Multiomics Analysis across the Life Cycle Identifies Zn₂Cys₆_61 as a Target for Enhancing Triterpenoid Production in Ganoderma lucidum

Abstract

Ganoderic acids (GAs) are high-value lanostane-type triterpenoids derived from Ganoderma lucidum (G. lucidum) with broad applications in functional foods and nutraceuticals, yet their low natural abundance limits industrial production. In this study, an integrated life-cycle multiomics analysis combining metabolomics, transcriptomics, and proteomics was conducted across six developmental stages in four G. lucidum strains to elucidate regulatory mechanisms governing GA biosynthesis. Weighted gene coexpression network analysis identified candidate cytochrome P450 enzymes and transcription factors associated with GA accumulation. A Zn₂Cys₆-type transcription factor, Zn₂Cys₆_61, was identified as a central regulator and functionally validated through overexpression and RNA interference. Genetic manipulation of Zn₂Cys₆_61 expression significantly altered GA levels, with overexpression markedly enhancing GA accumulation. Further analysis demonstrated that Zn₂Cys₆_61 directly binds to and activates the promoter of squalene synthase, a key enzyme in triterpenoid backbone biosynthesis. Together, these findings identify Zn₂Cys₆_61 as an effective engineering target and provide a transcription factor-based strategy for improving GA production in medicinal mushrooms.

1. Introduction

Mushrooms are widely recognized for their dual roles as food and medicine and contain diverse bioactive compounds with demonstrated anticancer, hepatoprotective, antidiabetic, and anti-inflammatory activities. − However, the mushroom-related basic research is far behind that of plants. , Among them, Ganoderma lucidum (G. lucidum) is one of the most renowned medicinal mushrooms, with over 2000 years of documented use in traditional Chinese medicine and growing applications in functional foods, beverages, and nutraceutical formulations. − It was officially included in China’s “medicine and food homology” list in 2023. The global market for G. lucidum-related products exceeds 2.5 billion USD annually, reflecting its high economic value.

The principal bioactive constituents of G. lucidum include polysaccharides, triterpenoids, sterols, proteins, amino acids, and alkaloids. Among these compounds, lanostane-type triterpenoids, which are known as ganoderic acids (GAs), are the major pharmacologically active compounds. To date, approximately 171 structurally diverse GAs have been isolated, and their biological activities are closely linked to variations in side-chain functional groups. −

GAs exhibit complex stereochemistry, making chemical synthesis extremely challenging. However, GA content in G. lucidum fruiting bodies is inherently low, generally below 0.5%, which severely restricts large-scale production and industrial applications. − Improving GA biosynthesis therefore requires a deeper understanding of pathway regulation and effective engineering strategies.

The upstream biosynthetic pathway from acetyl-CoA to lanosterol has been well elucidated. Key enzymes include 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR), the rate-limiting enzyme of the mevalonate pathway; squalene synthase (SQS), which commits flux to the triterpenoid branch; and lanosterol synthase (LS), which completes cyclization to the lanostane skeleton. − In contrast, downstream oxidative tailoring steps catalyzed mainly by cytochrome P450 monooxygenases (CYP450s) remain poorly understood, − with only a limited number of CYP450s functionally linked to GA biosynthesis. ,−

Transcription factors (TFs), particularly members of the fungus-specific Zn₂Cys₆ family, have emerged as key regulators of secondary metabolism. Representative examples include AflR in Aspergillus flavus, which coordinates aflatoxin biosynthesis, GAL4 involved in galactose utilization in Saccharomyces cerevisiae, and GilZ regulating gliotoxin formation in Aspergillus fumigatus. Despite their established regulatory roles in filamentous fungi, the transcriptional control of GA biosynthesis in G. lucidum remains largely unresolved.

In this study, we developed an integrated multiomics frameworkcombining metabolomics, transcriptomics, proteomics, and weighted gene coexpression network analysisto map developmental stage-specific regulation of GA biosynthesis across multiple G. lucidum strains. This approach enabled the identification of regulatory modules associated with GA accumulation and highlighted the Zn₂Cys₆-type transcription factor Zn₂Cys₆_61 as a central regulatory hub. Functional analyses demonstrated that Zn₂Cys₆_61 directly activates SQS transcription and enhances GA biosynthesis. These findings uncover a previously uncharacterized regulatory mechanism and provide a transcription-factor-based engineering strategy for improving triterpenoid production in G. lucidum. The multiomics workflow presented here establishes a scalable framework for accelerating metabolic engineering of medicinal mushrooms and other fungal biofactories.

2. Materials and Methods

2.1. Reagents and Plant Materials

Methanol (MeOH), formic acid, acetonitrile, ethanol, and ammonium acetate (all liquid chromatography–mass spectrometry (LC–MS) grades) were obtained from Thermo Fisher Scientific. Reference standards (>99.8%, quantification grade) ganodernic acid I (PS000600), ganoderic acid C2 (PS000597), ganoderic acid C6 (PS010883), ganoderic acid G (PS010838), ganoderenic acid B (PS010839), ganoderic acid N (PS010893), ganoderic acid B (PS010857), ganoderic acid LM2 (PS000601), ganoderic acid A (PS010388), ganoderic acid H (PS010902), lucidenic acid A (PS010891), ganoderic acid D2 (PS011345), ganoderenic acid D (PS010884), ganoderic acid C1 (PS010882), ganoderic acid F (PS013922), ganodermanontriol (PS013921), ganoderic acid DM (PSD250106-535), and ganoderic acid TR (PSD250106-536) were purchased from Chengdu Push Biotechnology Co., Ltd.

Samples of G. lucidum from four strains (237, 242, 249, and 250) were collected at six defined developmental stages by the Zhejiang Shouxiangu Botanical Drug Institute Co., Ltd. The stages included mycelium (JS, day 8), primordia (YJ, day 22 after soil covering), cap formation (KS, day 48), fruiting body before sporulation (CS, day 68), fruiting body after sporulation (F0, day 155), and spore powder (FR, days 80–155). All samples were rapidly frozen in liquid nitrogen and stored at −80 °C until analysis. Metabolomic profiling was performed with five biological replicates per stage (120 samples total), while transcriptomic and proteomic analyses used three biological replicates per stage. Because the F0-stage sample of strain 249 was oven-dried, RNA and protein could not be extracted, resulting in 69 samples for transcriptomic and proteomic analyses.

2.2. Mycelial Dry Weight Determination

Three agar blocks (1 cm² each) were excised from actively growing mycelial plates and inoculated into 100 mL of seed medium containing 35 g/L glucose, 5 g/L tryptone, 2.5 g/L yeast extract, 0.5 g/L MgSO₄·7H₂O, 1 g/L KH₂PO₄, and 0.05 g/L vitamin B₁. Cultures were incubated at 28 °C with shaking at 120 rpm in the dark for 7 days.

The resulting seed culture was then transferred into 100 mL of a fresh fermentation medium at a 5% (v/v) inoculation ratio. The fermentation medium consisted of 35 g/L lactose, 5 g/L tryptone, 5 g/L yeast extract, 0.5 g/L MgSO₄·7H₂O, 1 g/L KH₂PO₄, and 0.05 g/L vitamin B₁. Cultures were incubated under the same conditions (28 °C, 120 rpm, and dark) for an additional 7 days. After cultivation, mycelia were harvested, placed on aluminum foil, and dried in a forced-air oven at 45 °C with periodic turning until a constant weight was achieved. The resulting dry weight was recorded as mycelial dry biomass. Each transformant was analyzed with three biological replicates.

2.3. LC-MS Analysis and Ganoderic Acid Metabolite Profiling

Samples were dried in an oven at 60 °C for 48 h and then ground into powder and sieved through a 40-mesh screen. 0.5 g of the powdered material was mixed with 40 μL of internal standard working solution and extracted with 20 mL of methanol via ultrasonic treatment (50 kHz, 350 W) for 30 m. The extracts were centrifuged at 13,000 rpm for 10 m. A 1 mL aliquot of the supernatant was accurately transferred and diluted to a final volume of 5.0 mL with methanol. Then, the solution was filtered through a 0.22 μm filter.

Chromatographic separation was performed on a Waters ACQUITY UPLC HSS T3 column (1.8 μm, 2.1 × 100 mm) at 25 °C with a flow rate of 0.45 mL/min. The injection volume was 1.0 μL, and detection was conducted at 254 nm. The mobile phase was composed of 0.1% formic acid (A) and acetonitrile (B), with a gradient elution program of 5% B (0 min), 5–26.5% B (0–1 min), 26.5% B (1–3 min), 26.5–35% B (3–12 min), 35–50% B (12–14 min), 50–60% B (14–17 min), 60–100% B (17–20 min), and 100% B (20–22 min). The UPLC system was coupled to a Synapt XS HDMS mass spectrometer (Waters Corporation) equipped with an electrospray ionization source. Data were acquired in full-scan mode over a m/z range of 50–1200. The source temperature was set to 140 °C, with a capillary voltage of 2.5 kV and cone voltage of 35 V. Collision energies were set at 6 eV (low) and ramped from 25 to 50 eV (high), with a desolvation gas flow of 1000 L/h.

2.4. Transcriptome Profiling

Total RNA was extracted from the samples according to the EasyPure RNA Kit (ER101-01, TransGene Biotech) instructions. After all samples passed quality control, cDNA libraries were constructed, and transcriptome sequencing was performed on the NovaSeq 6000 S4 system. This was performed by Wuhan Bena Technology Co., Ltd. (Wuhan, China). Raw sequencing reads in FASTQ format were quality-filtered using Trimmomatic v0.39 to remove adapter sequences, low-quality reads, and reads containing poly-N stretches. Clean reads were then aligned to the G. lucidum reference genome (unpublished) using HISAT2 v2.2.1. Gene-level quantification was carried out with HTSeq-count v2.0.1, and expression values were normalized using the trimmed mean of M-values (TMM) method implemented in edgeR. To identify differentially expressed genes (DEGs), transcript abundance in the mycelia opening, mature fruiting body, and spore stages was compared to that in the primordia stage. DEGs were defined as those with a fold change >2 and a false discovery rate (FDR)-adjusted P-value < 0.05, as described previously. Functional enrichment analysis of the DEGs was conducted using TBtools v2.019, , with annotations based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases.

2.5. Proteome Profiling

Proteins were extracted from powdered samples using lysis buffer supplemented with 1% protease inhibitor (Sigma, P8215). After centrifugation at 20,000 g for 10 min at 4 °C, supernatants were collected and protein concentrations were determined using a BCA assay (Sangon Biotech, C503021). Equal amounts of protein were reduced with 5 mM dithiothreitol at 30 °C for 45 min and alkylated with 30 mM iodoacetamide for 1 h at room temperature in the dark. Proteins were precipitated with prechilled acetone, washed three times, and resuspended in 0.1 M triethylammonium bicarbonate. Proteolytic digestion was performed with sequencing-grade trypsin (Promega, V5111) at a 1:25 (w/w) enzyme-to-substrate ratio for 12 h at 37 °C. The reaction was terminated with 1% trifluoroacetic acid, and peptides were desalted using a C18 solid-phase extraction column (Phenomenex) and vacuum-dried prior to analysis.

Peptide samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) on a Q Exactive HF-X mass spectrometer (Thermo Scientific) equipped with a nanoelectrospray ionization (NSI) source and coupled to an online UPLC system. The instrument was operated in data-dependent acquisition (DDA) mode, performing one full MS scan followed by 20 MS/MS scans of the top precursor ions exceeding an intensity threshold of 5 × 10⁴, with a dynamic exclusion window of 15 s. Automatic gain control (AGC) targets were set to 3 × 10⁶ for MS scans and 1 × 10⁵ for MS/MS scans. All LC-MS/MS analyses were carried out by Micrometer Biotech Company (Hangzhou, China). Raw MS data were processed using MaxQuant software (v1.5.2.8). , Database searches were performed against a reverse (decoy) database to estimate the false discovery rate (FDR). Peptide-spectrum matches (PSMs) were validated using percolator algorithm with p < 0.05.

2.6. Construction and Visualization of Weighted Gene Coexpression Networks

Weighted gene coexpression network analysis (WGCNA) was employed to construct gene coexpression modules from the full set of expressed genes using the R software package WGCNA. Network construction was carried out with the blockwiseModules function, applying default settings except for the following parameters: the soft-thresholding power was selected based on scale-free topology, while the module merging threshold (mergeCutHeight) was set to 0.1, deepSplit to 4, and the minimum module size (minModuleSize) to 30.

Expression data were normalized using the TMM method, and an adjacency matrix was generated to quantify the pairwise correlations among genes. This matrix was then transformed into a topological overlap matrix (TOM) to measure the network connectivity. Genes with highly similar expression profiles were clustered into distinct coexpression modules, and the first principal component of each moduletermed the module eigengenewas computed.

Subsequently, correlations between module eigengenes and ganoderic acid content during G. lucidum development were evaluated to identify modules associated with metabolite accumulation. For network visualization, the gene–gene interaction networks were exported and graphically rendered using Cytoscape (version 3.9.1).

2.7. Acquisition of Transgenic G. lucidum Transformations

For the overexpression construct, the coding sequence of Zn₂Cys₆_61 was amplified and inserted into the linearized pCAMBIA1304-MsdhB vector using homologous recombination. For the RNAi construct, approximately 300 bp nonconserved fragments of Zn₂Cys₆_61 were cloned into the pSilent-1 vector. All recombinant plasmids were verified by sequencing after transformation into E. coli DH5α competent cells (DL1001, Weidi Biotechnology).

G. lucidum transformants were generated via poly­(ethylene glycol) (PEG)-mediated protoplast transformation. Protoplasts were prepared by digesting 4-day-old mycelia with 2% (w/v) lysing enzymes in 0.6 M mannitol at 30 °C for 2.5 h with gentle shaking. Approximately 1 × 10⁷ protoplasts were collected by centrifugation at 3000 g for 5 min at 4 °C and resuspended in 160 μL of STC buffer (1.2 M sorbitol, 10 mM Tris-HCl, 50 mM CaCl₂, pH 7.5). The protoplast suspension was mixed with 5 μg of plasmid DNA, 100 μg of heparin sodium, and 5 μg of spermidine and incubated on ice for 30 min, followed by the addition of 50 μL of PTC buffer (40% PEG 4000, 0.6 M mannitol, 50 mM CaCl₂). After a 30 min heat shock at 28 °C, the mixture was plated on CYM regeneration medium. After 12 h, the plates were overlaid with selective medium containing 4 μg/mL carboxin. Putative transformants appearing after 7–10 days were verified by PCR amplification of vector-specific markers. The primers used for gene cloning are given in Table S6, while the primers for PCR identification are given in Table S7.

2.8. Quantitative Reverse Transcription-PCR

Total RNA was isolated from mycelial powder ground using a tissue homogenizer under liquid nitrogen using a FastPure Universal Plant Total RNA Isolation Kit (RC411, Vazyme). The RNA was subsequently reverse-transcribed to synthesize first-strand cDNA using PrimeScript II First Strand cDNA Synthesis Kit (Takara, Japan). qPCR reactions were set up using an iQ SYBR Green Supermix (1708884, Bio-Rad). Relative expressions were calculated and normalized to the expression of 18S rRNA. The primer pairs used in these qPCR reactions are listed in Table S8. The experiments were performed in triplicate, and the results are presented as the means ± SDs.

2.9. Determination of Total Triterpenoid Content in G. lucidum Strains

To construct the standard curve, a 0.1 mg/mL oleanolic acid standard solution was prepared. Aliquots of 0, 0.1, 0.2, 0.3, 0.4, and 0.5 mL were transferred to 10 mL glass tubes and evaporated in a 100 °C water bath for 5–10 min. 0.1 mL of 5% vanillin–glacial acetic acid solution and 0.4 mL of perchloric acid were then added. The mixture was vortexed and incubated in a 70 °C water bath for 15 min, cooled in an ice bath for 5 min, followed by the addition of 2 mL of ethyl acetate. Absorbance was measured at 548 nm to generate the standard curve. For sample preparation, dried G. lucidum mycelia were ground into a fine powder using a cryogenic grinder. A 25 mg aliquot was extracted with 1.5 mL of 95% ethanol at room temperature for 2 h with intermittent shaking, followed by 30 min of ultrasonication and centrifugation at 4000 rpm for 15 min. Then, 200 μL of the supernatant was processed using the same colorimetric reaction as described for the standard solution.

2.10. Subcellular Localization of Zn₂Cys₆_61

The p1034W was digested, recovered, and purified for homologous recombination with Zn₂Cys₆_61-EGFP, EGFP, and EGFP1 containing homologous arms. The primers for PCR are listed in Table S9. The recombinant product was transferred into E. coli DH5α competent cells and cultured overnight at 37 °C. The plasmid was extracted from the bacterial solution with correct sequencing. The strains of WT, p1034W-EGFP1, and p1034W–Zn₂Cys₆_61-EGFP were inoculated in the PDA medium containing 10 mM CaCl2. Then, the coverslip was stained with DAPI and rinsed with PBS several times. The positions of the EGFP protein and Zn₂Cys₆_61-EGFP fusion protein were observed.

2.11. Yeast One-Hybrid (Y1H) Assay

Yeast one-hybrid (Y1H) assays were performed by using the MATCHMAKER Gold Yeast One-Hybrid system (630491, Clontech) to examine the direct binding of Zn₂Cys₆_61 to the promoter of SQS. Promoter fragments of SQS containing G-box elements were amplified using primers listed in Table S10 and cloned into the pAbAi vector using the In-Fusion HD Cloning Kit (639648, Takara Bio). The recombinant bait plasmid was linearized with BstB I (R0519S, New England Biolabs) and integrated into the genome of the Y1HGold yeast strain (630491, Clontech). Transformants were selected on SD/-Ura plates at 30 °C. Autoactivation of the bait strain was assessed on SD/-Ura plates containing increasing concentrations of aureobasidin A (AbA) (630466, Clontech) to determine the minimum inhibitory concentration for screening.

The prey vector pGADT7-Zn₂Cys₆_61 was constructed by amplifying the coding sequence of Zn₂Cys₆_61 and cloning it into the pGADT7 vector digested with Sfi I (R0123S, New England Biolabs) and BamH I (R0136S, New England Biolabs). The recombinant prey plasmid was transformed into the bait yeast strain along with an empty pGADT7 control by using the Super Competent Cell Preparation and Transformation Kit (CD501, Vazyme). Positive protein–DNA interactions were evaluated based on growth on SD/-Leu medium supplemented with the predetermined concentration of AbA after incubation at 30 °C for 3–5 days.

2.12. The Dual-Luciferase Reporter Assay

The effector vector was constructed by inserting the coding sequence of Zn ₂ Cys ₆ _61 into the pGreenII-62SK vector digested with the appropriate restriction enzymes. The reporter vector was constructed by cloning the promoter fragment of SQS into the pGreenII-0800-LUC vector. The integrity of all of the constructs was confirmed by sequencing. The resulting effector and reporter plasmids, along with their corresponding empty vectors as negative controls, were separately transformed into Agrobacterium tumefaciens strain GV3101. Transformed Agrobacterium strains were cultured in LB medium supplemented with appropriate antibiotics at 28 °C to an OD₆₀₀ of approximately 0.8. Cells were harvested and resuspended in infiltration buffer (10 mM MES, pH 5.7, 10 mM MgCl₂, and 150 μM acetosyringone). Effector and reporter bacterial suspensions were mixed at a 1:8 ratio and infiltrated into leaves of 4–6-week-old Nicotiana benthamiana using a needleless syringe. After 48 h of incubation under normal growth conditions, infiltrated leaf areas were sprayed with 1 mM D-luciferin (LUCK, GoldBio). Luminescence signals were captured and quantified using a chemiluminescence imaging system (5200Multi, Tanon).

2.13. Electrophoretic Mobility Shift Assay (EMSA)

A biotin-labeled double-stranded DNA probe containing the SQS promoter sequence (5′-CGACGGTATCGATAAGCGGAAGACAGCTTTCCAG-3′) was synthesized, and an unlabeled probe with the same sequence was used as a specific competitor. Purified recombinant Zn₂Cys₆_61 fusion protein was used in the binding reactions. EMSA was conducted using a chemiluminescent EMSA kit (GS606, Beyotime) following the manufacturer’s protocol. Binding reactions were incubated at room temperature for 10 min to reduce nonspecific interactions, followed by incubation with the biotin-labeled probe for 20 min. Competition assays were performed by adding increasing amounts of unlabeled probe. After the addition of loading buffer, samples were immediately resolved on a 6% nondenaturing polyacrylamide gel prepared in 0.5× TBE buffer and electrophoresed at 120 V for 60 min with gel temperature maintained below 30 °C. Protein–DNA complexes were transferred to a nylon membrane in 0.5× TBE buffer at a constant current of 380 mA for 60 min, followed by UV cross-linking for 15 min. Chemiluminescent detection was performed using streptavidin–HRP conjugate and BeyoECL Moon substrate, and signals were visualized with a high-sensitivity imaging system.

2.14. Statistical Analysis

Experimental data were obtained from three or more biological replicates, presented as mean ± SD using Graph Prism 9 software. Unpaired two-tailed Student’s t tests were performed, and p-values <0.05 were considered statistically significant. SIMCA14.1 software was used for PCA and OPLS-DA.

3. Results and Discussion

3.1. Significant Variation in Ganoderic Acid Content of Four G. lucidum Strains during Six Developmental Stages

To evaluate the dynamic changes in GA content across different developmental stages in four G. lucidum strains, samples were collected at six defined growth stages: JS, YJ, KS, CS, F0, and FR (Figure A). Eighteen representative GAs were quantified, including ganoderic acid I, C2, C6, G, N, B, LM2, A, H, D2, C1, F, DM, and TR; ganoderenic acids B and D; lucidenic acid A, and ganodermanontriol.

1.

Morphological characteristics and ganoderic acid contents of G. lucidum at different developmental stages. (A) Representative morphological features of G. lucidum at six developmental stages: JS, YJ, KS, CS, F0, and FR. (B) Contents of ganoderic acids I, C2, C6, G, N, B, LM2, A, H, D2, C1, F, DM, and TR; ganoderenic acids B and D; lucidenic acid A; and ganodermanontriol in samples of four G. lucidum strains collected at the corresponding developmental stages. Data are presented as means ± SD (n = 5). Unpaired two-tailed Student’s t tests were performed. Different letters indicate significant differences (p < 0.05).

Significant differences in the accumulation of these GAs were observed among the four strains across six developmental stages. In strain “237”, ganodermanontriol and ganoderic acid N accumulated at markedly higher levels than in the other strains, whereas most other GAs were present at consistently lower levels. Ganoderic acid C6 showed the greatest differential accumulation at the KS stage, with strain “242” reaching 132.47 mg/gapproximately 21-fold higher than that of strain “237” (Figure B). This observation is consistent with previous reports showing enhanced secondary metabolite accumulation during fruiting body maturation and substantial compositional differences among G. lucidum varieties. ,

None of the 18 GAs were detected at the JS stage, and GA TR was absent at the YJ stage (Figure B). At the FR stage, GAs-D2 and TR were not detected. Among the remaining 16 compounds, strain “249” exhibited the highest levels during the FR stage with significantly greater accumulation than the other three strains. Specifically, the content of these 16 GAs in strain “249” was 1.4- to 28.9-fold higher than that in strain “237” at this stage (Figure B).

3.2. Distinct Metabolite Accumulation among G. lucidum Strains at the Same Developmental Stage

To further investigate the metabolite variation among G. lucidum strains, metabolomic profiling was performed across all six developmental stages. Principal component analysis (PCA) revealed clear separation of the four strains at each stage, with PC1 explaining over 38% of the variance and PC2 indicating high reproducibility among biological replicates (Supporting Figure S1A). All samples fell within the 99% confidence interval in Hotelling’s T² test, confirming data set reliability (Supporting Figure S1B).

Partial least-squares discriminant analysis (PLS-DA) models constructed for each developmental stage showed strong explanatory and predictive power, with R ² and Q ² values exceeding 0.90 (Figure A). Given its relatively low GA content, strain “237” was selected as the reference for comparative analysis. Orthogonal PLS-DA (OPLS-DA) performed at all six stages also yielded robust models (R ² and Q ² > 0.90), with no evidence of overfitting based on permutation tests (Supporting Figure S1C–H), indicating significant metabolic differences among strains at the same developmental stage.

2.

Correlation analysis of differential metabolites among four G. lucidum strains at the same developmental stage. (A) Score plot of PLS-DA. (B) DEMs correlation heatmap at the JS stage. (C) DEMs correlation heatmap at the YJ, KS, CS, and F0 stage. (D) DEMs correlation heatmap at the FR stage. The Pearson correlation coefficient ranges between 1 and −1, with 1 representing a perfect positive correlation in orange and −1 representing a perfect negative correlation in green, n = 5.

To identify key discriminative metabolites, variables with VIP ≥ 1 and p ≤ 0.05 were selected based on the OPLS-DA models (Supporting Figure S2A–F, Table S1). The metabolite names corresponding to each code are listed in Appendix A. At the JS stage, metabolites such as ganoderic acids Mg, Mb, V, GS-2, S3, and Mh were differentially accumulated. At YJ, KS, CS, and F0 stages, the differential metabolites mainly included ganoderic acids B, H, G, C2, C6, K, C1, A, AM1, D2, L, DM, lucidenic acids A, D, E, N,ganoderenic acids B, H, and ganodermanontriol. At the FR stage, the differential metabolites mainly included ganoderic acids A, G, C1, F, H, B, C2, C6, D2, and ganoderenic acid H.

Pairwise correlation analysis revealed distinct metabolite interaction patterns across developmental stages (Figure B–D). Notably, ganoderic acids Mg, Mb, and GS-2 were significantly correlated at JS. During YJ to F0, ganoderic acids A, C1, F, and ganoderenic acid H showed strong positive correlations, as did TR with D, and a cluster comprising B, H, G, C2, C6, K, DM, I, AM1, D2, lucidenic acids D, E, A, N, and lucidenic acid B and triterpenone. At FR, significant correlations were observed among ganoderic acids A, G, C1, D2, C6, F, H, B, and C2, and lucidenic acid H.

3.3. Transcriptome Analysis of Four G. lucidum Strains during Six Developmental Stages

To further investigate the underlying causes of metabolite variation among different G. lucidum strains, transcriptome analyses were performed to identify genes potentially involved in the regulation of GA biosynthesis. Some studies have shown that genes with similar expression patterns may share biological functions, , we selected “237” as the reference group and identified differentially expressed genes (DEGs) in strains “242,” “249,” and “250” across six developmental stages (Supporting Figure S3A). The sample F0249 was excluded from the transcriptomic and proteomic analyses because it was inadvertently oven-dried during processing, which prevented successful RNA and protein extraction. PCA revealed clear separation among different groups and good consistency among most biological replicates, except for samples JS242-1, KS237-3, and JS249-3, which showed poor intragroup correlation and were therefore excluded from downstream analyses (Supporting Figure S3B,C). Several genes involved in the biosynthetic pathway of ganoderic acids such as AACT, HMGS, PMK, FPS, and SQS were highly expressed across different developmental stages and G. lucidum strains. Notably, AACT and SQS consistently showed relatively high expression levels across different developmental stages and G. lucidum strains (Figure A).

3.

Transcriptome profiling of four G. lucidum strains at six developmental stages. (A) Expression profiles of genes involved in the ganoderic acid biosynthetic pathways in six stages of four G. lucidum strains. The key enzymes involved in ganoderic acid biosynthesis in G. lucidum are highlighted in red. Color scale represents log₂(TMM+1). AACT, acetyl-CoA acetyltransferase; HMGR, 3-hydroxy-3-methylglutaryl-CoA reductase; HMGS, 3-hydroxy-3-methylglutaryl-CoA synthase; MK, mevalonate kinase; PMK, phosphomevalonate kinase; MVD, mevalonate pyrophosphate decarboxylase; IDI, isopentenyl diphosphate isomerase; FPS, farnesyl pyrophosphate synthase; SQS, squalene synthase; SE, squalene epoxidase; LS, lanosterol synthase. (B–G) GO and KEGG enrichment analysis of DEGs at six stages.

Although several key genes involved in the MVA pathway, including AACT and SQS, exhibited relatively high expression levels at the JS stage, none of the representative ganoderic acids were detected at this stage. At the early mycelial stage, activation of the MVA pathway is likely required to support basal sterol biosynthesis for cell proliferation and membrane formation, rather than secondary metabolite accumulation. − In contrast, the oxidative tailoring steps responsible for structural diversification of ganoderic acids, which are predominantly catalyzed by cytochrome P450 monooxygenases, appear to be tightly developmentally controlled. Both transcriptomic and proteomic data indicate that the expression and abundance of these downstream enzymes are markedly low at the JS stage, thereby limiting the conversion of triterpenoid precursors into detectable ganoderic acids. Likewise, the absence of GA-TR at the YJ stage may reflect stage-dependent metabolic flux redistribution and compound-specific biosynthetic timing, a phenomenon commonly observed for structurally complex lanostane-type triterpenoids. ,,

GO and KEGG enrichment analysis was conducted for DEGs from each stage, with the top 10 terms ranked by p-values displayed. In the molecular function category, “oxidoreductase activity” was consistently the most significantly enriched term, suggesting conserved redox enzyme activity across stages. In the cellular component category, JS, YJ, and F0 stages were enriched for “extracellular region,” potentially related to the secretion of compounds such as polysaccharides or proteins, while KS, CS, and FR stages were enriched for “membrane,” possibly associated with transmembrane transport or signal transduction, indicating dynamic subcellular localization during development. In the biological process category, “secondary metabolic process” was the top term in JS and KS stages, likely related to triterpenoid and phenolic compound biosynthesis; “carbohydrate metabolic process” was predominant in YJ and CS stages, possibly reflecting energy metabolism or polysaccharide accumulation; “cytoplasmic translation” was enriched in F0, implying active protein synthesis; and “transmembrane transport” dominated in FR, possibly related to nutrient uptake or signaling (Figure B–G).

KEGG enrichment analysis of DEGs from all six stages revealed significant enrichment (p < 0.05) in pathways closely related to CYP450, TF, and GA biosynthesis. Specifically, JS was enriched for CYP450, Biosynthesis of other secondary metabolites, Pantothenate and CoA biosynthesis, and Ubiquinone and other terpenoid-quinone biosynthesis; YJ was enriched for Biosynthesis of other secondary metabolites, Transcription factors, CYP450, and Metabolism of cofactors and vitamins; KS, CS, and F0 stages showed consistent enrichment in the biosynthesis of other secondary metabolites and CYP450 pathways (Figure B–G).

3.4. Proteome Analysis of Four G. lucidum Strains during Six Developmental Stages

To further elucidate changes at the protein level of key enzymes and TFs, proteomic analyses were performed. Using the “237” strain as reference, differentially expressed proteins (DEPs) in strains “242”, “249”, and “250” were identified across six stages using a fold-change threshold >2 and p < 0.05 (Supporting Figure S4A). PCA of proteomic data revealed high reproducibility among biological replicates and clear separation among samples from six developmental stages, indicating pronounced stage-dependent proteomic variation (Supporting Figure S4B). Pearson correlation analysis further confirmed strong correlations among replicates (r > 0.7), with no apparent outliers, supporting the suitability of all 69 samples for downstream analyses (Supporting Figure S4C).

The protein expression levels of AACT, HMGR, MVD, FPS, and SQS were relatively high, especially in the JS and YJ stages (Supporting Figure S5A). GO and KEGG enrichment analyses were conducted for DEPs from each stage. Within the molecular function category, oxidoreductase activity was consistently the most enriched term at all six stages, underscoring the central role of redox enzymes in secondary metabolism (e.g., ganoderic acid biosynthesis). In the cellular component category, the most significantly enriched terms were extracellular region (JS, KS, CS), membrane, obsolete mitochondrial part, and cell surface (YJ, F0, FR), reflecting dynamic shifts in subcellular localization and suggesting secretion activity and organelle reprogramming across development. For biological processes, the most enriched terms at JS, YJ, KS, CS, F0, and FR were stress granule assembly, cellular ketone metabolic process, small molecule catabolic process, carbohydrate metabolic process, mitochondrial membrane organization, and oxidation–reduction process, respectively, reflecting stage-specific physiological adaptation (Supporting Figure S5B–G).

KEGG enrichment analysis further revealed stage-dependent metabolic features. The most significantly enriched pathways included metabolism of other amino acids (JS), metabolism (YJ, KS, CS), other glycan degradation (F0), and enzymes with EC numbers (FR). Notably, at the KS stage, the biosynthesis of other secondary metabolites was significantly enriched and included multiple genes of interest, such as CYP450s, TFs, and enzymes involved in ganoderic acid biosynthesis (Supporting Figure S5B–G). These findings suggest that the KS stage represents a metabolic turning point, during which secondary metabolite synthesisparticularly ganoderic acidsis highly active, and may serve as an optimal intervention period for enhancing bioactive compound production.

3.5. Mining and Analysis of Transcription Factors and CYP450

To further explore CYP450s and TFs at the transcriptional level, we constructed a WGCNA based on the expression patterns of key genes involved in ganoderic acid biosynthesis and transcriptome data. A soft threshold power of 4 was selected (scale-free R ² ≈ 0.9) to construct the network (Supporting Figure S6A). Dynamic tree cutting and module merging resulted in 15 coexpression modules (Supporting Figure S6B). Module–trait correlation analysis (|r| > 0.7, p < 0.05) revealed several modules significantly associated with biosynthetic pathway genes, including the turquoise module correlated positively with GLHMGR and GLSE; blue with GLMVD and GLMK; yellow with GLAACT, GLMVD, and GLFPP-1; magenta with GLCYP512U6, GLFPP-2, and GLCYP5150L8; and green and pink with GLOSC and GLPMK (Figure A). In total, 22 CYP450s (Supporting Table S2) and 27 TFs (Supporting Table S3) were found in the regulatory network, proposed as candidate regulators of ganoderic acid biosynthesis (Figure B).

4.

Potential key CYP450s and transcription factors identified by WGCNA. (A) Correlation coefficients and significance levels between WGCNA modules and genes involved in the ganoderic acid biosynthetic pathway (red indicates positive correlation; blue indicates negative correlation). (B) Network of hub genes in the modules. (C) Correlation coefficients and significance between modules and proteins involved in the ganoderic acid biosynthetic pathway (red indicates positive correlation; blue indicates negative correlation). (D) Network of hub proteins in the modules. n = 3.

To assess regulation at the protein level, a proteome-based WGCNA was constructed using expression profiles from all 69 samples. A soft threshold power of 1 (scale-free R ² ≈ 0.9) was applied (Supporting Figure S6C), and dynamic clustering identified seven coexpression modules (Supporting Figure S6D). Module–trait correlation analysis (|r| > 0.7, p < 0.05) revealed that the turquoise module correlated with GLMK; the red module with GLAACT, GLPMK, and GLMK; the yellow module with GLSQS, GLIDI, GLAACT, GLPMK, and GLMK; the blue module with GLSQS, GLOSC, GLIDI, GLAACT, GLPMK, and GLMK; and the green module with GLOSC (Figure C). Additionally, there were 25 CYP450s (Supporting Table S4) and 26 TFs (Supporting Table S5) also recognized as candidate regulators (Figure D).

3.6. Identification of Potential Target Genes Associated with Ganoderic Acid Biosynthetic Pathway through Coexpression Network Analysis

To identify transcription factors (TFs) and cytochrome P450 monooxygenases (CYP450s) more precisely involved in ganoderic acid biosynthesis, a coexpression network was constructed based on quantitative correlations between ganoderic acid contents and transcriptomic profiles. A soft-thresholding power of 4 was selected to achieve a scale-free network topology (Figure A), and gene modules were generated by hierarchical clustering (Figure B). After merging highly similar modules, 15 distinct coexpression modules were obtained. Correlations between module eigengenes and structural genes involved in ganoderic acid biosynthesis were subsequently calculated (Figure C), and modules with |r| > 0.7 and p < 0.05 were considered significantly associated with ganoderic acid accumulation.

5.

WGCNA of TMM-normalized gene expression and Ganoderic acid biosynthetic pathway genes. (A) Network topology analysis across a range of soft-thresholding powers used to determine the optimal parameter. (B) Gene dendrogram constructed by average linkage hierarchical clustering, illustrating module assignment. (C) Heatmap of correlation coefficients and significance between identified modules and key ganoderic acid biosynthetic pathway genes (red indicates positive correlation; blue indicates negative correlation). (D) Network of hub genes in the modules. (E) Expression levels of transcription factors in six stages of four G. lucidum strains.

Correlation analysis revealed that the genes in the turquoise module were significantly negatively correlated with ganoderic acids I, A, N, D2, C1, and F; genes in the blue module with ganoderic acids A, N, D2, C1, and F; genes in the red module were positively correlated with ganoderic acids D2; and genes in the brown module were positively correlated with ganoderic acid H. To further identify hub genes associated with ganoderic acid accumulation, coexpression networks of the turquoise (2,798 genes), blue (993 genes), brown (550 genes), and red (334 genes) modules were visualized using Cytoscape (Figure D).

Within the positively correlated modules, 3 CYP450s (CYP5150W10, CYP502A1, and CYP5140A3) and 3 TFs (Zn2Cys6_61, Zf-CCHC-123, and Zf-CCHC-4) were identified in the positively correlated modules. In contrast, 8 CYP450 genes (CYP5150M1, CYP5139A14, CYP505D11, CYP5150W13, CYP5035D1, CYPFOM15A2, CYP5150A14, and CYP53C2) and 8 TFs (Zn ₂ Cys ₆_50, Zn ₂ Cys ₆_73, Zn ₂ Cys ₆_13, Zn ₂ Cys ₆_44, Zn ₂ Cys ₆_39, MYB-12, MADX_box_1, and GATA_8) were identified in the negatively correlated modules.

Previous studies have demonstrated that multiple TF families, including MYB, bHLHs, WRKYs, and ERFs, are involved in the positive regulation of natural product biosynthesis, such as anthocyanins, artemisinin, and tanshinone. − In filamentous fungi, Zn₂Cys₆ transcription factors are well established as central regulators of secondary metabolism. − In the present study, the expression levels of Zn ₂ Cys ₆ _61 were markedly higher than those of other TFs across six developmental stages in four G. lucidum strains (Figure E). The stable and relatively high expression of Zn₂Cys₆_61 across multiple strains and developmental stages further highlights its potential as a robust and broadly applicable target for metabolic engineering aimed at improving GA production. Moreover, Zn ₂ Cys ₆ _61 was also identified as a key candidate TF through WGCNA (Supporting Table S3). Therefore, Zn ₂ Cys ₆ _61 was selected for further investigation to elucidate its potential regulatory roles in ganoderic acid biosynthesis.

The integrative multiomics network analysis identified several CYP450 candidates potentially involved in ganoderic acid biosynthesis, including CYP512U6, CYP512A13, CYP512A3, and CYP5150L8, which has been previously reported. ,,, While these findings support the robustness of our integrative approach, the precise catalytic roles of these CYP450s remain to be elucidated. Gene mining and pathway analysis have not only improved the discovery of bioactive compounds in medicinal plants and mushrooms, but also facilitated the enhancement of their production through biosynthetic approaches.

3.7. Zn ₂ Cys ₆ _61 Is Involved in the Biosynthesis of Ganoderic Acids in G. lucidum

Based on the multiomics-integrated analysis, Zn ₂ Cys ₆ _61 was selected for functional validation in GA biosynthesis. At the JS stage, overexpression of GlZn ₂ Cys ₆ _61 led to a 19.8, 21.7, and 32.4% increase in total triterpenoid content in 3 independent transformants (Figure A). In contrast, silencing of GlZn₂ Cys ₆_61 reduced total triterpenoid levels by 19.5, 21.3, and 17.2% compared with the WT (Figure B).

6.

Positive regulatory effects of Zn ₂ Cys ₆ _61 on ganoderic acid biosynthesis in the JS stage. (A) Total triterpenoid content in OE-GlZn₂Cys₆_61 transformants. (B) Total triterpenoid content in Si-GlZn ₂ Cys ₆ _61 transformants. (C–F) Quantitative RT-PCR analysis of Zn ₂ Cys ₆ _61, SQS, LS, and HMGR expression in OE-GlZn ₂ Cys ₆ _61 transformants. (G-J) Quantitative RT-PCR analysis of Zn ₂ Cys ₆ _61, SQS, LS, and HMGR expression in Si-GlZn ₂ Cys ₆ _61 transformants. (K) Subcellular localization of GlZn2Cys6_61 protein in G. lucidum. DAPI, nuclear staining; EGFP, the field of view under green excitation fluorescence; Merge: the overlapping field of view of DAPI and EGFP. (L) Transcription activation activity of Zn ₂ Cys ₆ _61. (M) Dual-luciferase transient assay in Nicotiana benthamiana. Data are presented as means ± SD (n = 3). Unpaired two-tailed Student’s t tests were performed (p < 0.05).

To evaluate whether overexpression or silencing of Zn ₂ Cys ₆ _61 affects vegetative growth, mycelial dry weight was measured for the WT, Zn ₂ Cys ₆ _61 overexpression, and RNAi strains under identical liquid culture conditions. We found that no significant differences in mycelial dry biomass were observed among the WT, OE, and RNAi strains. These results indicate that modulation of Zn ₂ Cys ₆ _61 expression does not have a detectable impact on mycelial growth under the conditions tested (Supporting Figure S7A).

To further investigate the underlying regulatory mechanisms, the expression levels of three key structural genes in the GA biosynthesis pathway (HMGR, SQS, and LS) were quantified. In OE-GlZn₂Cys₆_61 transformants, SQS was significantly upregulated (Figure C–F). In the Si-GlZn₂Cys₆_61 transformants, the expression levels of HMGR and SQS decreased significantly (Figure G–J). These results indicate that Zn₂Cys₆_61 positively regulates GA biosynthesis, likely through the modulation of key pathway genes.

To determine the subcellular localization of Zn₂Cys₆_61, the coding sequences were fused in-frame with GFP and transiently expressed in Nicotiana benthamiana cells. Fluorescence signals were observed by using confocal laser scanning microscopy. The GFP signals of fusion proteins were predominantly localized in the nucleus (Figure K), indicating that Zn₂Cys₆_61 functions as a nuclear-localized transcription factor.

qRT-PCR analysis further showed that overexpression of GlZn ₂ Cys ₆ _61 significantly increased the SQS transcript abundance. The promoter sequence of SQS is provided in Appendix B. To determine whether Zn2Cys6_61 directly regulates SQS transcription, Y1H assays were performed. The minimum AbA concentration required to inhibit the growth of pSQS-AbAi was 500 ng/mL (Supporting Figure S7B). Yeast one-hybrid (Y1H) assays demonstrated that GlZn₂Cys₆_61 specifically binds to the promoter region of SQS (Figure L). Consistently, dual-luciferase reporter assays showed that the expression of GlZn₂Cys₆_61 significantly enhanced luciferase activity driven by the SQS promoter compared with the empty vector control (Figure M), confirming its role in direct transcriptional activation of SQS.

SQS represents a key metabolic branch point that commits carbon flux from sterol biosynthesis toward triterpenoid formation. In this study, the regulatory relationship between Zn₂Cys₆_61 and SQS was established primarily at the transcriptional level, as supported by promoter-binding assays (Y1H and EMSA), dual-luciferase activation, and consistent changes in SQS transcript abundance following genetic manipulation of Zn₂Cys₆_61. These transcriptional effects were further reflected in altered total triterpenoid and ganoderic acid accumulation, indicating a functional impact on the pathway flux. Direct quantification of pathway intermediates, such as squalene, would provide additional insight into the metabolic consequences of SQS regulation and help refine the relationship between transcriptional control and metabolite-level flux. However, intermediate metabolite pools are often subject to rapid turnover and complex regulation, particularly in stage-dependent secondary metabolic pathways, such as ganoderic acid biosynthesis. Future studies integrating targeted metabolite profiling with stage-resolved genetic manipulation will be valuable for dissecting how Zn₂Cys₆_61-mediated SQS regulation is translated into dynamic changes in squalene availability and downstream triterpenoid diversification.

Furthermore, electrophoretic mobility shift assay (EMSA) analysis revealed direct binding of GlZn₂Cys₆_61 to the biotin-labeled SQS promoter fragment in vitro (Supporting Figure S7C).

By direct targeting and activation of the promoter of the rate-limiting enzyme gene SQS, the transcription factor Zn₂Cys₆_61 was demonstrated to effectively promote ganoderic acid biosynthesis. This regulatory mode is consistent with previous reports on the bHLH family transcription factor GlbHLH7, which also enhances GA accumulation through activation of the SQS promoter, underscoring the central role of SQS as a key control point in the triterpenoid biosynthetic network of G. lucidum.

As a member of the fungus-specific Zn₂Cys₆ family, Zn₂Cys₆_61 is likely embedded within the intrinsic regulatory circuitry governing secondary metabolism in G. lucidum. Further multiomics analysis revealed that the expression of Zn₂Cys₆_61 is not only highly synchronized with SQS but also significantly correlated with the upstream mevalonate pathway gene HMGR and several downstream CYP450 oxidase genes. This suggests that Zn₂Cys₆_61 may function as a more upstream regulatory node with the capacity to coordinate a broader range of processes from pathway initiation to product modification. However, unlike plant bHLH or MYB transcription factors, Zn₂Cys₆ proteins exhibit considerable diversity, and no universally conserved DNA-binding motif has been clearly defined for this family to date. − This complexity poses challenges for precise motif-level mapping of the Zn₂Cys₆–DNA interactions.

Notably, our EMSA results confirmed that the functional binding site of Zn₂Cys₆_61 is located within a defined 200-bp region of the SQS promoter, providing direct biochemical evidence for its promoter association. The sequence of the defined 200-bp region of the SQS promoter is provided in Appendix B. In addition, multiomics correlation analyses revealed that Zn₂Cys₆_61 expression is highly coordinated not only with SQS, but also with the upstream mevalonate pathway gene HMGR and several downstream CYP450 oxidase genes, suggesting that Zn₂Cys₆_61 may function as an upstream regulatory node capable of coordinating precursor supply and downstream oxidative modification.

To further investigate the role of Zn2Cys6_61 in GA biosynthesis at the YJ stage, GA contents were measured in Si-GlZn ₂ Cys ₆ _61 transformants. Compared with the WT strain, the levels of most GAs showed a significant decreasing trend in the silenced lines. In particular, GA-C2, C6, G, B, D2, C1, and F exhibited pronounced reductions (p < 0.0001), while GA-I, A, and H were also markedly decreased (Figure ). Consistent with these metabolic changes, comparative transcriptomic analysis at the YJ stage revealed that the expression levels of key genes involved in the ganoderic acid biosynthetic pathway were significantly downregulated in the Si-GlZn ₂ Cys ₆ _61 strain relative to the WT, further supporting the positive regulatory role of Zn₂Cys₆_61 in GA biosynthesis (Supporting Figure S7D). Collectively, these results demonstrate that Zn₂Cys₆_61 is a key positive regulator of GA biosynthesis.

7.

Content of individual ganoderic acids in Si-GlZn ₂ Cys ₆ _61 transformants in the YJ stage.

The magnitude of transcriptional changes observed for transcription factors does not necessarily scale linearly with their regulatory impact on downstream metabolic pathways. In many cases, relatively modest changes in transcription factor abundance can lead to pronounced downstream effects, particularly when the regulator functions as an upstream node within a tightly controlled biosynthetic network. In the present study, although the transcriptional upregulation of Zn₂Cys₆_61 in overexpression lines was moderate, it consistently resulted in significant activation of the SQS promoter, elevated SQS transcript levels, and reproducible increases in total triterpenoid and GA accumulation across independent transformants.

It is well recognized that transcript abundance does not always correlate linearly with protein levels due to post-transcriptional regulation, differential translation efficiency, and protein turnover. In this study, Zn₂Cys₆_61 was identified as a transcriptional regulator that primarily exerts its effect at the level of gene expression initiation. This is supported by direct binding of Zn₂Cys₆_61 to the SQS promoter, transcriptional activation in dual-luciferase assays, and consistent changes in SQS transcript levels following the genetic manipulation of Zn₂Cys₆_61. Although discrepancies between transcript and protein abundances were observed for certain pathway enzymes, the regulatory influence of Zn₂Cys₆_61 is evident from coordinated trends across multiple molecular layers. Multiomics correlation analyses revealed that Zn₂Cys₆_61 expression is synchronized with key upstream (HMGR) and downstream (CYP450) components of the ganoderic acid biosynthetic pathway. Importantly, these molecular changes ultimately converged at the metabolic level, as reflected by consistent alterations in total triterpenoid and ganoderic acid accumulation in Zn₂Cys₆_61 overexpression and silencing strains.

In this study, functional validation of Zn₂Cys₆_61 was primarily conducted at the JS and YJ developmental stages. The JS stage was selected because it represents a relatively homogeneous and experimentally tractable phase, enabling efficient genetic manipulation and early assessment of the transcription factor function. At this stage, measurement of total triterpenoid content provides an integrated indicator of pathway activity, encompassing triterpenoid precursors and intermediates, even though representative ganoderic acids are not yet detectable. , We acknowledge that the regulatory role of Zn₂Cys₆_61 at later developmental stages, including KS, CS, F0, and FR, was not examined in the present study. Ganoderic acid biosynthesis is highly stage-dependent and involves complex coordination between upstream precursor supply, downstream oxidative modification, and developmental cues. Dissecting the stage-specific regulatory dynamics of Zn₂Cys₆_61 and its effects on individual ganoderic acid monomers will require systematic analyses across fruiting body development and represents an important direction for future research.

Despite these limitations, the current work integrates life-cycle-resolved multiomics analyses with genetic manipulation and direct promoter-binding evidence, providing a robust framework for identifying transcriptional regulators of secondary metabolism in medicinal fungi. The identification of Zn₂Cys₆_61 as a positive regulator of ganoderic acid biosynthesis establishes a solid foundation for subsequent stage-resolved metabolic engineering and compound-specific optimization strategies.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jafc.5c17368.

• Results of nontargeted identification of anions in the JS, YJ, KS, CS, F0, and FR developmental stages (Appendix A); SQS promoter sequence and 200-bp region of the SQS promoter that contain functional binding site of Zn₂Cys_{6_}61 (Appendix B); Figures S1–S7; and Tables S1–S10 (PDF)

∇.

Y.L. and L.L. contributed equally to this work. Y.L.: Conceptualization; methodology; validation; formal analysis; investigation; data curation; writingoriginal draft preparation; writingreview and editing; visualization; funding acquisition. L.L.: Conceptualization; methodology; software; validation; formal analysis; investigation; data curation; visualization. M.L., J.X. and J.Y.: Validation; formal analysis; investigation. X.H. and R.Z.: Software. W.Y., W.L., L.M., H.Y., S.Z., Y.S. and Y.W.: Validation. Z.Y. and Z. Liang: Supervision, funding acquisition. Z. Li: Resources, supervision, funding acquisition. D.Y.: Writingreview and editing; supervision; project administration; funding acquisition.

The authors declare no competing financial interest.