High-Resolution Core Gene-Associated Multiple Nucleotide Polymorphism (cgMNP) Markers for Strain Identification in the Wine Cap Mushroom Stropharia rugosoannulata
by
, , , and
Fei Liu
1
,
Bin Cao
1,
Hongmei Dai
1,
Guojie Li
2
,
Shoumian Li
2,
Wei Gao
3,* and
Ruilin Zhao
1,4,*
1
State Key Laboratory of Microbial Diversity and Innovative Utilization, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China
2
College of Horticulture, Hebei Agricultural University, Baoding 071001, China
3
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
4
College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
*
Authors to whom correspondence should be addressed.
Microorganisms 2025, 13(7), 1685; https://doi.org/10.3390/microorganisms13071685
Submission received: 30 May 2025
/
Revised: 9 July 2025
/
Accepted: 16 July 2025
/
Published: 17 July 2025
(This article belongs to the Special Issue Fungal Biology and Interactions—3rd Edition)
Abstract
Stropharia rugosoannulata, an ecologically valuable and economically important edible mushroom, faces challenges in strain-level identification and breeding due to limited genomic resources and the lack of high-resolution molecular markers. In this study, we generated high-quality genomic data for 105 S. rugosoannulata strains and identified over 2.7 million SNPs, unveiling substantial genetic diversity within the species. Using core gene-associated multiple nucleotide polymorphism (cgMNP) markers, we developed an efficient and transferable framework for strain discrimination. The analysis revealed pronounced genetic differentiation among cultivars, clustering them into two distinct phylogenetic groups. Nucleotide diversity (π) across 83 core genes varied significantly, highlighting both highly conserved loci under purifying selection and highly variable loci potentially associated with adaptive evolution. Phylogenetic analysis of the most variable gene, Phosphatidate cytidylyltransferase mitochondrial, identified 865 SNPs, enabling precise differentiation of all 85 cultivars. Our findings underscore the utility of cgMNP markers in addressing challenges posed by horizontal gene transfer and phylogenetic noise, demonstrating their robustness in cross-species applications. By providing insights into genetic diversity, evolutionary dynamics, and marker utility, this study establishes a foundation for advancing breeding programs, conservation strategies, and functional genomics in S. rugosoannulata. Furthermore, the adaptability of cgMNP markers offers a universal tool for high-resolution strain identification across diverse fungal taxa, contributing to broader fungal phylogenomics and applied mycology.
1. Introduction
Stropharia rugosoannulata Farl. ex Murrill, commonly known as the wine cap mushroom, is a fast-growing, resilient, and nutritionally valuable edible fungus [1,2]. With an estimated annual production exceeding 490,000 tons in China (https://bigdata.cefa.org.cn/ accessed on 10 July 2025), it is considered one of the most commercially important edible fungi in China, frequently integrated into recycling agricultural systems and functional food production. First domesticated in Germany in the 1960s, S. rugosoannulata was later endorsed by the Food and Agriculture Organization (FAO) as a promising cultivated mushroom for developing countries [3]. Renowned for its adaptability, S. rugosoannulata is widely cultivated using agricultural by-products, showcasing a robust capacity for lignocellulose degradation [4]. Studies have demonstrated that this species produces extracellular peroxidases during growth in beech wood microcosms, with manganese-oxidizing peroxidases identified as the primary enzymes responsible for lignin degradation [5]. In addition, it has demonstrated potential in environmental applications, such as wastewater treatment, due to its ligninolytic enzymatic activity [6]. Recently, the genome sequences and transcripts of S. rugosoannulata have been determined, providing a foundational resource for further genetic studies [7,8,9,10,11].
Despite its ecological and industrial significance, genetic studies on S. rugosoannulata remain limited [12], which hampers efforts in breeding, conservation, and understanding its evolutionary relationships. Accurate strain-level identification is crucial, as strains of the same species often differ markedly in phenotypic traits such as cap color, developmental timing, and degradation efficiency [13,14,15]. Traditional methods based on morphology and biochemistry lack resolution and reproducibility, especially for closely related strains [16]. Although molecular markers—such as single nucleotide polymorphisms (SNPs) and multiple nucleotide polymorphisms (MNPs)—have improved strain differentiation in other fungi [13,17,18], their application in S. rugosoannulata remains underdeveloped.
Recent advances in molecular marker technologies, particularly MNP markers, offer promising solutions to these challenges. MNP markers have been successfully employed for strain identification in various fungi, including Lentinula edodes [17], Flammulina filiformis [18], and Pleurotus eryngii [19]. Compared to earlier methods like Simple Sequence Repeats (SSR) markers, MNPs offer superior accuracy and accessibility. For instance, SSR markers, though previously used to identify strains of Agaricus bisporus [20], are prone to errors during polymerase chain reactions due to DNA polymerase slippage [21,22]. MNP markers avoid such pitfalls, enabling more reliable genetic analyses. However, conventional MNP-based approaches often generate large datasets, posing analytical challenges.
Core gene-associated MNP (cgMNP) markers overcome these limitations by targeting polymorphisms within conserved, single-copy genes [23]. These markers offer a balance between evolutionary stability and informative variation, enabling high-resolution phylogenetic reconstruction and facilitating strain-level discrimination. Recent studies in A. bisporus have demonstrated the effectiveness of cgMNP markers in resolving complex strain relationships and uncovering subtle genetic differences [23]. Derived from conserved genomic regions, cgMNPs are less prone to horizontal gene transfer, ensuring high reliability and transferability [23,24]. By enabling the construction of high-resolution phylogenies, these markers provide valuable insights into strain relationships and comparative genomics, making them a versatile tool in fungal phylogenomics [23,25].
In this study, we applied cgMNP markers to a population of 105 S. rugosoannulata strains to investigate strain differentiation and population structure. By integrating cgMNPs with whole-genome resequencing, we constructed a high-resolution phylogenetic framework and assessed genetic diversity. This work extends the use of cgMNPs to a new species, providing a genomic resource to support breeding, conservation, and phylogenetic studies in edible mushrooms.
2. Materials and Methods
2.1. Sample Collection, DNA Extraction and Genome Resequencing
A total of 105 Stropharia rugosoannulata strains (Table 1) were selected to represent broad genetic, geographic, and phenotypic diversity. The collection included 85 cultivated and 20 wild strains obtained from major production regions and native habitats across China. All strains were isolated through tissue culture of fresh fruiting bodies, followed by multiple rounds of sub-culturing on potato dextrose agar (PDA) medium. Axenic purity was confirmed via microscopic examination to ensure the absence of contamination. Mycelial biomass was then harvested for genomic DNA extraction using the CTAB method. DNA quality and concentration were assessed with a NanoDrop spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA) and further quantified using Qubit fluorometry (Thermo Fisher Scientific Inc., Waltham, MA, USA) to ensure sufficient quality for sequencing. Whole-genome sequencing was conducted on the Illumina NovaSeq 6000 platform (Illumina, Inc., San Diego, CA, USA), generating 150 bp paired-end reads for each strain.
2.2. Read Mapping and Genotype Calling
Raw reads were trimmed to remove adapter sequences and low-quality bases using fastp [26]. Processed reads were aligned to the S. rugosoannulata reference genome (obtained from NCBI) using the Burrows–Wheeler Aligner (BWA) MEM algorithm (version 0.7.17-r1188) [27], achieving an average genome coverage of 98.1% across strains. The mapping rate, genome depth, and quality of aligned reads were analyzed to ensure uniform data quality across samples. The sequencing statistics of the samples are summarized in Table 1 and Table S1. Variant calling was conducted using the SAMtools v1.10 [8], with separate pipelines for identifying SNPs and MNPs. Variants were filtered based on quality scores (QUAL > 30) and read depth (DP > 10) to minimize false positives. We identified a total of 2,760,421 SNPs, with a subset of 433,344 SNPs exhibiting minor allele frequencies (MAF) > 0.05, which were present in more than half of the strains. A Circos plot was generated using the circlize package (v0.4.16) in R [28] to visualize SNP density, MNP positions, and GC content across the genome.
2.3. Core Gene Identification and Marker Development
We used BUSCO v5.2.2 [29] to identify core genes in the S. rugosoannulata genome, targeting genes conserved across the fungal lineage. Eighty-three core genes were selected as previously based on criteria of single-copy status and uniform distribution across the genome (Table S2). GO category and KEGG pathway were annotated using eggNOG-mapper v2 for eggNOG 5.0 [30]. We identified 10,143 SNPs associated with these genes to be used as core gene-associated MNP (cgMNP) markers, following approaches previously validated in Agaricus bisporus [23].
2.4. Phylogenetic Analysis and Statistical Analysis
Neighbor-joining (NJ) trees were constructed based on core gene-associated MNP markers to evaluate the genetic relationships among the S. rugosoannulata strains. The phylogenetic tree was visualized in iTOL v6 [31], with color-coded rings indicating strain origin (geographic and cultivation source) and cap color for ease of interpretation. Genetic similarity (GS) between two strains was calculated using the following formula: the number of identical core MNP sequences shared between two strains divided by the total number of core MNP sequences analyzed. Descriptive statistics of nucleotide diversity (π) for each core gene were calculated using VCFtools version 0.1.16 [32] to quantify genetic variation. Nucleotide diversity data were visualized through sliding window analysis (window length: 300 bp, step size: 50 bp), highlighting the top 12 genes with the highest diversity values for comparative analysis.
3. Results
3.1. Genome Resequencing and Identification of Core Gene-Assicated MNP Markers in Stropharia rugosoannulata
We collected 105 Stropharia rugosoannulata strains, including 20 wild and 85 cultivated strains from diverse geographic locations in China (Table 1). The geographic distribution of all strain collection sites is illustrated in Supplementary Figure S1, created using the hchinamap v0.1.0 R package. We sequenced these S. rugosoannulata strains, yielding a total of approximately 441.6 Gb of high-quality, clean data, averaging 4.2 Gb per strain (Table S1). Quality metrics were consistently high, with a mean Q20 value of 98.2% and a mean Q30 value of 93.0%, ensuring that the dataset was robust for downstream genetic analyses, including the identification of multiple nucleotide polymorphism (MNP) markers. Mapping reads to the S. rugosoannulata reference genome (Figure 1) yielded a mean genome coverage of 98.1%, a mean sequencing depth of 82.3, and an average mapping rate of 96.7% (Table S1). A total of 2,760,421 single nucleotide polymorphisms (SNPs) were identified across the genome. Of these, 433,344 SNPs, with a minor allele frequency (MAF) greater than 0.05 and present in more than half of the 105 strains, were selected as input for the identification of core gene-associated multiple nucleotide polymorphism (cgMNP) markers. A Circos plot illustrates the genome-wide characteristics of S. rugosoannulata, showing the locations of MNP markers alongside GC content and SNP density (Figure 1).
To assess the transferability of gene markers initially identified in Agaricus bisporus, we examined the presence of 83 core genes in the S. rugosoannulata genome using BUSCO. We successfully annotated these genes in the reference genome, identifying a total of 2205 SNPs associated with these core gene markers. The spatial distribution of these MNPs is visualized in Figure 1, demonstrating their genomic distribution and suitability for strain differentiation studies.
3.2. Phylogenetic Analysis and Genetic Similarity Values Using Core Gene-Associated MNP Markers
Using the 2205 SNPs identified within core gene markers, we constructed a neighbor-joining (NJ) phylogenetic tree to analyze the genetic relationships among the 105 S. rugosoannulata strains. This tree revealed distinct genetic subgroups, with patterns of differentiation linked to geographical origin and cultivation status. The NJ tree visualized these relationships, providing insights into evolutionary connections and population dynamics within S. rugosoannulata (Figure 2). The analysis supports the utility of core gene-associated MNP markers in exploring complex genetic structures, enabling high-resolution strain identification and evolutionary studies within and beyond A. bisporus populations.
To further explore genetic relationships among the 85 S. rugosoannulata cultivars, we calculated genetic similarity (GS) values for all pairwise comparisons. This analysis identified two distinct pedigrees, labeled G1 to G2 (Figure 3; Table S3). The number of strains within each pedigree was as follows: G1 included six strains with yellow pileus, and G2 comprised the majority with 79 strains. GS values within each pedigree exceeded 69.5%, with mean GS values of 96.0% and 91.2%, respectively. A GS threshold of 69.5% effectively differentiated pedigrees, and strains within the same pedigree were considered different cultivars if their GS values ranged from 69.5% to 97.6%.
3.3. Nucleotide Diversity Analysis of Core Genes
Nucleotide diversity (π) is a crucial metric, as it measures the average degree of polymorphism within a population. A higher π value indicates greater genetic variation, which can be linked to evolutionary adaptation, ecological interactions, and selective pressures. The average value of π across the 83 genes varied between 0.0000 and 0.0160, with a mean π value of 0.0033. Figure 4A presents the nucleotide diversity for the 12 genes exhibiting the highest diversity, which includes genes annotated with “Phosphatidate cytidylyltransferase, mitochondrial,” “WD40-repeat-containing domain,” “RNA recognition motif domain,” “Prp18,” “Coatomer subunit gamma,” “Aminoacyl-tRNA synthetase, class II,” “Glycosyl transferase, family 3,” “Rab-GTPase-TBC domain,” “Actin family,” “Peroxisome biogenesis factor 1,” and “Splicing factor 3B subunit 1.” GO and KEGG analyses revealed that the top 12 highly diverse genes are involved in key functions such as the metabolic process, cellular component organization or biogenesis, organelle, and membrane part (Figure 4B; Table S2).
3.4. Phylogenetic Analysis Using the Most Variable Gene
The most variable gene identified in this study is Phosphatidate cytidylyltransferase mitochondrial (also known as MMp37 or Tam41), which catalyzes the formation of CDP-diacylglycerol (CDP-DAG) from phosphatidic acid within the mitochondrial inner membrane. CDP-DAG is essential for the biosynthesis of cardiolipin, a dimeric phospholipid that stabilizes supercomplexes of the mitochondrial respiratory chain, thereby playing a critical role in mitochondrial function.
To further explore its utility as a genetic marker, we selected Phosphatidate cytidylyltransferase mitochondrial for phylogenetic analysis of 85 S. rugosoannulata cultivars. Leveraging the 865 SNPs identified within this gene, we constructed a phylogenetic tree to assess the genetic relationships among these strains. The analysis demonstrated that this single gene marker could achieve 100% discrimination of all the cultivars (Figure 5). These results highlight the potential of Phosphatidate cytidylyltransferase mitochondrial as a robust and precise marker for strain differentiation in S. rugosoannulata.
4. Discussion
This study provides the first application of core gene-associated MNP (cgMNP) markers in Stropharia rugosoannulata, offering new insights into its population structure, domestication patterns, and core gene variability. Resequencing 105 strains revealed over 2.7 million SNPs, and the cgMNP approach enabled high-resolution strain differentiation. Notably, Phosphatidate cytidylyltransferase mitochondrial was identified as the most polymorphic gene, serving as a powerful marker for distinguishing cultivars and supporting breeding strategies.
4.1. Insights into Stropharia rugosoannulata Genetic Diversity and Phylogeny
Our analysis revealed two major phylogenetic clades, corresponding to wild and cultivated origins. This differentiation likely reflects distinct selective pressures under natural versus managed environments, consistent with patterns observed in other domesticated fungi [12,13]. Among the 83 core genes analyzed, Phosphatidate cytidylyltransferase mitochondrial, involved in lipid metabolism and mitochondrial integrity, exhibited the highest polymorphism, with 865 SNPs. In humans, biallelic variants in TAMM41 (the human counterpart of this gene) have been linked to reduced cardiolipin levels in muscles, which contribute to neonatal mitochondrial disease [33]. Despite its functional conservation across eukaryotes, this gene alone enabled the complete discrimination of all 85 domesticated strains. Its variability highlights the utility of targeting core functional loci to distinguish closely related genotypes.
Adaptive evolution in S. rugosoannulata is likely driven by its exposure to diverse substrates and cultivation systems, leading to selection for traits such as mycelial growth, cap morphology, and lignocellulose degradation. These traits are encoded, in part, by core genes under both purifying and diversifying selection. The cgMNP strategy, by focusing on such genes, effectively captures both deep phylogenetic signals and recent adaptive divergence—making it especially well-suited for studying species under domestication and ecological transition.
While traditional single-gene markers are often limited in resolution [34], cgMNPs leverage polymorphisms within conserved loci to enhance discriminatory power. This genome-informed approach enables precise reconstruction of evolutionary relationships, offering insights into how selection and adaptation have shaped S. rugosoannulata’s genetic landscape.
4.2. Advantages of Core Gene-Associated MNP Markers
CgMNPs are derived from single-copy core genes that are both functionally essential and taxonomically conserved. This minimizes confounding effects from rapidly evolving or horizontally transferred regions [35,36]. Moreover, cgMNPs strike an effective balance between sequence conservation and informative polymorphism, enabling both broad taxonomic resolution and fine-scale discrimination at the strain level.
Although first applied in Agaricus bisporus [23], our study demonstrates the broader applicability of cgMNPs in a phylogenetically distant Basidiomycete. To our knowledge, this is only the second published report utilizing cgMNPs for fungal strain identification, underscoring the novelty of the method. While the formal literature on cross-species transferability remains limited due to the recency of the approach, our findings provide direct and empirical validation: 2205 SNPs derived from these core genes enabled high-resolution phylogenetic reconstruction across 105 strains. These results not only affirm the robustness of cgMNPs but also highlight their promising utility in broader fungal lineages.
This dual capacity of cgMNPs—anchoring analyses in evolutionary conserved loci while still capturing strain-specific variation—is further illustrated by our identification of Phosphatidate cytidylyltransferase mitochondrial as the most polymorphic gene. Its 865 SNPs enabled 100% resolution of all 85 cultivars, making it a powerful candidate for rapid strain differentiation.
Beyond this, our analysis of nucleotide diversity (π) across 83 core genes revealed considerable heterogeneity, ranging from highly conserved genes under purifying selection to others with substantial variability—likely reflecting recent adaptation or domestication. Notably, genes containing “WD40-repeat-containing domains” and “RNA recognition motif domains” have well-established roles in eukaryotes, including functions in signal transduction, cell cycle regulation, gene expression, and protein–protein interactions [37,38]. Their high nucleotide diversity in this study suggests adaptive evolution in S. rugosoannulata, possibly driven by environmental factors or cultivation conditions. Similarly, the diversity observed in genes such as “Rab-GTPase-TBC domain” and “Coatomer subunit gamma,” which are involved in intracellular transport and vesicle formation, is significant. These processes are crucial for cell growth and development [37]. In contrast, genes exhibiting low diversity likely reflect strong purifying selection, ensuring the maintenance of essential biological functions [12].
These findings lead us to hypothesize that polymorphisms in genes involved in mitochondrial lipid metabolism, RNA processing, and intracellular transport may have been favored during artificial selection. Such adaptations could enhance traits critical to domesticated performance—such as growth rate, stress resilience, and substrate utilization. This framework lays the groundwork for integrating cgMNP-based markers with GWAS and transcriptomics to uncover the functional roles of high-variation loci.
4.3. Implications for Breeding and Future Directions
The clear subdivision of strains into genetic groups provides a practical foundation for breeding. These groups facilitate the selection of strains with desirable traits such as yield, stress tolerance, and morphology. The identification of Phosphatidate cytidylyltransferase mitochondrial as a highly informative locus supports the integration of gene-specific markers into precision breeding.
In breeding programs, cgMNPs can be used to verify hybrids, trace parentage, and authenticate elite cultivars—supporting intellectual property protection and germplasm management. The 865-SNP profile within a single gene could be adapted into barcode panels or diagnostic kits for rapid in-lab or field-based identification.
Beyond genetic applications, S. rugosoannulata has a strong ecological value. Its ability to grow agricultural waste and degrade lignin-rich substrates makes it a valuable species in circular agriculture. Recent studies have underscored the broader agricultural value of edible mushrooms: a livestock–crop–mushroom circular system improves plant growth and reduces antibiotic resistance in soils [39], while spent mushroom substrate enhances microbial abundance and enzymatic activity across farming systems [40]. These findings reinforce the ecological relevance of the genomic markers we developed.
Looking ahead, expanding the core gene set and validating cgMNPs across more fungal taxa will strengthen their applicability. Addressing issues such as horizontal gene transfer and gene rate heterogeneity will further improve phylogenomic precision. Importantly, integrating polymorphic core gene markers into breeding pipelines offers practical advantages. For instance, cgMNP markers can be used to verify hybrid authenticity, trace parentage in controlled crosses, and distinguish elite cultivars in variety registration or field trials. These applications support efficient germplasm selection, protect intellectual property, and accelerate genetic improvement in S. rugosoannulata and other edible mushrooms.
5. Conclusions
This study demonstrates that core gene-associated MNP (cgMNP) markers enable accurate and high-resolution strain identification in Stropharia rugosoannulata. Using over 2.7 million SNPs from 105 strains, we characterized population structure and genetic diversity with precision. A single gene—Phosphatidate cytidylyltransferase mitochondrial—proved sufficient to distinguish all cultivars, highlighting the power of gene-specific markers. Beyond this species, cgMNP markers show promise for broader fungal applications due to their balance of evolutionary conservation and polymorphism. These findings offer valuable genomic resources for breeding, conservation, and phylogenomic research in edible mushrooms.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms13071685/s1, Figure S1: Geographic distribution of the 105 Stropharia rugosoannulata strains analyzed in this study. The map is color-coded to indicate the number of strains collected from each province in China. Table S1: Basic information of whole-genome sequenced 105 Stropharia rugosoannulata strains. This table lists essential details of the Stropharia rugosoannulata strains used in the study, including strain identifiers, origins (cultivated or wild), and sequencing statistics. Table S2: Detailed information of 83 core MNP marker-associated genes. Table S3: GS values of pairwise comparisons between all cultivars of Stropharia rugosoannulata.
Author Contributions
Resources, S.L. and H.D.; methodology, F.L.; investigation, G.L. and F.L.; writing—original draft preparation, F.L.; writing—review and editing, F.L.; visualization, F.L. and B.C.; supervision, W.G. and R.Z.; funding acquisition, R.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the National Key R&D Program of China project (2022YFD1200605), the Beijing Innovation Consortium of Agriculture Research System (BAIC03), the Key Research and Development Planning Project in Science and Technology of Hebei Province (21326315D), the Innovation Team of Edible Fungi of Hebei Modern Agricultural Industrial Technology System, Valuable and Rare Edible Fungus Position (HBCT2023090202), and the Talent Introduction Scientific Research Special Project of Hebei Agricultural University (YJ201849).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.
Acknowledgments
The authors express their sincere gratitude to Yunhui Wei from the Institute of Microbiology, Jiangxi Academy of Agricultural Sciences, and Dafeng Sun and Shaoxiong Liu from the Kunming Institute of Edible Fungi for providing some of the fungal strains used in this study.
Conflicts of Interest
The authors declare no conflicts of interest.
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Figure 1.
Circos plot of the Stropharia rugosoannulata genome, illustrating features from the outermost to innermost circles as follows: (i) the 16 longest scaffolds of the genome, (ii) GC content, (iii) SNP density, and (iv) positions of core gene-associated MNPs.
Figure 1.
Circos plot of the Stropharia rugosoannulata genome, illustrating features from the outermost to innermost circles as follows: (i) the 16 longest scaffolds of the genome, (ii) GC content, (iii) SNP density, and (iv) positions of core gene-associated MNPs.
Figure 2.
Phylogenetic tree of Stropharia rugosoannulata based on core gene-associated MNPs from 105 strains. This neighbor-joining tree, constructed from MNPs within core genes, depicts genetic relationships among wild and cultivated strains of S. rugosoannulata, with branch colors representing pileus color (brown/yellow). The innermost ring indicates geographic origin, and the outermost ring distinguishes between wild and cultivated strains, illustrating genetic diversity, geographic distribution, and domestication effects.
Figure 2.
Phylogenetic tree of Stropharia rugosoannulata based on core gene-associated MNPs from 105 strains. This neighbor-joining tree, constructed from MNPs within core genes, depicts genetic relationships among wild and cultivated strains of S. rugosoannulata, with branch colors representing pileus color (brown/yellow). The innermost ring indicates geographic origin, and the outermost ring distinguishes between wild and cultivated strains, illustrating genetic diversity, geographic distribution, and domestication effects.
Figure 3.
Heatmap illustrating pairwise genetic similarity (GS) values among 85 Stropharia rugosoannulata cultivars. Each cell represents the GS value between two strains, with color intensity indicating the degree of genetic similarity. Cultivars are grouped into two distinct pedigrees (G1 and G2) based on a GS threshold of 69.5%, enabling clear separation of genetic lineages. This analysis reveals both intra- and inter-pedigree variation, providing insights into the genetic structure of domesticated S. rugosoannulata.
Figure 3.
Heatmap illustrating pairwise genetic similarity (GS) values among 85 Stropharia rugosoannulata cultivars. Each cell represents the GS value between two strains, with color intensity indicating the degree of genetic similarity. Cultivars are grouped into two distinct pedigrees (G1 and G2) based on a GS threshold of 69.5%, enabling clear separation of genetic lineages. This analysis reveals both intra- and inter-pedigree variation, providing insights into the genetic structure of domesticated S. rugosoannulata.
Figure 4.
Genetic and functional characterization of the 12 highest diverse genes in Stropharia rugosoannulata. (A) Nucleotide diversity (π) profiles of the 12 genes with the highest variability, calculated using a sliding window approach (window size: 300 bp; step size: 50 bp). The Y-axis represents nucleotide diversity (π), and the X-axis denotes relative position along each gene. (B) Gene Ontology (GO) functional classification of these 12 genes based on Level 2 annotations. GO terms are grouped into three major categories: biological process, cellular component, and molecular function. The left Y-axis indicates the number of genes assigned to each GO category, while the right Y-axis shows the proportion of genes within each functional group.
Figure 4.
Genetic and functional characterization of the 12 highest diverse genes in Stropharia rugosoannulata. (A) Nucleotide diversity (π) profiles of the 12 genes with the highest variability, calculated using a sliding window approach (window size: 300 bp; step size: 50 bp). The Y-axis represents nucleotide diversity (π), and the X-axis denotes relative position along each gene. (B) Gene Ontology (GO) functional classification of these 12 genes based on Level 2 annotations. GO terms are grouped into three major categories: biological process, cellular component, and molecular function. The left Y-axis indicates the number of genes assigned to each GO category, while the right Y-axis shows the proportion of genes within each functional group.
Figure 5.
Phylogenetic tree of Stropharia rugosoannulata based on the phosphatidate cytidylyltransferase mitochondrial gene from 85 cultivars. The phylogenetic tree was constructed using 865 SNPs identified within the Phosphatidate cytidylyltransferase mitochondrial gene, demonstrating the genetic relationships among 85 cultivated strains of S. rugosoannulata. The outer ring highlights the classification of these strains into two distinct genetic pedigrees (G1–G2), providing clear visualization of their evolutionary divergence. This analysis underscores the utility of this gene as a high-resolution marker for precise strain differentiation and classification.
Figure 5.
Phylogenetic tree of Stropharia rugosoannulata based on the phosphatidate cytidylyltransferase mitochondrial gene from 85 cultivars. The phylogenetic tree was constructed using 865 SNPs identified within the Phosphatidate cytidylyltransferase mitochondrial gene, demonstrating the genetic relationships among 85 cultivated strains of S. rugosoannulata. The outer ring highlights the classification of these strains into two distinct genetic pedigrees (G1–G2), providing clear visualization of their evolutionary divergence. This analysis underscores the utility of this gene as a high-resolution marker for precise strain differentiation and classification.
Table 1.
Basic information of whole-genome sequenced 105 Stropharia rugosoannulata strains.
| Sample | BaseSum (G) | Mean Depth | Source | Location | Color |
|---|---|---|---|---|---|
| 01R0001 | 2.4634890 | 48.82 | Cultivar | Shaanxi | brown |
| 01R0002 | 2.7003492 | 53.53 | Cultivar | Fujian | brown |
| 01R0003 | 2.2118484 | 43.84 | Wild | Fujian | brown |
| 01R0004 | 2.4641238 | 47.61 | Wild | Fujian | brown |
| 01R0005 | 3.2382552 | 63.92 | Cultivar | Fujian | brown |
| 01R0006 | 3.1049862 | 61.25 | Wild | Fujian | brown |
| 01R0007 | 2.1526557 | 42.72 | Cultivar | Fujian | brown |
| 01R0008 | 2.8159038 | 55.81 | Cultivar | Fujian | brown |
| 01R0009 | 2.4095355 | 46.78 | Cultivar | Fujian | brown |
| 01R0010 | 3.8067042 | 75.28 | Cultivar | Beijing | brown |
| 01R0011 | 2.2150770 | 43.84 | Cultivar | Shandong | brown |
| 01R0012 | 2.3642259 | 44.65 | Wild | Shandong | brown |
| 01R0013 | 2.2152048 | 43.81 | Cultivar | Shandong | brown |
| 01R0014 | 2.7182691 | 53.96 | Wild | Shandong | brown |
| 01R0015 | 2.3323134 | 46.26 | Wild | Shandong | brown |
| 01R0016 | 2.1267690 | 41.98 | Wild | Shandong | brown |
| 01R0017 | 2.3049033 | 45.76 | Cultivar | Heilongjiang | brown |
| 01R0018 | 2.2960254 | 45.33 | Cultivar | Shandong | brown |
| 01R0019 | 2.5697190 | 48.79 | Wild | Heilongjiang | brown |
| 01R0020 | 2.1695034 | 42.97 | Cultivar | Heilongjiang | brown |
| 01R0021 | 2.5653045 | 50.58 | Cultivar | Xinjiang | brown |
| 01R0022 | 2.3667072 | 46.81 | Cultivar | Shandong | brown |
| 01R0023 | 1.8403629 | 36.23 | Cultivar | Fujian | brown |
| 01R0024 | 2.3851818 | 45.32 | Wild | Shanghai | brown |
| 01R0025 | 2.3525052 | 43.29 | Wild | Yunnan | brown |
| 01R0026 | 2.2615755 | 40.93 | Wild | Yunnan | brown |
| 01R0027 | 2.6118186 | 48.34 | Wild | Yunnan | brown |
| 01R0028 | 2.3147187 | 42.99 | Wild | Yunnan | brown |
| 01R0029 | 2.0687826 | 38.71 | Wild | Yunnan | brown |
| 01R0030 | 2.3813883 | 43.69 | Wild | Yunnan | brown |
| 01R0031 | 2.1197256 | 38.65 | Wild | Yunnan | brown |
| 01R0032 | 2.0447976 | 37.89 | Wild | Yunnan | brown |
| 01R0033 | 2.0854716 | 41.14 | Cultivar | Yunnan | brown |
| 01R0034 | 2.3866515 | 47.10 | Cultivar | Yunnan | brown |
| 01R0035 | 2.4101256 | 47.73 | Cultivar | Shandong | brown |
| 01R0036 | 2.4361248 | 47.90 | Cultivar | Shandong | brown |
| 01R0037 | 2.4706656 | 48.73 | Cultivar | Shandong | brown |
| 01R0038 | 2.8203177 | 55.23 | Cultivar | Shandong | brown |
| 01R0039 | 2.3010948 | 45.61 | Cultivar | Xinjiang | brown |
| 01R0040 | 2.2662033 | 44.86 | Cultivar | Xinjiang | brown |
| 01R0041 | 2.0078208 | 39.72 | Cultivar | Shandong | brown |
| 01R0042 | 2.2996011 | 45.61 | Cultivar | Shandong | brown |
| 01R0043 | 1.9783281 | 38.87 | Cultivar | Shandong | brown |
| 01R0044 | 2.2833378 | 43.88 | Wild | Shandong | brown |
| 01R0045 | 2.1330795 | 42.21 | Cultivar | Beijing | brown |
| 01R0046 | 2.3766336 | 46.77 | Cultivar | Beijing | brown |
| 02R0001 | 2.1366321 | 42.40 | Cultivar | Fujian | brown |
| 02R0002 | 3.9907788 | 78.80 | Cultivar | Shandong | brown |
| 02R0003 | 4.0328454 | 78.92 | Cultivar | Sichuan | brown |
| 03R0001 | 2.1084636 | 40.96 | Wild | Shandong | brown |
| Sru01 | 5.6994243 | 113.32 | Cultivar | Sichuan | brown |
| Sru02 | 5.5662936 | 111.02 | Cultivar | Sichuan | brown |
| Sru03 | 5.9468457 | 118.30 | Cultivar | Yunnan | yellow |
| Sru04 | 5.8567626 | 116.58 | Cultivar | Yunnan | yellow |
| Sru05 | 5.5058730 | 109.77 | Cultivar | Yunnan | yellow |
| Sru06 | 5.8558644 | 116.71 | Cultivar | Yunnan | yellow |
| Sru07 | 5.7291126 | 113.94 | Cultivar | Beijing | brown |
| Sru09 | 5.1560214 | 102.65 | Cultivar | Beijing | brown |
| Sru10 | 5.7752538 | 114.96 | Cultivar | Beijing | brown |
| Sru11 | 5.6348997 | 112.48 | Cultivar | Zhejiang | brown |
| Sru12 | 5.4018870 | 107.45 | Cultivar | Jiangxi | brown |
| Sru13 | 5.8063167 | 115.58 | Cultivar | Beijing | brown |
| Sru14 | 5.7922959 | 115.30 | Cultivar | Beijing | brown |
| Sru15 | 5.8244898 | 116.15 | Cultivar | Yunnan | yellow |
| Sru16 | 5.9801625 | 119.33 | Cultivar | Henan | yellow |
| Sru17 | 5.7301551 | 114.16 | Cultivar | Beijing | brown |
| Sru18 | 5.8504152 | 116.27 | Cultivar | Beijing | brown |
| Sru19 | 5.9063601 | 117.76 | Cultivar | Beijing | brown |
| Sru20 | 5.9725761 | 119.05 | Cultivar | Beijing | brown |
| Sru21 | 5.7092889 | 113.46 | Cultivar | Beijing | brown |
| Sru22 | 5.6090259 | 111.86 | Cultivar | Beijing | brown |
| Sru23 | 5.8316094 | 116.25 | Cultivar | Hubei | brown |
| Sru24 | 5.6767800 | 112.93 | Cultivar | Guangdong | brown |
| Sru25 | 5.7882921 | 115.13 | Cultivar | Hubei | brown |
| Sru26 | 5.9944332 | 119.12 | Cultivar | Shandong | brown |
| Sru27 | 5.9446413 | 118.46 | Cultivar | Hubei | brown |
| Sru28 | 5.5093176 | 109.88 | Cultivar | Jiangsu | brown |
| Sru29 | 5.7809028 | 115.23 | Cultivar | Shaanxi | brown |
| Sru30 | 5.7510969 | 114.69 | Cultivar | Yunnan | brown |
| Sru31 | 5.8846452 | 117.11 | Cultivar | Shaanxi | brown |
| Sru32 | 5.6994126 | 113.27 | Cultivar | Shaanxi | brown |
| Sru33 | 5.8452510 | 116.32 | Cultivar | Shaanxi | brown |
| Sru34 | 5.8584156 | 116.66 | Cultivar | Hebei | brown |
| Sru35 | 5.7443418 | 114.49 | Cultivar | Shaanxi | brown |
| Sru36 | 5.8878756 | 117.28 | Cultivar | Shaanxi | brown |
| Sru37 | 5.5336191 | 110.15 | Cultivar | Shaanxi | brown |
| Sru38 | 5.9763822 | 119.16 | Cultivar | Shaanxi | brown |
| Sru39 | 5.6468109 | 112.55 | Cultivar | Hebei | brown |
| Sru40 | 5.7865203 | 115.05 | Cultivar | Hebei | brown |
| Sru42 | 5.8858707 | 117.23 | Cultivar | Fujian | brown |
| Sru44 | 5.9152776 | 117.69 | Cultivar | Hubei | brown |
| Sru45 | 5.9668284 | 119.01 | Cultivar | Hebei | brown |
| Sru46 | 5.5061244 | 109.12 | Cultivar | Hebei | brown |
| Sru47 | 5.7764799 | 115.12 | Wild | Shandong | brown |
| Sru48 | 4.9183146 | 95.88 | Cultivar | Shandong | brown |
| Sru49 | 5.9916786 | 119.14 | Cultivar | Shandong | brown |
| Sru50 | 5.6500371 | 112.37 | Cultivar | Shandong | brown |
| Sru51 | 5.6395635 | 112.08 | Cultivar | Hubei | brown |
| Sru52 | 5.5902006 | 111.54 | Cultivar | Yunnan | brown |
| Sru53 | 5.9733798 | 118.83 | Cultivar | Sichuan | brown |
| Sru54 | 5.5741188 | 105.36 | Cultivar | Hunan | brown |
| Sru55 | 4.4327850 | 83.93 | Cultivar | Shanxi | brown |
| Sru56 | 5.7086718 | 113.87 | Cultivar | Gansu | brown |
| Sru57 | 4.9019262 | 97.74 | Cultivar | Yunnan | brown |
| Sru58 | 5.5635774 | 111.02 | Cultivar | Hebei | brown |
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Liu, F.; Cao, B.; Dai, H.; Li, G.; Li, S.; Gao, W.; Zhao, R. High-Resolution Core Gene-Associated Multiple Nucleotide Polymorphism (cgMNP) Markers for Strain Identification in the Wine Cap Mushroom Stropharia rugosoannulata. Microorganisms 2025, 13, 1685. https://doi.org/10.3390/microorganisms13071685
AMA Style
Liu F, Cao B, Dai H, Li G, Li S, Gao W, Zhao R. High-Resolution Core Gene-Associated Multiple Nucleotide Polymorphism (cgMNP) Markers for Strain Identification in the Wine Cap Mushroom Stropharia rugosoannulata. Microorganisms. 2025; 13(7):1685. https://doi.org/10.3390/microorganisms13071685
Chicago/Turabian StyleLiu, Fei, Bin Cao, Hongmei Dai, Guojie Li, Shoumian Li, Wei Gao, and Ruilin Zhao. 2025. "High-Resolution Core Gene-Associated Multiple Nucleotide Polymorphism (cgMNP) Markers for Strain Identification in the Wine Cap Mushroom Stropharia rugosoannulata" Microorganisms 13, no. 7: 1685. https://doi.org/10.3390/microorganisms13071685
APA StyleLiu, F., Cao, B., Dai, H., Li, G., Li, S., Gao, W., & Zhao, R. (2025). High-Resolution Core Gene-Associated Multiple Nucleotide Polymorphism (cgMNP) Markers for Strain Identification in the Wine Cap Mushroom Stropharia rugosoannulata. Microorganisms, 13(7), 1685. https://doi.org/10.3390/microorganisms13071685
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