, respectively. Ultimately, compound 1 (SHA- 1) is anticipated to be produced via enol-keto modification and the action of an oxidase from compound 7 ((+)-γ-ionylideneacetic acid) through compounds 14 and 15.

Comparative synteny analyses

The gene content and synteny of the ancA BGC are relatively well-conserved across Sanghuangporus species (Fig. 6). Two S. vaninii genomes (GCA_024703735 and GCA_036873625) exhibited incomplete synteny, possibly due to fragmentation of the assembled genomes. Sanghuangporus baumii KMRB15090420 possesses the BGC across two contigs (2 and 8). Notable differences exist among species. For example, ACEPAI_7412 is exclusively found in S. weigelae; ACEPAI_7424 is missing in S. vaninii (GCA_009806525 is annotated as S. sanghuang but is phylogenetically identified as S. vaninii); and ACEPAI_7429 is repositioned in S. sanghuang. Overall, the conservation of synteny mirrors the species’ phylogenetic relationships.

Fig. 6
figure 6

Synteny analysis of ancA BGC across Sanghuangporus species. The species tree on the left has been inferred based on 59 single copy genes. Strains newly analyzed in this study are bolded. Strains of possibly misidentified species names are annotated with quotation marks (“”). Genes are enclosed by gray boxes based on contigs/scaffolds. Comparative examples of exclusively present/absent or repositioned genes are labelled on top and indicated by colored shades. Predicted functions of encoding proteins for each gene are available in Fig. 5A

Full size image

The BGC becomes more disorganized outside the genus as the species becomes phylogenetically more distant from Sanghuangporus (Supplementary Material 2: Fig. S3). For the most distant species, Pyrrhoderma noxium (GCA_002287475), more than half (24) of the genes were absent. Nevertheless, the core ancA gene (ACEPAI_7410) and nine other genes (ACEPAI_7414, ACEPAI_7419, ACEPAI_7420, ACEPAI_7421, ACEPAI_7434, ACEPAI_7435, ACEPAI_7438, ACEPAI_7439, and ACEPAI_7441) were conserved across Hymenochaetaceae.

Conservation of AncA in basidiomycetes

Of the 34 available Hymenochaetales genomes on NCBI (accessed: 2024–05–28), 11 haloacid dehydrogenase (HAD)-like proteins were detected through NCBI BLASTp hits of Sanghuangporus AncA amino acid sequences, including one from S. baumii strain 821 (GCA_001481415). Protein sequences from Hymenochaetaceae, which includes the entire Hymenochaetales except Schizopora paradoxa (reidentified as Xylodon ovisporus [84]), were monophyletically grouped with 100 bootstrap support (Fig. 7). Multiple copies of highly similar proteins from peat moss (Sphagnum spp.) were detected and included in the phylogenetic analysis, forming formed a clade outside the Hymenochaetales clade. Furthermore, groups of paralogous AncA proteins were also detected from diverse basidiomycetes lineages (Supplementary Material 2: Fig. S2).

Fig. 7
figure 7

Maximum likelihood phylogenetic tree of Sanghuangporus AncA orthologous protein sequences. Species names in quotation marks (“”) are names in GenBank. Sequence of Geoglossum umbratile (Ascomycota) has been selected as an outgroup. Node bootstrap support values ≥ 70 are indicated

Full size image

Protein-metabolite binding prediction

Binding affinity analysis was conducted using Vina-GPU 2.1 to predict the likelihood of protein-metabolite interactions. The best (minimum) binding energy value (ΔG, kcal/mol) ranged from − 4.4 and − 9.5 kcal/mol (Fig. 5A). The protein products of ACEPAI_7410 (AncA), ACEPAI_7430, ACEPAI_7436, ACEPAI_7437, ACEPAI_7440, and ACEPAI_7444 exhibited relatively high binding affinities across all analyzed secondary metabolites. Notable pairings included ACEPAI_7418 (a hypothetical protein) with compound 6, and ACEPAI_7444 (a peptidase) with compound 4. The protein structure of ACEPAI_7410, predicted using Alphafold 3, showed a high structural similarity to AncA (Fig. 5B), with a PyMOL alignment score of 1242.500 and an RMSD of 2.649. In the terpene cyclase domain of AncA, two aspartate residues were found to interact with FPP (Fig. 5C). In the phosphatase domain, Mg2+-postulated AncA demonstrated an interaction with diphosphate-attached compound 9, showing a binding energy of − 7.5 kcal/mol (Fig. 5D).

Discussion

Feature-based molecular networking efficiently identified a large cluster of nodes predicted to be ABA-related sesquiterpenoides. Through chromatographic methods focused on isolating this cluster, we successfully obtained six known compounds (2–7) and identified one novel compound, SHA- 1 (compound 1). SHA- 1 is distinguished by a ketone functional group and a methyl group. The methyl group appears as a doublet in its NMR spectrum, unlike the other isolated compounds, which feature either a singlet methyl group or an exomethylene group at the corresponding position. This metabolomic information was effectively used to propose an ABA-related biosynthetic pathway, combining genome sequencing data, reference, and analysis of molecular formulas with MS2 fragmentation similarity matching using Sirius 6.0.0.

The sesquiterpenoid biosynthesis pathway was predicted based on the genomic data and the chemistry of the isolated compounds. This prediction was supported by previous studies. The core STS enzyme AncA converts compound 8 (FPP) to compound 9, which has been previously isolated from Phellinus linteus [81]. The transition from compound 3 to compound 2 has been reported for Rosisphaerella cruenta (= Cercospora cruenta) [85]. Cis–trans isomerization between compounds 2 and 13 has been observed in R. rosicola (= C. rosicola) [86]. While the exact mechanism and STS behind the conversion of FPP to compound 11 (α-ionylideneethanol) is unrevealed, the enzymes responsible for the syntheses of compounds 12 and 13 are known in Botrytis cinerea, which are ABA2 and ABA4, respectively [25, 87]. In Botrytis cinerea, four biosynthetic genes, bcABA1 to bcABA4, exist in a BGC [88]. An orthologous ABA4 gene has been detected in Sanghuangporus [89]. The ABA4 gene encodes a protein (short-chain dehydrogenase/reductase) that synthesizes ABA from 1,4-trans-dihydroxyl-α-ionylideneacetic acid. However, no gene homologous to that of ABA2 was identified for our studied strains. Additional studies are required to identify the enzyme responsible for the conversion of compound 11 to compound 12.

No related genes were previously known for the downstream γ-ionylideneethanol (compound 10) pathway in the Kingdom Fungi, even for Cercospora, which has been exceptionally studied for the ABA synthesis pathway based on γ-ionylideneethanol [26, 90, 91]. The deep learning-based BGC search, deepBGC, allowed us to detect a putative BGC for the ABA-related compound syntheses using γ-ionylideneethanol based on in silico methods. In addition, protein-folding and foldseek allowed us to predict the protein functions through structural comparison using a well-established database from fungal species that may be evolutionarily distant from Sanghuangporus (e.g. ACEPAI_7418 in Supplementary Material 3: Table S3). Furthermore, molecular docking calculation allowed us to predict which genes in the ancA BGC are likely to be involved in the biosynthetic pathway of the detected ABA-related sesquiterpenoids, such as ACEPAI_7436 and ACEPAI_7437 (Fig. 5A). Other genes with low binding affinity are assumed to be involved in genetic expression or signaling, according to their functions. For example, ACEPAI_7411, encoding for a putative mediator complex, may be involved in regulating RNA polymerase II-dependent transcription. The ACEPAI_7421 gene encodes for an enzyme comparable to GRP1. This enzyme has a pleckstrin homology (PH) domain that is highly similar to yeast Sec7 protein, which catalyzes guanine nucleotide exchange of ADP ribosylation exchange factor, ARF [92, 93]. Given that ARF proteins regulate membrane trafficking, the GRP1-like protein may participate in activating signaling pathways.

The core protein of ancA BGC, the terpene cyclase AncA, is a HAD-like protein, a primary enzyme that converts FPP to (R)-trans-γ-monocyclofarnesol in A. cinnamomea (= T. camphoratus). It contains two domains, terpene cyclase and pyrophosphatase, which catalyze the cyclization of FPP and remove pyrophosphate, respectively [61]. The ancA gene is part of a BGC with genes ancB, ancC, ancD, and ancE that are responsible for the production of antrocin and its relatives [61]. These compounds are responsible for diverse anti-cancer effects [94,95,96]. Similarly, ancA and neighboring genes in the BGC are deemed to be involved in the synthesis of various pharmaceutical compounds in Sanghuangporus. Antibacterial, anti-inflammatory, and antioxidant activities are recognized for some compounds isolated in this study. Compounds 6, 7, and 9 from Phellinus linteus (= Sanghuangporus sp.) have been reported for their antibacterial activity against oral pathogen Porphyromonas gingivalis [81]: compounds 6 and 7 showed a weak antibacterial activity (Minimum Inhibitory concentration, MIC: 34.1 and 155 μg/mL) while compound 9 showed a potent bacterial growth inhibition (MIC: 5.9 μg/mL, MIC of positive controls, hinokitiol and triclosan, was 25.0 and 3.13, respectively). Compound 7 was also reported to not only have antibacterial activity against Micrococcus luteus but also possess antifungal activities against Mucor plumbeus [12]. Compounds 3, 5, and 7 from the mycelium of P. linteus displayed hepatoprotective capability to hepatic fibrosis, conferring protection against it [11, 82]. Additionally, compound 7 exhibited antioxidant activities, albeit weaker than the effects of polyphenols [97].

AncA is well-preserved across Basidiomycota (Fig. 7). However, it does not belong to any known clades of STSs (I to IV) [98] and, thus, appears to synthesize a widely variant group of sesquiterpenoids compared to other mushrooms. It is worth noting that AncA ortholog was also found in peat moss (Sphagnum spp.). Based on the phylogenetic relationship, the ancA gene is presumed to have transferred over to Sphagnum spp. by horizontal gene transfer (HGT) from a bryophilous mushroom that is found in Hymenochaetales or from a species that is phylogenetically close to Hymenochaetales. The full complement of genes in the ancA BGC is not universally conserved across species (Supplementary Material 2: Fig. S3). This reflects species-specific adaptations in the biosynthetic capacity for sesquiterpenoids and the types of sesquiterpenoids produced, which may be shaped by various ecological factors. It may also explain why Sanghuangporus produces a particularly high number of medicinal compounds [99]. Regardless, the retention of a few key genes in Hymenochaetaceae, including the ancA gene (ACEPAI_7410) and nine other genes (ACEPAI_7414, ACEPAI_7419, ACEPAI_7420, ACEPAI_7421, ACEPAI_7434, ACEPAI_7435, ACEPAI_7438, ACEPAI_7439, and ACEPAI_7441), may suggest their essential role in the sesquiterpenoids biosynthetic pathway.

There are concerns regarding the future perspective of research on Sanghuangporus. First and foremost, S. sanghuang is still being reported by its old name. In addition, other Sanghuangporus species such as S. baumii and S. vaninii have been misleadingly reported as sanghwang (S. sanghuang) [100]. Similar issues were detected in this study, where phylogenomic and BCG comparative analyses suggest the misidentification of two reference genomic data (Fig. 6): GCA_001481415 S. baumii is assumed to be S. sanghuang, and GCA_009806525 S. sanghuang is assumed to be S. vaninii [31]. Additionally, although not utilized in this study due to the short genome assembly, GCA_016618145 S. lonicericola is assumed to be a Trametes species instead (Supplementary Material 2: Fig. S4). Misidentification and the use of wrong names significantly hinder the comprehensive understanding of the chemical profile for each Sanghuangporus species and urgently demand reidentification and subsequent reorganization of compounds [101].

Conclusions

Genomic and metabolic data from four Sanghuangporus species, S. baumii, S. sanghuang, S. vaninii, and S. weigelae, were collectively analyzed to predict a BGC responsible for the biosynthesis of the isolated ABA-related sesquiterpenoids. Natural product genome mining was employed to find a BGC consisting a STS gene, ancA, that is universal across Basidiomycota and plays a pivotal role in the biosynthetic pathway of sesquiterpenoids. Further investigation into the correlation between ABA-related sesquiterpenoid profiles and the identified BGC holds promise for elucidating the intricate pathways involved in metabolite production. Future studies could focus on delineating the role of enzymes such as ACEPAI_7418 (a hypothetical protein), as well as exploring how different genetic components and gene orientations of ancA BGC across Sanghuangporus influence the metabolic types and concentrations. Integrating genetic techniques such as cloning, gene knock-out, and transcriptomics, along with compound feeding experiments, may provide valuable insights into the specific functions of these enzymes. Our findings represent an advancement toward fully understanding the medicinal potential of Sanghuangporus species.

Data availability

Genomic data and related information that support the findings in this study have been deposited in NCBI under the Bioproject PRJNA1153063.

References

  1. Zhou LW, Vlasák J, Decock C, Assefa A, Stenlid J, Abate D, et al. Global diversity and taxonomy of the Inonotus linteus complex (Hymenochaetales, Basidiomycota): Sanghuangporus gen. nov., Tropicoporus excentrodendri and T. guanacastensis gen. et spp. nov., and 17 new combinations. Fungal Divers. 2016;77:335–47.

    Article  Google Scholar 

  2. Kim HR, Hong JS, Choi JS, Han GJ, Kim TY, Kim SB, Chun HK. Properties of wet noodle changed by the addition of Sanghwang mushroom (Phellinus linteus) powder and extract. Korean J Food Sci Technol. 2005;37(4):579–83.

    Article  Google Scholar 

  3. Kang DG, Cao LH, Lee JK, Choi DH, Kim SJ, Lee H, et al. Endothelium-dependent induction of vasorelaxation by the butanol extract of Phellinus igniarius in isolated rat aorta. Am J Chin Med. 2006;34(04):655–65.

    Article  PubMed  Google Scholar 

  4. Tian XM, Dai YC, Song AR, Xu K, Ng LT. Optimization of liquid fermentation medium for production of Inonotus sanghuang (higher Basidiomycetes) mycelia and evaluation of their mycochemical contents and antioxidant activities. Int J Med Mushrooms. 2015;17(7):681–91.

    Article  PubMed  Google Scholar 

  5. Han JG, Hyun MW, Kim CS, Jo JW, Cho JH, Lee KH, et al. Species identity of Phellinus linteus (sanghuang) extensively used as a medicinal mushroom in Korea. J Microbiol. 2016;54:290–5.

    Article  CAS  PubMed  Google Scholar 

  6. Chen H, Tian T, Miao H, Zhao YY. Traditional uses, fermentation, phytochemistry and pharmacology of Phellinus linteus: a review. Fitoterapia. 2016;113:6–26.

    Article  CAS  PubMed  Google Scholar 

  7. Lin WC, Deng JS, Huang SS, Wu SH, Lin HY, Huang GJ. Evaluation of antioxidant, anti-inflammatory and anti-proliferative activities of ethanol extracts from different varieties of Sanghuang species. RSC Adv. 2017;7(13):7780–8.

    Article  CAS  Google Scholar 

  8. Cai C, Ma J, Han C, Jin Y, Zhao G, He X. Extraction and antioxidant activity of total triterpenoids in the mycelium of a medicinal fungus, Sanghuangporus sanghuang. Sci Rep. 2019;9(1):7418.

    Article  PubMed Central  PubMed  Google Scholar 

  9. Wan X, Jin X, Wu X, Yang X, Lin D, Li C, et al. Structural characterisation and antitumor activity against non-small cell lung cancer of polysaccharides from Sanghuangporus vaninii. Carbohydr Polym. 2022;276:118798.

    Article  CAS  PubMed  Google Scholar 

  10. Zheng YB, Lu CH, Shen YM. New abscisic acid-related metabolites from Phellinus vaninii. J Asian Nat Prod Res. 2012;14(7):613–7.

    Article  CAS  PubMed  Google Scholar 

  11. Huang SC, Kuo PC, Hung HY, Pan TL, Chen FA, Wu TS. Ionone derivatives from the mycelium of Phellinus linteus and the inhibitory effect on activated rat hepatic stellate cells. Int J Mol Sci. 2016;17(5):681.

    Article  PubMed Central  PubMed  Google Scholar 

  12. Chepkirui C, Cheng T, Matasyoh J, Decock C, Stadler M. An unprecedented spiro [furan-2, 1’-indene]-3-one derivative and other nematicidal and antimicrobial metabolites from Sanghuangporus sp. (Hymenochaetaceae, Basidiomycota) collected in Kenya. Phytochem Lett. 2018;25:141–6.

    Article  CAS  Google Scholar 

  13. Kinghorn AD, Falk H, Gibbons S, Kobayashi J, editors. Progress in the chemistry of organic natural products Volume 106. Switzerland: Springer International Publishing AG; 2017.

  14. Sterner O, Bergman R, Kihlberg J, Wickberg B. The sesquiterpenes of Lactarius vellereus and their role in a proposed chemical defense system. J Nat Prod. 1985;48(2):279–88.

    Article  CAS  Google Scholar 

  15. Ishikawa NK, Fukushi Y, Yamaji K, Tahara S, Takahashi K. Antimicrobial cuparene-type sesquiterpenes, enokipodins C and D, from a mycelial culture of Flammulina velutipes. J Nat Prod. 2001;64(7):932–4.

    Article  CAS  PubMed  Google Scholar 

  16. Malagòn O, Porta A, Clericuzio M, Gilardoni G, Gozzini D, Vidari G. Structures and biological significance of lactarane sesquiterpenes from the European mushroom Russula nobilis. Phytochemistry. 2014;107:126–34.

    Article  PubMed  Google Scholar 

  17. Freihorst D, Brunsch M, Wirth S, Krause K, Kniemeyer O, Linde J, et al. Smelling the difference: transcriptome, proteome and volatilome changes after mating. Fungal Genet Biol. 2018;112:2–11.

    Article  CAS  PubMed  Google Scholar 

  18. Jiang JH, Wu SH, Zhou LW. The first whole genome sequencing of Sanghuangporus sanghuang provides insights into its medicinal application and evolution. J Fungi. 2021;7(10):787.

    Article  CAS  Google Scholar 

  19. Zhou L, Fu Y, Zhang X, Wang T, Wang G, Zhou L, et al. Transcriptome and metabolome integration reveals the impact of fungal elicitors on triterpene accumulation in Sanghuangporus sanghuang. J Fungi. 2023;9(6):604.

    Article  CAS  Google Scholar 

  20. Wumuti B, Tang YQ, Wang ST, Li YW, Zou L. Cloning and function identification of the sesquiterpenes synthase gene SbTps1 in Sanghuangporus baumii. For Eng. 2021;37:33–9.

    Google Scholar 

  21. Cheng T, Chepkirui C, Decock C, Matasyoh JC, Stadler M. Sesquiterpenes from an Eastern African medicinal mushroom belonging to the genus Sanghuangporus. J Nat Prod. 2019;82(5):1283–91.

    Article  CAS  PubMed  Google Scholar 

  22. Rajachan OA, Sangdee A, Kanokmedhakul K, Tontapha S, Amornkitbamrung V, Kanokmedhakul S. Cyclofarnesane sesquiterpenoids from the fungus Sanghuangporus sp. Phytochem lett. 2020;37:17–20.

    Article  CAS  Google Scholar 

  23. Seo M, Koshiba T. Complex regulation of ABA biosynthesis in plants. Trends Plant Sci. 2002;7(1):41–8.

    Article  CAS  PubMed  Google Scholar 

  24. Cohen AC, Bottini R, Piccoli PN. Azospirillum brasilense Sp 245 produces ABA in chemically-defined culture medium and increases ABA content in arabidopsis plants. Plant Growth Regul. 2008;54:97–103.

    Article  CAS  Google Scholar 

  25. Inomata M, Hirai N, Yoshida R, Ohigashi H. The biosynthetic pathway to abscisic acid via ionylideneethane in the fungus Botrytis cinerea. Phytochemistry. 2004;65(19):2667–78.

    Article  CAS  PubMed  Google Scholar 

  26. Neill SJ, Horgan R, Walton DC, Lee TS. The biosynthesis of abscisic acid in Cercospora rosicola. Phytochemistry. 1982;21(1):61–5.

    Article  CAS  Google Scholar 

  27. Inomata M, Hirai N, Yoshida R, Ohigashi H. Biosynthesis of abscisic acid by the direct pathway via ionylideneethane in a fungus, Cercospora cruenta. Biosci Biotechnol Biochem. 2004;68(12):2571–80.

    Article  CAS  PubMed  Google Scholar 

  28. Cano-Prieto C, Undabarrena A, de Carvalho AC, Keasling JD, Cruz-Morales P. Triumphs and challenges of natural product discovery in the postgenomic era. Annu Rev Biochem. 2024;93(1):411–45.

    Article  CAS  PubMed  Google Scholar 

  29. Yan X, Ge H, Huang T, Hindra, Yang D, Teng Q, et al. Strain prioritization and genome mining for enediyne natural products. MBio. 2016;7(6):e02104–16.

  30. Nickles GR, Oestereicher B, Keller NP, Drott MT. Mining for a new class of fungal natural products: the evolution, diversity, and distribution of isocyanide synthase biosynthetic gene clusters. Nucleic Acids Res. 2023;51(14):7220–35.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  31. Shen S, Liu SL, Jiang JH, Zhou LW. Addressing widespread misidentifications of traditional medicinal mushrooms in Sanghuangporus (Basidiomycota) through ITS barcoding and designation of reference sequences. IMA Fungus. 2021;12:1–21.

    Article  Google Scholar 

  32. Jin C, Ma JX, Wang H, Tang LX, Ye YF, Li X, Si J. First genome assembly and annotation of Sanghuangporus weigelae uncovers its medicinal functions, metabolic pathways, and evolution. Front Cell Infect Microbiol. 2024;13:1325418.

    Article  PubMed Central  PubMed  Google Scholar 

  33. Schmid R, Heuckeroth S, Korf A, Smirnov A, Myers O, Dyrlund TS, et al. Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat Biotechnol. 2023;41(4):447–9.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  34. Nothias LF, Petras D, Schmid R, Dührkop K, Rainer J, Sarvepalli A, et al. Feature-based molecular networking in the GNPS analysis environment. Nat Methods. 2020;17(9):905–8.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  35. Dührkop K, Fleischauer M, Ludwig M, Aksenov AA, Melnik AV, Meusel M, Dorrestein PC, Rousu J, Böcker S. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat Methods. 2019;16(4):299–302.

    Article  PubMed  Google Scholar 

  36. Sim SB, Corpuz RL, Simmonds TJ, Geib SM. HiFiAdapterFilt, a memory efficient read processing pipeline, prevents occurrence of adapter sequence in PacBio HiFi reads and their negative impacts on genome assembly. BMC Genomics. 2022;23(1):157.

    Article  PubMed Central  PubMed  Google Scholar 

  37. Chen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. Imeta. 2023;2(2):e107.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  38. Hu J, Wang Z, Sun Z, Hu B, Ayoola AO, Liang F, et al. NextDenovo: an efficient error correction and accurate assembly tool for noisy long reads. Genome Biol. 2024;25(1):107.

    Article  PubMed Central  PubMed  Google Scholar 

  39. Nie F, Ni P, Huang N, Zhang J, Wang Z, Xiao C, et al. De novo diploid genome assembly using long noisy reads. Nat Commun. 2024;15(1):2964.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  40. Cheng H, Concepcion GT, Feng X, Zhang H, Li H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods. 2021;18(2):170–5.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Vaser R, Sović I, Nagarajan N, Šikić M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. 2017;27(5):737–46.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  42. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34(18):3094–100.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  43. Aury JM, Istace B. Hapo-G, haplotype-aware polishing of genome assemblies with accurate reads. NAR Genom Bioinform. 2021;3(2):lqab034.

    Article  PubMed Central  PubMed  Google Scholar 

  44. Marçais G, Kingsford C. A fast, lock-free approach for efficient parallel counting of occurrences of k-mers. Bioinformatics. 2011;27(6):764–70.

    Article  PubMed Central  PubMed  Google Scholar 

  45. Ranallo-Benavidez T, Jaron KS, Schatz MC. GenomeScope 2.0 and Smudgeplot for reference-free profiling of polyploid genomes. Nat Commun. 2020;11(1):1432.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  46. Roach MJ, Schmidt SA, Borneman AR. Purge Haplotigs: allelic contig reassignment for third-gen diploid genome assemblies. BMC Bioinformatics. 2018;19:1–10.

    Article  Google Scholar 

  47. Davey JW, Davis SJ, Mottram JC, Ashton PD. Tapestry: validate and edit small eukaryotic genome assemblies with long reads. BioRxiv. 2020;2020–04. Preprint at https://doiorg.publicaciones.saludcastillayleon.es/10.1101/2020.04.24.059402

  48. Kim D, Gilchrist CL, Chun J, Steinegger M. UFCG: database of universal fungal core genes and pipeline for genome-wide phylogenetic analysis of fungi. Nucleic Acids Res. 2023;51(D1):D777–84.

    Article  CAS  PubMed  Google Scholar 

  49. Flynn JM, Hubley R, Goubert C, Rosen J, Clark AG, Feschotte C, Smit AF. RepeatModeler2 for automated genomic discovery of transposable element families. Proc Natl Acad Sci USA. 2020;117(17):9451–7.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  50. Tarailo-Graovac M, Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinformatics. 2009;4.10.:4–10.

  51. Gabriel L, Brůna T, Hoff KJ, Ebel M, Lomsadze A, Borodovsky M, Stanke M. BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA. Genome Res. 2024;34(5):769–77.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  52. Chan PP, Lin BY, Mak AJ, Lowe TM. tRNAscan-SE 2.0: improved detection and functional classification of transfer RNA genes. Nucleic Acids Res. 2021;49(16):9077–96.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  53. Nawrocki EP, Eddy SR. Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics. 2013;29(22):2933–5.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  54. Kalvari I, Nawrocki EP, Ontiveros-Palacios N, Argasinska J, Lamkiewicz K, Marz M, et al. Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res. 2021;49(D1):D192–200.

    Article  CAS  PubMed  Google Scholar 

  55. Cantalapiedra CP, Hernández-Plaza A, Letunic I, Bork P, Huerta-Cepas J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol Biol Evol. 2021;38(12):5825–9.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  56. Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK, Cook H, et al. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 2019;47(D1):D309–14.

    Article  CAS  PubMed  Google Scholar 

  57. Steinegger M, Söding J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat Biotechnol. 2017;35(11):1026–8.

    Article  CAS  PubMed  Google Scholar 

  58. Jones P, Binns D, Chang HY, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30(9):1236–40.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  59. Sequeira JC, Rocha M, Alves MM, Salvador AF. UPIMAPI, reCOGnizer and KEGGCharter: bioinformatics tools for functional annotation and visualization of (meta)-omics datasets. Comput Struct Biotechnol J. 2022;20:1798–810.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  60. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, Madden TL. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:1–9.

    Article  Google Scholar 

  61. Chen TH, Chen CT, Lee CF, Huang RJ, Chen KL, Lu YC, et al. The biosynthetic gene cluster of mushroom-derived antrocin encodes two dual-functional haloacid dehalogenase-like terpene cyclases. Angew Chem Int Ed. 2023;62(9):e202215566.

    Article  CAS  Google Scholar 

  62. Blin K, Shaw S, Augustijn HE, Reitz ZL, Biermann F, Alanjary M, et al. antiSMASH 7.0: new and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Res. 2023;51(W1):W46–50.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  63. Hannigan GD, Prihoda D, Palicka A, Soukup J, Klempir O, Rampula L, Bitton DA. A deep learning genome-mining strategy for biosynthetic gene cluster prediction. Nucleic Acids Res. 2019;47(18):e110.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  64. Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. 2024;1–3.

  65. Van Kempen M, Kim SS, Tumescheit C, Mirdita M, Lee J, Gilchrist CL, et al. Fast and accurate protein structure search with Foldseek. Nat Biotechnol. 2024;42(2):243–6.

    Article  PubMed  Google Scholar 

  66. Burley SK, Bhikadiya C, Bi C, Bittrich S, Chen L, Crichlow GV, et al. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. 2021;49(D1):D437–51.

    Article  CAS  PubMed  Google Scholar 

  67. Bordin N, Sillitoe I, Nallapareddy V, Rauer C, Lam SD, Waman VP, et al. AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms. Commun Biol. 2023;6(1):160.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  68. Ding J, Tang S, Mei Z, Wang L, Huang Q, Hu H, et al. Vina-GPU 2.0: further accelerating AutoDock Vina and its derivatives with graphics processing units. J Chem Inf Model. 2023;63(7):1982–98.

    Article  CAS  PubMed  Google Scholar 

  69. O’Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, Hutchison GR. Open Babel: an open chemical toolbox. J Cheminform. 2011;3:1–14.

    Article  Google Scholar 

  70. The PyMOL Molecular Graphics System, Version 3.0 Schrödinger, LLC.

  71. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  72. Darriba D, Posada D, Kozlov AM, Stamatakis A, Morel B, Flouri T. ModelTest-NG: a new and scalable tool for the selection of DNA and protein evolutionary models. Mol Biol Evol. 2020;37(1):291–4.

    Article  CAS  PubMed  Google Scholar 

  73. Stamatakis A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  74. Portik DM, Wiens JJ. SuperCRUNCH: a bioinformatics toolkit for creating and manipulating supermatrices and other large phylogenetic datasets. Methods Ecol Evol. 2020;11(6):763–72.

    Article  Google Scholar 

  75. Minh BQ, Nguyen MAT, Haeseler A, editors. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37(5):1530–1534.

  76. Hoang DT, Chernomor O, von Haeseler A, Bui QT, Moritz T, Minh BQ. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35(2):518–22.

    Article  CAS  PubMed  Google Scholar 

  77. Kalyaanamoorthy S, Minh BQ, Scheffler K, Shankaracharya, Ranjan P, Asher A, et al. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14(6):587–589.

  78. Gilchrist CL, Booth TJ, van Wersch B, van Grieken L, Medema MH, Chooi YH. Cblaster: a remote search tool for rapid identification and visualization of homologous gene clusters. Bioinformatics Adv. 2021;1(1):vbab016.

    Article  Google Scholar 

  79. Gilchrist CL, Chooi YH. Clinker & clustermap.js: automatic generation of gene cluster comparison figures. Bioinformatics. 2021;37(16):2473–2475.

  80. Todoroki Y, Hirai N, Koshimizu K. Synthesis and biological activity of 1′-deoxy-1′-fluoro- and 8′-fluoroabscisic acids. Phytochemistry. 1995;40(3):633–41.

    Article  CAS  Google Scholar 

  81. Shirahata T, Ino C, Mizuno F, Asada Y, Hirotani M, Peterson GA, et al. γ-Ionylidene-type sesquiterpenoids possessing antimicrobial activity against Porphyromonas gingivalis from Phellinus linteus and their absolute structure determination. J Antibiot. 2017;70(5):695–8.

    Article  CAS  Google Scholar 

  82. Huang SC, Wang PW, Kuo PC, Hung HY, Pan TL. Hepatoprotective principles and other chemical constituents from the mycelium of Phellinus linteus. Molecules. 2018;23(7):1705.

    Article  PubMed Central  PubMed  Google Scholar 

  83. Yeo WH, Hwang EI, So SH, Lee SM. Phellinone, a new furanone derivative from the Phellinus linteus KT&G PL-2. Arch Pharm Res. 2007;30(8):924–6.

    Article  CAS  PubMed  Google Scholar 

  84. Fernández-López J, Martín MP, Dueñas M, Telleria MT. Multilocus phylogeny reveals taxonomic misidentification of the Schizopora paradoxa (KUC8140) representative genome. MycoKeys. 2018;38:121–7.

    Article  Google Scholar 

  85. Takino J, Kozaki T, Sato Y, Liu C, Ozaki T, Minami A, Oikawa H. Unveiling biosynthesis of the phytohormone abscisic acid in fungi: unprecedented mechanism of core scaffold formation catalyzed by an unusual sesquiterpene synthase. J Am Chem Soc. 2018;140(39):12392–5.

    Article  CAS  PubMed  Google Scholar 

  86. Neill SJ, Horgan R, Walton DC, Mercer CA. The metabolism of α-ionylidene compounds by Cercospora rosicola. Phytochemistry. 1987;26(9):2515–9.

    Article  CAS  Google Scholar 

  87. Takino J, Kozaki T, Ozaki T, Liu C, Minami A, Oikawa H. Elucidation of biosynthetic pathway of a plant hormone abscisic acid in phytopathogenic fungi. Biosci Biotechnol Biochem. 2019;83(9):1642–9.

    Article  CAS  PubMed  Google Scholar 

  88. Siewers V, Kokkelink L, Smedsgaard J, Tudzynski P. Identification of an abscisic acid gene cluster in the grey mold Botrytis cinerea. Appl Environ Microbiol. 2006;72(7):4619–26.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  89. Liu Z, Tong X, Liu R, Zou L. Metabolome and transcriptome profiling reveal that four terpenoid hormones dominate the growth and development of Sanghuangporus baumii. J Fungi. 2022;8(7):648.

    Article  CAS  Google Scholar 

  90. Kato T, Oritani T, Yamashita K. Metabolism of (2 Z, 4 E)-γ-ionylideneethanol and (2 Z, 4 E)-γ-ionylideneacetic acid in Cercospora cruenta. Agric Biol Chem. 1987;51(10):2695–9.

    CAS  Google Scholar 

  91. Yamamoto H, Inomata M, Uchiyama T, Oritani T. Identification of 2, 3-dihydro-γ-ionylideneethanol in Cercospora cruenta. Biosci Biotechnol Biochem. 2001;65(4):810–6.

    Article  CAS  PubMed  Google Scholar 

  92. Klarlund JK, Guilherme A, Holik JJ, Virbasius JV, Chawla A, Czech MP. Signaling by phosphoinositide-3, 4, 5-trisphosphate through proteins containing pleckstrin and Sec7 homology domains. Science. 1997;275(5308):1927–30.

    Article  CAS  PubMed  Google Scholar 

  93. Klarlund JK, Rameh LE, Cantley LC, Buxton JM, Holik JJ, Sakelis C, et al. Regulation of GRP1-catalyzed ADP ribosylation factor guanine nucleotide exchange by phosphatidylinositol 3, 4, 5-trisphosphate. J Biol Chem. 1998;273(4):1859–62.

    Article  CAS  PubMed  Google Scholar 

  94. Rao YK, Wu AT, Geethangili M, Huang MT, Chao WJ, Wu CH, et al. Identification of antrocin from Antrodia camphorata as a selective and novel class of small molecule inhibitor of Akt/mTOR signaling in metastatic breast cancer MDA-MB-231 cells. Chem Res Toxicol. 2011;24(2):238–45.

    Article  CAS  PubMed  Google Scholar 

  95. Yeh CT, Huang WC, Rao YK, Ye M, Lee WH, Wang LS, et al. A sesquiterpene lactone antrocin from Antrodia camphorata negatively modulates JAK2/STAT3 signaling via microRNA let-7c and induces apoptosis in lung cancer cells. Carcinogenesis. 2013;34(12):2918–28.

    Article  CAS  PubMed  Google Scholar 

  96. Chiu KY, Wu CC, Chia CH, Hsu SL, Tzeng YM. Inhibition of growth, migration and invasion of human bladder cancer cells by antrocin, a sesquiterpene lactone isolated from Antrodia cinnamomea, and its molecular mechanisms. Cancer Lett. 2016;373(2):174–84.

    Article  CAS  PubMed  Google Scholar 

  97. Zhang J, Chen B, Dai H, Ren J, Zhou L, Wu S, Liu H. Sesquiterpenes and polyphenols with glucose-uptake stimulatory and antioxidant activities from the medicinal mushroom Sanghuangporus sanghuang. Chin J Nat Med. 2021;19(9):693–9.

    CAS  PubMed  Google Scholar 

  98. Wu J, Yang X, Duan Y, Wang P, Qi J, Gao JM, Liu C. Biosynthesis of sesquiterpenes in basidiomycetes: a review. J Fungi. 2022;8(9):913.

    Article  CAS  Google Scholar 

  99. Lu J, Su M, Zhou X, Li D, Niu X, Wang Y. Research progress of bioactive components in Sanghuangporus spp. Molecules. 2024;29(6):1195.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  100. Chang YH, Kim TR, Kim HS, Yeo IH, Lee SY, Ha HC. Genetic characterization of Phellinus baumii PMO-P4 by analyzing restriction fragment length polymorphisms of nuclear ribosomal DNA internal transcribed spacers (ITS). J Mushroom. 2006;4(2):43–7.

    Google Scholar 

  101. Zhou LW, Ghobad-Nejhad M, Tian XM, Wang YF, Wu F. Current status of ‘Sanghuang’ as a group of medicinal mushrooms and their perspective in industry development. Food Rev Int. 2022;38(4):589–607.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This research was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation (NRF) of Korea (RS-2023-NR076619) for YC, CWS, and YWL, and the Bio & Medical Technology Development Program of the NRF funded by the Korean government (MSIT) (No. RS-2024-00352229) for SHS.

Author information

Author notes

  1. Yoonhee Cho, Chang Wan Seo and Hyeonjae Cho are co-first authors.

Authors and Affiliations

  1. School of Biological Sciences and Institute of Biodiversity, Seoul National University, Seoul, 08826, Republic of Korea

    Yoonhee Cho, Chang Wan Seo, Abel Severin Lupala & Young Woon Lim

  2. College of Pharmacy, Natural Products Research Institute, Seoul National University, Seoul, 08826, Republic of Korea

    Hyeonjae Cho, Yeongwoon Jin & Sang Hee Shim

  3. Department of Microbiology, Parasitology and Biotechnology, Sokoine University of Agriculture, P.O. Box 3019, Morogoro, 67125, Tanzania

    Abel Severin Lupala

Authors
  1. Yoonhee Cho
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Chang Wan Seo
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Hyeonjae Cho
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Yeongwoon Jin
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Abel Severin Lupala
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Sang Hee Shim
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Young Woon Lim
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

YC, SHS, and YWL designed the work; YC and CWS acquired, analyzed, and interpreted the genomic data; HJC, YJ, and SHS acquired, analyzed, and interpreted the metabolic data; YC, CWS, HJC, and ASL drafted the manuscript; YC, CWS, HJC, SHS, and YWL revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Sang Hee Shim or Young Woon Lim.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

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://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cho, Y., Seo, C.W., Cho, H. et al. A conserved terpene cyclase gene in Sanghuangporus for abscisic acid-related sesquiterpenoid biosynthesis. BMC Genomics 26, 378 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12864-025-11542-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12864-025-11542-9

Keywords

BMC Genomics

ISSN: 1471-2164

Contact us