Introduction

A. bisporus is among the most popular and common edible mushrooms and has been widely cultivated worldwide, given its excellent functional properties. A. bisporus is characterized by good texture, high nutritional value, and unique flavor. Compared with other vegetables, it has higher protein content and lower fat content1,2. Most importantly, it is widely thought that they have certain medicinal values3. A. bisporus has been cultivated for over 300 years4. After a relatively long period of artificial culture, A. bisporus has been subject to artificial selection to a certain extent and has become different from wild A. bisporus. It is well-established that there are differences in flavor between cultivated and wild A. bisporus primarily due to their unique chemical composition5. In addition, the protein, dietary fiber, vitamins, minerals, and amino acid composition between cultivated and wild mushrooms may differ. Nowadays, the majority of globally produced A. bisporus mushrooms are from cultivated sources. The challenge we face is how to cultivate mushrooms with superior flavor and higher nutritional value. Therefore, revealing the metabolites and their molecular mechanisms of cultivated and wild A. bisporus mushrooms is crucial for understanding their cultivation and enhancing the mushroom industry.

Next-generation sequencing (NGS) technology is extensively used in life sciences for genome sequencing, transcriptome sequencing, and metagenomics sequencing6,7. Comparative transcriptome analysis is an effective way to compare gene expression patterns between different subjects and to provide insights into biological processes. By analyzing the sequencing data, we can screen the key differentially expressed genes and the biological processes involved and then reveal the molecular differences between wild and cultivated A. bisporus. Many transcriptome studies have been performed for mushroom species, including Lentinus edodes89, Ganoderma lucidum10, Agrocybe aegerita11, Auricularia polytricha12, Pleurotus eryngii subsp. Tuoliensis13 and Cordyceps militaris14. Transcriptome analysis is an effective tool that can reveal the differences between cultivated and wild A. bisporus. Detection of mushroom metabolites can reveal changes in their main flavor compounds. In addition, organic acids are important compounds that affect the accumulation of flavor substances in mushrooms. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has proven to be an efficient and powerful tool for studying these products15.

Here, we present the first combined transcriptomic and metabolomic investigation of wild and cultivated A. bisporus. By analyzing 43 strains (23 wild, 20 cultivated) via RNA-seq and LC-MS/MS, we: Identify key differentially expressed genes (DEGs) and metabolites linked to flavor divergence; Construct gene-metabolite correlation networks to reveal regulatory hubs; Uncover cultivation-specific metabolic shifts, including upregulated methionine permease genes (AGABI2DRAFT_188981/191000) and altered organic acid profiles. This study not only delineates the molecular basis of flavor differentiation but also provides actionable targets for precision breeding, addressing a critical bottleneck in sustainable mushroom industry development.

Materials and methods

Test mushroom strains and culture conditions for fruiting

A total of 43 A. bisporus strains, divided into 23 wild strains (abbreviated as W) and 20 cultivated strains (abbreviated as C), were tested. It mainly includes the wild ARP strains in the United States, the wild strains in China and the traditional cultivated strains in Europe and the United States. The strain was stored and provided by the Institute of Edible Mushroom, Fujian Academy of Agricultural Sciences, Fujian China. The strain number and source are shown in Table S1.

The tested strains were cultivated in the artificial climate mushroom room in institute of edible mushroom, fujian academy of agricultural sciences. A plastic basket with a growing area of 40 cm×50 cm was used for plot cultivation which 17.0 kg of wet compost was filled. 200 g of wheat spawn was used in total per strain. The humidity of mushroom house was kept at 70–80%, and the temperature of culture material was controlled to 24 ± 1℃. In the stage of mushroom emergence, the humidity is controlled at 85 ~ 90%, and the temperature of the mushroom house is controlled at 16 ~ 18℃. Control ventilation time and ventilation volume. The covering soil thickness is 4 cm. Fresh fruiting bodies could be obtained after 40 days of sowing. In the first tide, the fruiting bodies with strong growth were selected to observe the color and morphological characteristics of fruiting bodies. Part of the test strains fruiting scene is shown in Fig. 1. Fresh fruiting body samples (10 g) were cut and quickly frozen in liquid nitrogen at -196℃ for further analysis. Three biological replicates were performed for each test strain, and subsequent assay experiments were carried out.

Fig. 1
figure 1

Part of the test strains fruiting scene. A1, B1, C1 and D1 show test strains of Ag2K811, AgLH830, 02 − 1 and A15 fruiting in plastic basket. A2, B2, C2 and D2 show its longitudinal section of fruiting body.

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Mushroom total protein and amino acid content determination

The protein content in samples was determined by Kjeldahl nitrogen determination method (Kjeltec 8400 Kjeldahl Nitrogen Analyzer, Foss Company) and the amino acid content was determined by acid hydrolyzation-automatic amino acid analyzer (Hitachi L-8900 High-Speed Amino Acid Analyzer, Japan) using the method described by Jie et al.16.

RNA Sequencing and analysis

RNA sequencing with total RNA extracted from the fruiting bodies was performed as described previously17. Briefly, total RNA was extracted from the mushroom fruiting bodies using the RNeasy Plant Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. Then the extracted RNAs were further treated with DNase I. The DNase-I-treated RNA samples were purified using an RNeasy column (Qiagen, Hilden, Germany). The RNA quality and quantity were analyzed by UV spectrophotometry and gel electrophoresis. The cDNA library was constructed using a TruSeq RNA sample prep kit v2 (Illumina, San Diego, CA, USA), and RNA sequencing with an Illumine HiSeq 4000 sequencing system (Illumina, San Diego, CA, USA) was performed by Biomics (Beijing) Biotech Co., Ltd (Beijing, China). The raw data obtained by sequencing was filtered to obtain clean data using Trimmomatic (v.0.33) with the default parameters18. Then, we used HISAT2 (v.2.10)19 to map the clean reads to the reference genome (Agaricus bisporus var. bisporus H97, NCBI BioProject: PRJNA61005), which exhibited chromosome-level assembly20 and employed StringTie (v.1.3.4) to calculate each gene’s FPKM value21. All genes were annotated using local BLASTX programs against the Nr, SwissPort, GO and PFAM databases. The RNA-Seq data were transformed into Gene Ontology (GO, www.geneontology.org/) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG, www.genome.jp/kegg/) analysis. DESeq2 software (Differential expression analysis for sequence count data software, 1.10.1)22 was used to identify differential gene expression, and the negative binomial distribution pvalue model was calculated using the standardized method of DESeq2, and the identifying standard of differential gene expression was P < 0.05.

Metabolome sequencing and analysis

A total of 100 mg fruiting body samples were collected and powered by an automated grinder (JXFSTPRP-24/32, Shanghai Netcom, China; at 60 Hz for2 min) and ultrasonic cleaner (SB-5200DT, Ningbo Scientz Biotechnology Co., Ltd, Ningbo, China; for 30 min). Chloroform (200 µL) was added for lipid extraction, followed by 30-min sonication. After that, the samples were centrifuged at 12000 rpm for 10 minutes at 4℃ and dried in a vacuum centrifuge concentrator (LNG-T98, Huamei Biochemistry Instrument, Taicang, China). Then, 1 ml 50% pre-cooled methyl alcohol and 20 µL L-2-chloro-phenylalanine were used for sample extraction, followed by cooling at -20°C for 2 min and grinding (at 60 Hz for 2 min). Homogenates were centrifuged, supernatants were collected, dried, and dissolved into 50% pre-cooled methyl alcohol, vortex oscillated for 60 s, ultrasonic-treated for 30 s, and finally centrifuged (at 12000 rpm for 10 min at 4℃) and dried. All samples were subjected to treatments with methoxamine hydrochloride pyridine under rotation (2 min) and sonication (at 37°C for 90 min)23. System stability and accuracy were validated using QC samples at an interval of 5 samples24. MS raw data (total ion current, TIC) was converted into available file format using ChemStation (version E.02.02.1431, Agilent Technologies Inc). ChromaTOF (version 4.34, LECO, St Joseph, MI) was used to analyze the data, and NIST and Fiehn databases were used to annotate the metabolites. After alignment with the Statistic Compare component, the ‘raw data array’ (.cvs) was obtained from raw data, including peak names, retention time-m/z and peak intensities. All internal standards and pseudo-positive peaks were removed25. Data were log2 transformed and then imported into the SIMCA (Standard isolinear method of class assignment) software package (14.0, Umetrics, Umeå, Sweden,https://www.sartorius.com/en/products/process-analytical-technology/data-analytics-software/mvda-software/simca). Unweighted principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA, with 7-fold cross-validation and response permutation testing, 200 times randomly permutated) were performed to visualize the metabolism difference between groups26. Metabolites with variable important in projection (VIP) > 1 and p-value < 0.05 by two-tailed Student’s t-test were used to identify differential metabolites. Metabolites between groups with |fold change (FC)| ≥ 1 were considered differential metabolites. The KEGG pathways associated with the differential metabolites were identified from the KEGG database (http://www.genome.jp/KEGG/pathway.html) with a threshold of corrected p < 0.0527,28. Redundancy analysis (RDA) using CANOCO (version 5.0, Biometris, Netherland).

Quantitative real-time PCR (qRT-PCR)

The samples were ground with liquid nitrogen, total RNA was extracted with a Magen total RNA extraction kit (R4151-02, Magen, Guangzhou, China), and cDNA was synthesized from 1 mg of total RNA via cDNA synthesis supermix (CAT: 11141ES60, Yeasen, Shanghai, China) according to the manufacturer’s instructions. qRT‒PCR analysis was performed with three biological replicates and three technical replicates with Hieff™ qPCR SYBR Green Master Mix (Low Rox Plus, part number 11202ES08, Yeasen, Shanghai, China) on a QuantStudio 6 Flex PCR system (ABI). All amplification consisted of denaturing for 10 s at 95 °C, followed by 40 cycles of 5 s each at 95 °C and 30 s at primer-specific annealing temperature. The specificity of each RT-qPCR reaction was tested using a dissociation curve (gradient from 60 °C to 95 °C). The sequences of the reference β-actin gene29 and gene-specific primer pairs and their amplicon sizes are shown in Table S5. For the analysis of the qRT-PCR output, the 2−ΔΔCT method of relative quantification was used30. The data are shown as the means ± standard deviations (SDs) of six independent experiments.

Results

Mushroom total protein and amino acids

We initially analyzed the total protein content of all mushroom samples. Amino acid and protein content, as indicated by A325 and A319, were higher in the C group, whereas A409 and A408 showed higher levels in the W group. Overall protein content levels between the C and W groups did not exhibit significant differences (Fig. 2).

Fig. 2
figure 2

Total protein and amino acid content. Total protein and amino acid content are displayed with heatmap. The redder the color, the higher the content of the metabolite, and the bluer the color, the lower the content of the metabolite. The bottom row of the heat map represents total protein content. W, The wild-type strain; C, the artificially cultivated mushroom strain.

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Sequence data summary

Following quality control of the raw sequencing data, the percentage of Q20 and Q30 reads was 97.05% and 92.28%, respectively (S2 Table), indicating the high quality of sequencing data. The sequence alignment of the clean reads to the reference genome indicated that overall mapping rates in most samples ranged between 46.53% and 78.82%. The concordant pair alignment rate was 50–65% in most samples (Table S2). The percentage of reads with unique mapping sites in each sample was as high as 99%.

Identification of DEGs

A total of 103 DEGs were identified between wild-type and cultivated A. bisporus, with 45 and 58 DEGs highly expressed in W and C groups, respectively (Fig. 3A). The expression of these DEGs is shown in Fig. 3B. Among the DEGs, AGABI2DRAFT_133726 (gamma-glutamylcyclotransferase, GGCT) and AGABI2DRAFT_134468 (hypothetical protein AN958_09571) were the top two DEGs highly expressed in cultivated A. bisporus; AGABI2DRAFT_62224 (protein disulfide-isomerase A1, PDIA1) and AGABI2DRAFT_68765 (H+ antiporter, TC-CPA1) were the top two DEGs highly expressed in wild-type A. bisporus (Table S3).

Fig. 3
figure 3

Identification of differentially expressed genes (DEGs). (A) Volcano map of differentially expressed genes. Significantly differentially expressed genes are shown as a red (up) or green (down) dot. (B) Heatmap of DEGs. The red and green colors represent up-regulation and down-regulation, respectively. (C) 16 candidate DEGs that enriched in GO terms. The red and blue colors represent up-regulation and down-regulation, respectively. (D) Candidate DEGs that enriched in KEGG pathways. The red and blue colors represent up-regulation and down-regulation, respectively. W, The wild-type strain; C, the artificially cultivated mushroom strain.

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In addition, GO and KEGG enrichment analyses of the 103 DEGs were conducted. Significant enrichment in 7 key GO terms was found (Fig. S1A), including cation transport (GO:0006812), amino acid transmembrane transport (GO:0003333), carbohydrate metabolic process (GO:0005975), proteolysis (GO:0006508), metabolic process (GO:0008152), transmembrane transport (GO:0055085), oxidation-reduction process (GO:0055114). There are 16 DEGs were enriched in these GO terms (Fig. 3C). KEGG pathway analysis results (Fig. S1B) showed that the Phosphatidylinositol signaling system (ko04070), Inositol phosphate metabolism (ko00562), Fructose and mannose metabolism (ko00051) and Homologous recombination (ko03440) were significantly enriched. DEGs, including AGABI2DRAFT_139182, AGABI2DRAFT_191352, AGABI2DRAFT_203654, AGABI2DRAFT_188981, AGABI2DRAFT_192972 and AGABI2DRAFT_191000 were involved in these pathways (Fig. 3D).

Identification of differential metabolites

There were 44 differential metabolites between wild-type and cultivated A. bisporus (Fig. 4A, Table S4). A comparison of the levels of differential metabolites between W and C is shown in Fig. 4B. The top 10 differential metabolites according to the content level were fumaric acid, 2-hydroxybutanoic acid, isoleucine, phenylalanine 1, uridine 2, proline, alpha-ketoglutaric acid, Valine, O-phosphonothreonine 1 and palmitic acid.

Fig. 4
figure 4

Identification of differential metabolites. (A) Volcano map of differential metabolites. Significantly differentially expressed genes are shown as a red (up) or green (down) dot. (B) Heatmap of differential metabolites. The red and blue colors represent up-regulation and down-regulation, respectively. (C) Five candidate DEGs that enriched in KEGG pathways. The red and blue colors represent up-regulation and down-regulation, respectively. W, The wild-type strain; C, the artificially cultivated mushroom strain.

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During KEGG enrichment analysis (Fig S2), Citrate cycle (TCA cycle) (ath00020), Alanine, aspartate and glutamate metabolism (ath00250), Aminoacyl-tRNA biosynthesis (ath00970), Valine, leucine and isoleucine biosynthesis (ath00290), and Valine, leucine and isoleucine degradation (ath00280) were significantly enriched. Five differential metabolites participated in these pathways, including alpha-ketoglutaric acid, Valine, fumaric acid, isoleucine and proline (Fig. 4C). The level of fumaric acid was the highest among all differential metabolites.

Combined analysis of metabolome and transcriptome

Redundancy analysis (RDA) using CANOCO showed that AGABI2DRAFT_181624 and O-phosphonothreonine 1 were positively correlated, and AGABI2DRAFT_212894 was associated with proline, uridine2, and phenylalanine1. Moreover, AGABI2DRAFT_191352 was positively connected with 2-hydroxybutanoic acid. The results were showed in Fig. 5.

Fig. 5
figure 5

Redundancy analysis (RDA) of the differentially expressed genes and differential metabolites. W, The wild-type strain; C, the artificially cultivated mushroom strain.

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RT-qPCR verification results

In order to validate the differently expressed AGABI2DRAFT_181624, AGABI2DRAFT_212894 and AGABI2DRAFT_191352 in our RNA-seq data, we performed RT-qPCR analyses for expression of these genes. The expression trends of these three genes were similar to those of the transcriptome data (Fig. 6).

Fig. 6
figure 6

Quantitative analysis of gene expression. All qRT-PCR data are presented as mean ± SD. W, the wild-type strain; C, the artificially cultivated mushroom strain. Bars represents 2-DeltaDelta Ct value calculated by Delta-Delta Ct method.

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Discussion

The rising consumer demand for edible mushrooms coupled with heightened food safety awareness has positioned cultivated A. bisporus as a predominant choice in the market. However, artificial cultivation induces pronounced divergence between cultivated and wild strains, particularly in gene expression and metabolic profiles. While amino acids and proteins are traditionally considered pivotal drivers of mushroom flavor31, this study revealed no statistically significant associations between these macromolecules and strain-specific flavor differentiation. This discrepancy may stem from the subtle flavor variations observed between cultivated and wild A. bisporus, implying that secondary metabolites likely exert a more pronounced influence on flavor nuances.

Integrated transcriptomic and metabolomic analyses identified key differentially expressed genes (DEGs), notably AGABI2DRAFT_188981 and AGABI2DRAFT_191000, which encode high-affinity methionine permease (MUP1) and were upregulated in cultivated strains. Methionine, a sulfur-containing amino acid, is a critical precursor for flavor compounds in mushrooms32. MUP1-mediated regulation of methionine uptake and metabolism may indirectly modulate flavor by altering the production of sulfur-derived metabolites (e.g., sulfides and thiols), which are known contributors to mushroom flavor profiles. Although MUP1 itself is not a direct flavor determinant, its metabolic role highlights a potential regulatory node linking cultivation-induced genetic changes to flavor diversification.

These findings advance our understanding of how cultivation practices reshape the flavor landscape of A. bisporus and provide actionable targets for precision breeding of flavor-enhanced cultivars, thereby supporting sustainable development in the mushroom industry.

Functional characterization identified AGABI2DRAFT_191352 as a critical gene encoding 6-phosphofructokinase (PFK, EC 2.7.1.11), the rate-limiting enzyme in glycolysis responsible for generating phosphoenolpyruvate (PEP)31. PEP serves as a central precursor for biosynthesis of diverse organic acids33. The elevated expression of AGABI2DRAFT_191352 in wild-type mushrooms correlates with enhanced glycolytic flux, potentially driving higher accumulations of organic acids. Notably, redundancy analysis (RDA) revealed a significant positive correlation (r = 0.82, p < 0.05) between AGABI2DRAFT_191352 expression and 2-hydroxybutanoic acid levels, suggesting PFK-mediated glycolytic activity may directly modulate the synthesis of this flavor-related metabolite. Functional characterization revealed that AGABI2DRAFT_212894 as a critical gene encoding cytochrome P450 enzyme with a broad substrate range, excellent catalytic versatility, and high frequency of participation plays an important role in the biosynthesis of fungal natural products34. It is regulated during fruiting body development and maturation of A. bisporus35. The function of AGABI2DRAFT_181624 is related to transport and catabolism, which provides energy for A. bisporus by participating in the metabolism of carbohydrates such as glucose and galactose, supplying growth and development36.

According to the metabolomics results, fumaric acid, isoleucine, phenylalanine 1 and palmitic acid exhibited higher levels in W than in C, while lower levels of 2-hydroxybutanoic acid, uridine 2, proline, alpha-ketoglutaric acid, and O-phosphonothreonine 1 in W than in C. An increasing body of evidence suggests that organic acids impact the taste and aroma of mushrooms37,38,39. The difference in these organic acids might be the key to flavor differences between cultivated and wild mushrooms. In this study, fumaric acid was the most abundant organic acid in A. bisporus. Fumaric acid exists naturally in bolete mushrooms, Icelandic moss and lichen, and human skin naturally produces the acid when exposed to sunlight40. A synthetic form of fumaric acid is used as a food additive to enhance flavor and sourness41. We found that fumaric acid might account for flavor differences between cultivated and wild mushrooms. In addition, uridine 2 exhibited the most pronounced fold change between wild-type (W) and cultivated (C) strains. Previous studies have identified uridine in P. giganteus mushrooms, where it enhances phosphorylation of extracellular signal-regulated kinases (ERKs) and protein kinase B42, and is hypothesized to mitigate neurodegenerative pathologies such as Alzheimer’s disease (AD) through promoting neurite outgrowth and synaptic plasticity43. Intriguingly, our metabolomic profiling revealed significantly higher uridine abundance in cultivated A. bisporus (3.2-fold increase vs. wild-type, p < 0.01), underscoring its potential as a medicinally valuable metabolite in artificially propagated strains.

The observed metabolic divergence between wild and cultivated A. bisporus likely stems from cultivation-induced metabolic reprogramming, particularly in response to substrate composition and environmental controls (e.g., humidity, temperature). This study elucidates the molecular and metabolic foundations of flavor differentiation and establishes a framework for precision breeding strategies, such as targeted gene editing or metabolic engineering, to enhance both organoleptic and nutraceutical properties in mushroom cultivars.