Selection and validation of internal control genes for quantitative real-time RT‒qPCR normalization of Phlebopus portentosus gene expression under different conditions

Abstract

Phlebopus portentosus (Berk. and Broome) Boedijn is an attractive edible mushroom and is considered the only bolete for which artificial cultivation in vitro has been achieved. Gene expression analysis has become widely used in research on edible fungi and is important for elucidating the functions of genes involved in complex biological processes. Selecting appropriate reference genes is crucial to ensuring reliable RT‒qPCR gene expression analysis results. In our study, a total of 12 candidate control genes were selected from 25 traditional housekeeping genes based on their expression stability in 9 transcriptomes of 3 developmental stages. These genes were further evaluated using geNorm, NormFinder, and RefFinder under different conditions and developmental stages. The results revealed that MSF1 domain-containing protein (MSF1), synaptobrevin (SYB), mitogen-activated protein kinase genes (MAPK), TATA-binding protein 1 (TBP1), and SPRY domain protein (SPRY) were the most stable reference genes in all sample treatments, while elongation factor 1-alpha (EF1), actin and ubiquitin-conjugating enzyme (UBCE) were the most unstably expressed. The gene SYB was selected based on the transcriptome results and was identified as a novel reference gene in P. portentosus. This is the first detailed study on the identification of reference genes in this fungus and may provide new insights into selecting genes and quantifying gene expression.

Introduction

Phlebopus portentosus (Berk. and Broome) Boedijn is an important ectomycorrhizal edible fungus that is widely consumed in some regions of China, Thailand and other tropical countries [1, 2]. This fungus is rich in nutrients [3, 4] and has been successfully artificially cultivated in greenhouses on designed substrates in vitro in China and Thailand. It is the only species with ectomycorrhizal niches in the order Boletales for which industrialized cultivation has been achieved [1, 5, 6], with production of up to 6 tons per day [1]. Successful cultivation of this Boletales species might provide insight into the cultivation of other yet uncultivable fungi or Boletales in the future. Gene expression analyses of fruiting body formation, development and responses to environmental stresses are particularly important to understand the genetic background of this fungus [7], answering scientific questions on the functions of genes involved in complex biological and metabolic processes and the mechanisms of domestication [8–11]. Transcriptomic and reverse transcription quantitative real-time PCR (RT‒qPCR) are the most commonly used techniques to determine the gene expression patterns of fruiting body development and lignocellulose synthesis in mushrooms [7]. However, to date, no related research has explored the key steps affecting the results of RT‒PCR in P. portentosus.

RT‒qPCR is a highly sensitive tool used for the rapid and accurate characterization of gene expression in molecular experiments [9, 12, 13]. However, the accuracy and reliability of gene expression carried out using RT‒qPCR is strongly influenced by many factors, such as the quality of the raw sample material, inhibitor content in the samples, primer specificity, RNA quality and quantity, the efficiency of reverse transcription, and internal reference genes [11, 14, 15]. Among all variables, the use of an optimal reference gene for RT‒qPCR analysis is crucial to ensure accurate normalization, since unsuitable reference genes might cause unreliable and misleading results [16–18]. An ideal reference gene is characterized by a constant expression level across different environmental growing conditions and developmental stages [13, 19, 20]. Traditionally, several traditional and housekeeping genes involved in some basic cellular and molecular processes have been widely used as reference genes, including 18S ribosomal rDNA (18S), 28S ribosomal rDNA (28S), β-actin (ACTB), cyclophilin (CYP), tubulin (TUBα and TUBβ1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and ubiquitin (UBQ) [14, 16, 17, 21–23]. Unfortunately, based on the results of animal and plant studies, no universal reference genes are suitable for all species in various tissues and across different developmental stages and experimental treatments [19, 20]. A similar situation also exists in mushroom studies, e.g., SPRY domain protein (SPRY), alpha-tubulin (TUBα), cyclophilin (CYP), L-asparaginase (L-asp), and MSF1 domain-containing protein (MSF1) were regarded as the most stably expressed genes under different conditions in Volvariella volvacea [16, 21], while UBQ was the most stably expressed in Auricularia cornea [23]. Therefore, it is essential to validate suitable reference genes under different conditions to obtain biologically accurate expression data in a particular species.

There are a large number of published articles on selecting appropriate reference genes in different species. In addition to the two species above (Volvariella volvacea and Auricularia cornea), other studies have been performed to identify and evaluate reference genes in mushrooms, including Agaricus bisporus (J.E. Lange) Imbach [24], Ganoderma lucidum (Curtis) P. Karst. [25], Pleurotus ostreatus (Jacq.) P. Kumm [26], Ophiocordyceps sinensis (Berk.) G.H. Sung, J.M. Sung, Hywel-Jones & Spatafora [27], Wolfiporia cocos (F.A. Wolf) Ryvarden & Gilb [28, 29], Auricularia heimuer F. Wu, B.K. Cui & Y.C. Dai [30], Lepista sordida (Schumach.) Singer [31], Inonotus obliquus [32], Flammulina filiformis (Z.W. Ge, X.B. Liu & Zhu L. Yang) P.M. Wang, Y.C. Dai, E. Horak & Zhu L. Yang [32–34], Macrocybe gigantea (Massee) Pegler & Lodge [35], Armillaria mellea (Vahl) P. Kumm [36], Pleurotus eryngii (DC.) Quéland [37], Tuber melanosporum Vittad [38] and Morchella importuna M. Kuo, O’Donnell & T.J. Volk [39]. All these results revealed that different reference genes are required for different species and different conditions. However, studies of the stability and identification of reference genes have not been carried out in P. portentosus.

It is difficult to determine the optimal reference genes in a species without genomic and transcriptomic information [39–41]. Fortunately, some related datasets have been deposited in the NCBI database [7, 42], which can be screened for suitable reference genes and corresponding primer design for P. portentosus. In this study, some candidate reference genes were preevaluated and selected based on genomic and transcriptomic data. The expression patterns of filtered candidate reference genes under different treatments were further validated. Our results will provide new insights into reference gene selection for high-accuracy RT‒qPCR normalization in P. portentosus gene expression analysis.

Materials and methods

Sampling and culture conditions

The P. portentosus dikaryon strain 17026 used in this study was the same as that used in our previous work [7], which was provided by Hongzhen Agricultural Science and Technology Co. Ltd. and maintained at the Shanghai Academy of Agricultural Science. This strain was incubated on potato dextrose agar (PDA) at 30°C in the dark as previously described [7, 42]. Mycelial samples were grown in 150 mL potato dextrose broth (PDB) for 7 days under selected conditions with shaking at 150 rpm. The addition of NaCl (1%), CuSO4.5H₂O (1%), H₂O₂ (100 μM), HCl (pH 4.0), and NaOH (pH 9.0) created conditions of salt, heavy metal, oxidation, acid and alkali stresses at the optimal growing temperature of 30°C for this fungus. Incubation at 25°C and 35°C created cold and heat conditions, respectively. The artificial cultivation of P. portentosus was carried out in line with the methods published in our previous study [7]. The entire fruiting body was harvested, and the cap and stipe were chopped into small pieces (2 mm). All samples were immediately frozen in liquid nitrogen and stored at -80°C before RNA extraction. Three independent biological replicates were tested for each treatment.

RNA isolation and cDNA synthesis

All the samples were ground into a fine powder in a frozen mortar while immersed in liquid nitrogen. Total RNA was extracted from 100 mg of sample powder using TRIzol (Invitrogen) and DNase I (Ambion, USA) according to the manufacturer’s protocols. RNA concentration and purity were assessed using a NanoDrop 2000 Spectrophotometer (NanoDrop Technologies, Thermo Scientific, USA). Only RNA preparations with absorption ratio values of A260/280 and A260/A230 that were 1.8–2.0 and >2.0, respectively, were used for subsequent analysis. cDNA was synthesized using the PrimeScript™ RT reagent Kit with gDNA Eraser (Perfect Real Time) (TaKaRa Bio Inc., Dalian, China) from 1 μg total RNA in a final volume of 20 μL according to the provided protocol. The cDNA obtained was diluted 10-fold using nuclease-free water for further RT‒qPCR amplifications.

Selection and validation of candidate reference genes and primer design

Based on previous literature, a total of 25 housekeeping genes [14, 16, 17, 21–24, 26–30, 33–35, 37–39, 43], namely, 60S ribosomal protein (60S), Ras protein (RAS), actin 2 (ACTIN), adenine phosphoribosyl transferase (APT), cyclophilin (CYP), 18S ribosomal rDNA (18S rRNA), RNA polymerase subunit 2 (POL II), RNA polymerase subunit 3 (POL III), elongation factor 1-alpha (EF1), elongation factor 2-alpha (EF2), eukaryotic initiation factor 4A (EIF), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), GTP-binding nuclear protein (RAN), ribosomal protein S (RPS), s-adenosyl methionine decarboxylase (SAMDC), TATA binding protein 1 (TBP1), alpha-tubulin (TUBα), beta-Tubulin (TUBβ), ubiquitin-conjugating enzyme (UBCE), ubiquitin (UBQ), polyubiquitin (POL), MSF1-domain-containing protein (MSF1), SPRY domain protein (SPRY), L-asparaginase (ASP), and mitogen-activated protein kinase genes (MAPK), were selected. All candidate genes were preevaluated using transcriptome datasets from our previous study on the developmental stages of P. portentosus [7]. Gene expression was normalized using the fragments per kilobase of transcript million mapped reads (FPKM)>1 (FPKM) method [21, 43–47]. The average expression (mean) and the standard deviation (SD) were calculated per gene over the entire set of transcriptome data. Genes with low CV values (SD/mean) were considered to have higher expression stability than genes with high CV values. The cutoff CV value for stable gene expression was often set as <0.3, in line with previously published studies [46, 48]. The primer pairs were designed using Blast-Primer (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) based on the following criteria: amplified products ranging from 100–150 bp, Tm (melting temperature) ranging from 55 to 60°C, GC content ranging from 15% to 55%, and primer length ranging from 20 to 25. The PCR efficiency was calculated using 10-fold serial dilutions of cDNA.

RT‒qPCR and data analysis

RT‒qPCR was carried out on an Applied Biosystems 7500 Real-Time PCR system, and samples were run in triplicate. Each PCR mixture was prepared in a final volume of 20 μL containing 2 μl of prepared cDNA template (10-fold diluted template), 0.4 μl of each primer (10 nM, reverse and forward primers), 6.8 μl of ddH₂O and 10 μl of Power SYBR Green qPCR Master Mix (TaKaRa). Each sample and no-template control were run in three technical replicates. The running conditions and parameters used were as follows: initial denaturation at 95°C for 5 min, followed by 40 cycles of 95°C for 15 s and 60°C for 1 min. A temperature ramp step was used for dissociation analysis to confirm the single product with heating from 60°C to 95°C in steps of 0.5°C every 10 s.

To analyze the expression stability of candidate reference genes, three different programs, geNorm [49], NormFinder [50], and BestKeeper [51], were used based on the experimental design and manufacturers’ instructions. The detailed analysis methodology followed previous methods [16].

Results

Preevaluation of candidate reference genes based on transcriptome data

To identify stably expressed genes in P. portentosus, a total of 9 transcriptomes collected from 3 developmental stages, namely, mycelium, primordium and fruiting body, were used. Based on the CV value of gene expression (RPKM) and the cutoff for stably expressed genes (CV < 0.3) between different stages [46], 952 genes were stably expressed in all the samples. Based on the annotation of genes, synaptobrevin (SYB) was the most stable in the three stages, with CV = 0.034. Among the 25 candidate genes above, 11 genes were stably expressed across developmental stages, with CV values ranging from 0.15 to 0.30 (Fig 1), including MSF1, RAN, ACTIN, UBCE, MAPK, SPRY, EF1, EF2, CYP, TBP1 and RP. Some studies have accepted genes with CV < 0.35 as reference candidates [27]. Finally, 12 candidate genes and SYB were used for RT‒PCR validation.

[Figure omitted. See PDF.]

Specificity of amplification and PCR efficiency

Information on the primer pairs is listed in Table 1. To confirm the specificity of the primers, 2% agarose gel electrophoresis was used. A single band with the expected size, no primer dimers and single-peak melting curves of the PCR products from each primer pair on cDNA samples were considered to be high specificity (S1 Raw images and S1 Fig). The PCR efficiency ranged from 90–110%.

[Figure omitted. See PDF.]

Reference gene candidate expression profiles

To evaluate the expression levels of the 12 selected reference genes from the transcriptomic analysis, threshold cycle (Ct) values were calculated from the total samples (Fig 2). The average Ct values for the 12 genes ranged from 13.45 to 30.31, and most Ct values were between 21 and 28. UBCE was the most abundantly expressed gene, and SYB was the least expressed gene.

[Figure omitted. See PDF.]

The lines crossing the boxes represent the medians. The box plots represent the 25th and 75th percentiles. The whiskers represent the minimum and maximum values.

geNorm analysis

The geNorm analysis was based on the ‘pairwise comparison strategy’; the gene with the lowest M value had the most stable expression and was selected as a potential reference gene, while the gene with the highest M value had greater variation in expression. The results shown in Fig 3 revealed that different reference genes had different stabilities under different conditions. The top 2 stable reference genes for RT‒qPCR normalization were TBP1 and RAN for the fruiting body stage (M<0.5), ACT and EIF for heat stress and heavy metal stress (M<0.5), TBP1 and MAPK for cold stress (M<0.5), EF2 and TBP1 for oxidative stress (M<0.5), ACT and EF2 for salt stress (M<0.5), SYB and SPRY for acid stress (M<0.5), and MAPK and RAN for alkali stress (M<0.5). Except for UBCE, all the genes had the highest M values <1.0 across all samples, and SYB and SMF1 were the most stable genes (Fig 3). Therefore, these two reference genes were regarded as the best reference genes under the test conditions based on our data.

[Figure omitted. See PDF.]

The average expression stability values (M) of the reference genes were measured. The least stable gene with the highest M value is located on the left, while the most stable gene is located on the right.

It was acknowledged that a combination of two or more reference genes in RT‒qPCR analysis might generate results with higher accuracy than a single gene. The pairwise variations Vn/Vn + 1 between two normalization factors (NF values NFn, where n = number of genes included) calculated using geNorm software could estimate the optimal number of genes required for accurate normalization. The results showed that all the V-values were < 0.15 (Fig 4). However, the average geNorm M-value of genes (Fig 3) in all the treatments was not lower than 0.2, which suggested that two reference genes were adequate for normalizing gene expression data.

[Figure omitted. See PDF.]

Pairwise variations (Vn/n+1) calculated using geNorm. V is the variation value, where >0.15 indicates that an additional reference gene does not improve normalization.

NormFinder analysis

The program NormFinder takes into account the two most stable reference genes in intra- and intergroup expression, which was used to evaluate the optimal reference genes for RT‒qPCR normalization. A lower stability value indicated greater stability. The gene expression stabilities evaluated using NormFinder are listed in Table 2. The ranking order from top to bottom revealed that the stabilities decreased. More than 10 genes were candidate genes with stability values (SV) <0.15 during development, and Actin and CYP were the most stable (SV = 0.00). Actin, EIF, and TBP1 were the most stable under heavy metal stress (SV = 0.002), EF1 and Actin were the most stable under salt stress (SV = 0.003), EF2 and MAPK were most stable under acid stress, TBP1 and RAN were the most stable under alkali stress, CYP and UBCE were the most stable under cold stress, and Actin and EIF were the most stable under heat stress. Across all the samples, MSF1 was the most stable, followed by SYB, MAPK and EF2.

[Figure omitted. See PDF.]

BestKeeper analysis

BestKeeper is an Excel-based tool based on pairwise correlation analysis of selected reference genes by calculating the standard deviation (SD) and coefficient of variance (CV), which could be used to help in the selection of suitable reference genes. The lowest CV and SD were considered to be the most stably expressed evaluation criteria; gradual increases suggested that the stability decreased. It is essential to note that genes with an SD > 1 should be considered unacceptable. The BestKeeper analysis rankings revealed that SYB was the most suitable for samples in cold and heavy metal environments, EIF in a heat environment, MSF1 in an oxidative environment, UBCE in a salt environment, SPYR in an acid environment, and Actin in an alkaline environment (Fig 5). MSF1 and CYP were the most stable genes with the highest correlation coefficient. These results were in agreement with those selected by geNorm and NormFinder.

[Figure omitted. See PDF.]

The lowest CV and SD revealed the most stable location on the right side of the plot, while the least stable gene is located on the left.

Comprehensive stability analysis of the reference genes

To obtain a consensus result from the three methods above, the geometric means of combinations of all the results and the corresponding rankings for each candidate gene were used (Table 3). The combined results revealed that MSF1, SYB, MAPK, TBP1 and SPRY were the top 5 stable reference genes in all sample treatments. In contrast, EF1, Actin and UBCE were unstably expressed in most of the tested samples. Based on the geometric means of the three algorithms’ corresponding rankings, the results were more intuitive.

[Figure omitted. See PDF.]

Discussion

RT‒qPCR is now commonly used in scientific research for the analysis of gene expression; thus, it is critical to select a suitable reference for improving the accuracy and reliability during the qRT‒PCR process. However, there is no reference gene whose expression stability is consistent under different conditions that is suitable for all species. Therefore, many studies have been conducted to select stable reference genes under certain conditions. In our study, a total of 12 selected reference genes were evaluated using a transcriptomic method and three algorithms. This is the first detailed study on the stability of internal control genes for RT‒qPCR analysis in P. portentosus, and it revealed that transcriptome data could help screen suitable genes under different conditions. The results will be beneficial to gene expression analysis in this fungus.

Many genomic and transcriptomic datasets have been deposited in NCBI databases. These resources are an efficient way to mine and evaluate reference candidates [43–45, 47]. A large number of studies working on reference gene validation have been performed in plants (e.g., Arabidopsis, Brassica napus, and rice) and humans by selecting stably expressed genes from microarray and transcriptome datasets [8, 11, 39, 52, 53]. During this processing, 11 out of 25 traditional reference genes were maintained in this study. A total of 14 genes were excluded, including the commonly used internal control genes 18S rRNA and GAPDH. 18S rRNA and GAPDH are the least stable genes in plants, fungi, and animals, especially in mushrooms, e.g., O. sinensis, A. heimuer, F. filiformis, A. cornea, V. volvacea, and G. lucidum [16, 27, 30, 34, 54]. The results of the evaluation of the retained genes preprocessed using transcriptomic data were better than those directly evaluated using RT‒PCR and geNorm, NormFinder, and BestKeeper. Based on genomic and transcriptomic datasets in B. napus L., a total of 12 genes could be used as appropriate reference genes and were better than traditionally used housekeeping genes [53]. In addition, some novel candidate genes could be mined using transcriptomic methods. In Arabidopsis, 8 newly discovered reference genes were mined and identified using the omics method [13]. In this study, the gene SYB, with the lowest CV value based on the transcriptome data during the developmental stages, was also selected. Validation using different algorithms showed that SYB was one of the most stable reference genes, in line with the transcriptome results. All the results revealed that pretreatment with multiple transcriptomic datasets could provide unimaginable information about gene expression variation across different stages and treatments and hence provide good reference gene candidates [55].

In this study, MSF1, SYB, MAPK, TBP1, and SPRY were the most stable reference genes when all the results generated from the different methods were combined. In Volvariella volvacea, SPRY and MSF1 were the most stably expressed genes under different designed conditions [16], and MAPK was the best reference gene under acid treatment [31]. TBP (TATA-box-binding protein) was proven to be most stably expressed under different conditions and was sufficient for reliable results in humans [56]. In contrast, EF1, Actin and UBCE were unstably expressed in most tested samples of P. portentosus. Actin was the least stable and was deemed to be unsuitable as an internal control for V. volvacea, C. militaris, and L. edodes gene expression studies under different developmental stages and in different media [14, 16, 17]. ACT1 was the best internal control gene in F. filiformis [33, 34]. The stability of UBC was not consistent with previous studies; UBC was one of the most stable genes in Sacha inchi seedlings [57], pearl millet [Pennisetum glaucum (L.) R. Br.] [58], Japanese flounder (Paralichthys olivaceus) [59], developing and postharvested fruits of Actinidia chinensis [60], pepper (Capsicum annuum L.) [61] and Eucommia ulmoides Oliv [62]. All the results revealed that there were no consensus reference genes in all the species.

It was suggested that a combination of two or more reference genes should be used for RT‒qPCR analysis to generate more reliable results [63, 64]. Pairwise variation calculated in geNorm could be used to determine how many reference genes are needed for an accurate analysis. When analyzing all experimental groups under different conditions, all had V scores much lower than 0.15, which indicated that there was no need to select a combination of different reference genes for normalization. However, the results must be in line with M values generated by geNorm. Because not all the genes had M<0.2, two reference genes were adequate for RT‒qPCR analysis. MSF1 and SYB were selected as the best references for P. portentosus under different conditions.

Conclusions

After evaluating the expression stability of 12 candidate reference genes using geNorm, NormFinder and RefFinder under different developmental stages and growth conditions, different genes were found to be suitable for different conditions in P. portentosus. MSF1, SYB, MAPK, TBP1 and SPRY were the top 5 stable reference genes in all sample treatments, while elongation factor 1-alpha (EF1), actin, and ubiquitin-conjugating enzyme (UBCE) were the most unstably expressed genes. Two reference genes were adequate for RT‒qPCR studies to generate high-accuracy results. The combination of MSF1 and SYB might be the best selection. This is the first systematic study for selecting and validating reference genes in this fungus and provides guidelines for identifying genes and quantifying gene expression.

Supporting information

S1 Checklist. MIQE checklist.

https://doi.org/10.1371/journal.pone.0288982.s001

(XLSX)

S1 Raw images. The amplified fragments of candidate reference genes shown by agarose gel electrophoresis.

0–12 lanes: CK, MSF1, SPRY, EF2, RAN, EIF, UBCE, EF1, MAPK, TBP1, SYB, Actin, CYP. X: marker.

https://doi.org/10.1371/journal.pone.0288982.s002

S1 Fig. Melting curve generated by qRT-PCR for 12 reference genes.

https://doi.org/10.1371/journal.pone.0288982.s003

(PDF)

Citation: Hu C-M, Zhou C-L, Wan J-N, Guo T, Ji G-Y, Luo S-Z, et al. (2023) Selection and validation of internal control genes for quantitative real-time RT‒qPCR normalization of Phlebopus portentosus gene expression under different conditions. PLoS ONE 18(9): e0288982. https://doi.org/10.1371/journal.pone.0288982

About the Authors:

Chen-Menghui Hu

Contributed equally to this work with: Chen-Menghui Hu, Chen-Li Zhou

Roles: Data curation, Formal analysis, Methodology

Affiliation: Key Laboratory of Agricultural Genetics and Breeding of Shanghai, National Engineering Research Center of Edible Fungi, Key Laboratory of Edible Fungal Resources and Utilization (South), Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences, Shanghai, China

Chen-Li Zhou

Contributed equally to this work with: Chen-Menghui Hu, Chen-Li Zhou

Roles: Conceptualization, Data curation, Methodology

Affiliation: Key Laboratory of Agricultural Genetics and Breeding of Shanghai, National Engineering Research Center of Edible Fungi, Key Laboratory of Edible Fungal Resources and Utilization (South), Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences, Shanghai, China

Jia-Ning Wan

Roles: Data curation, Formal analysis, Methodology

Affiliation: Key Laboratory of Agricultural Genetics and Breeding of Shanghai, National Engineering Research Center of Edible Fungi, Key Laboratory of Edible Fungal Resources and Utilization (South), Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences, Shanghai, China

Ting Guo

Roles: Data curation, Methodology

Affiliation: Key Laboratory of Agricultural Genetics and Breeding of Shanghai, National Engineering Research Center of Edible Fungi, Key Laboratory of Edible Fungal Resources and Utilization (South), Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences, Shanghai, China

Guang-Yan Ji

Roles: Methodology

Affiliation: Hongzhen Agricultural Science and Technology Co. Ltd., Jinghong, China

Shun-Zhen Luo

Roles: Methodology

Affiliation: Hongzhen Agricultural Science and Technology Co. Ltd., Jinghong, China

Kai-Ping Ji

Roles: Investigation, Methodology

Affiliation: Hongzhen Agricultural Science and Technology Co. Ltd., Jinghong, China

Yang Cao

Roles: Methodology

Affiliation: Hongzhen Agricultural Science and Technology Co. Ltd., Jinghong, China

Qi Tan

Roles: Conceptualization, Investigation, Writing – review & editing

Affiliation: Key Laboratory of Agricultural Genetics and Breeding of Shanghai, National Engineering Research Center of Edible Fungi, Key Laboratory of Edible Fungal Resources and Utilization (South), Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences, Shanghai, China

Da-Peng Bao

Roles: Investigation, Methodology, Supervision, Writing – original draft

Affiliation: Key Laboratory of Agricultural Genetics and Breeding of Shanghai, National Engineering Research Center of Edible Fungi, Key Laboratory of Edible Fungal Resources and Utilization (South), Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences, Shanghai, China

Rui-Heng Yang

Roles: Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft

E-mail: [email protected]

Affiliation: Key Laboratory of Agricultural Genetics and Breeding of Shanghai, National Engineering Research Center of Edible Fungi, Key Laboratory of Edible Fungal Resources and Utilization (South), Institute of Edible Fungi, Shanghai Academy of Agricultural Sciences, Shanghai, China

ORICD: https://orcid.org/0000-0001-5442-9388

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