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Article

Lipid Metabolism and Membrane Remodeling Drive Sclerotium Formation in Morchella eximia: Insights from Integrated Transcriptomics and Metabolomics

by
Chunmou Wei
1,†,
Jimeng Li
2,†,
Zhongmei Mo
3,
Wei Liu
4,
Dan Zheng
1,
Xueyan Chen
1,
Fulin Li
1,
Mingfeng Tai
2,
Jiaxin Song
5,*,
Changhua Gu
1,* and
Qianqian Zhang
2,*
1
College of Agriculture, Tongren Polytechnic University, Tongren 554300, China
2
Shandong Provincial Key Laboratory of Agricultural Microbiology, College of Plant Protection, Shandong Agricultural University, Tai’an 271018, China
3
College of Agriculture and Forest Engineering and Planning, Tongren University, Tongren 554300, China
4
Key Laboratory of Chemistry in Ethnic Medicinal Resources, School of Ethnic Medicine, Yunnan Minzu University, Kunming 666100, China
5
Institute of Applied Mycology, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Fungi 2026, 12(2), 134; https://doi.org/10.3390/jof12020134
Submission received: 26 December 2025 / Revised: 28 January 2026 / Accepted: 10 February 2026 / Published: 12 February 2026
(This article belongs to the Special Issue Morchella: Innovative Mushrooms from Forests to Farms, Then to Our Tables)
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Abstract

Sclerotium formation represents a critical transition phase in the life cycle of morel, shifting from vegetative growth to dormant structures. The capacity for sclerotium formation directly influences the yield and stability of artificial cultivation. To elucidate the molecular regulatory mechanisms underlying this process, a combined transcriptomics and metabolomics approach was employed to analyze gene expression and metabolite dynamics during sclerotium development of Morchella eximia. A total of 2567 differentially expressed metabolites (DEMs) and 2314 differentially expressed genes (DEGs) were detected, primarily enriched in amino acid metabolism, lipid synthesis, and energy metabolism pathways. Amino acid metabolism facilitates protein synthesis and supplies carbon skeletons, while lipid metabolic networks, particularly de novo fatty acid synthesis from acetyl-CoA precursors, glycerophospholipid metabolism, sphingolipid metabolism, and unsaturated fatty acid biosynthesis, play a central role in sclerotium formation. A regulatory model was constructed, focusing on signal response, transcriptional regulation, nutrient transport and metabolism, morphology transition, lipid accumulation, and membrane system remodeling, demonstrating that lipids not only provide energy storage and membrane structural components for sclerotia but also mediate developmental transitions and environmental adaptation through signaling molecules and regulation of membrane properties. These findings systematically reveal the regulatory network governing morel sclerotium formation at the multi-omics level, with particular emphasis on the central role of lipid metabolism and membrane remodeling. The results offer a theoretical foundation for improving morel cultivation yield and stability through targeted metabolic regulation strategies.

1. Introduction

Sclerotia, which serve as nutritional reserve organs in fungi, typically develop under conditions of nutrient deficiency or environmental stress. They play a crucial role in enabling fungi to withstand adverse environments and complete their life cycles. Among higher fungal taxa, many species within the basidiomycota and ascomycota phyla are capable of forming sclerotia [1,2]. The biogenesis of fungal sclerotia involves intricate environmental signal perception mechanisms, including nutrient and oxidative stress signals, signal transduction pathways, and differential expression of genes encoding key enzymes and structural proteins [3].
In addition to essential nutritional elements for fungal growth, specifically carbon and nitrogen sources that influence sclerotium production [4,5,6], biotic and abiotic stresses can induce sclerotium formation, such as temperature [7,8], pH [9], and ion (calcium and iron cations) [10,11]. Numerous studies have demonstrated a significant correlation between oxidative stress and sclerotium development [12,13,14]. The intracellular accumulation of reactive oxygen species (ROS), hydrogen peroxide (H2O2), and antioxidant enzyme genes plays a key role in sclerotium formation in Polyporus umbellatus [8,15], Pleurotus tuber-regium [16], Sclerotinia sclerotiorum, and Sclerotium rolfsii [17,18].
Oxidative stress signals are transmitted through the mitogen-activated protein kinase (MAPK) pathway, leading to changes in hyphal polar growth and morphogenesis, which ultimately drive sclerotium formation [12,19]. In S. sclerotiorum, the MAPK homolog smk1 promotes sclerotial development and maturation via a pH-dependent signaling pathway [9,20]. In Verticillium dahliae, sclerotial development depends on the regulation of hydrophobin-encoding gene activity by the MAPK signaling pathway [21,22]. In Wolfiporia cocos, several genes associated with MAPK signaling, including those encoding Ras-GTPase, are involved in sclerotial development [19,23,24]. Inhibition of small GTPases, Rho, RAC, or CDC42 family, markedly reduces polarized hyphal growth and suppresses sclerotium formation [25,26].
Cellular signal transduction networks tightly regulate gene expression. Differentially expressed genes (DEGs) encoding enzymes and structural proteins have been identified in multiple sclerotium-forming fungi [27,28]. Research has demonstrated that genes involved in redox homeostasis, such as those encoding cytochrome P450 and choline oxidase, show distinct expression profiles during the mycelium-to-sclerotium differentiation process in fungi [24,27]. Furthermore, genes related to carbohydrate-active enzymes (CAZymes), especially those encoding glycosyl hydrolases (GHs), are highly expressed during the sclerotial differentiation stages of W. cocos [19], and Morchella importuna [29]. Chen et al. (2014) analyzed gene expression level between sclerotia-producing and non-sclerotia-producing isolates of Morchella conica via semi-quantitative RT-PCR, resulting in 67 differential gene fragments [28].
Sclerotium formation is a crucial stage in the life cycle of Morchella species, and is often used in cultivation as an empirical indicator of strain quality [3,30,31,32,33]. However, the underlying mechanisms, regulatory factors, and their relationship to fruiting body yield remain unclear [3,28,29,34]. Recent studies have investigated the impact of reactive oxygen species (ROS) on sclerotium formation [35,36]. For example, Liu et al. (2018) systematically examined factors influencing this process and found that a 20 mM hydrogen peroxide gradient induces superoxide dismutase (SOD) gene expression and activates the MAPK pathway, ultimately promoting sclerotium formation [36]. Building on these findings, the present study employed combined transcriptomic and metabolomic analyses to compare a sclerotium-producing and sclerotium-deficient Morchella eximia strain. This approach enabled comprehensive gene mining related to sclerotial formation and established a preliminary framework for further functional studies on gene regulation in morel sclerotial development.

2. Materials and Methods

2.1. Sample Collection

A multispore isolation process was performed on commercially cultivated Morchella eximia strains, resulting in a high-yielding sclerotium strain (M1) and a strain that did not produce sclerotia on Petri dishes (M8). The isolated strains were preserved on Potato Dextrose Agar (PDA) medium, which consisted of 200 g of potato, 20 g of glucose, 20 g of agar, and 1000 mL of distilled water. Mycelia samples were cultured and collected on PDA medium covered with sterile cellophane at 23 °C for 7 days. Two groups (M1 and M8), comprising a total of 12 samples, were prepared for metabolomic and transcriptomic analyses. Each experiment was repeated three times per group.

2.2. Metabolite Extraction and UHPLC-MS/MS Analysis

For extraction, 25 mg of the mycelium sample was mixed with beads and 500 μL of extraction solution (MeOH:ACN:H2O, v/v = 2:2:1). The mixed samples were vortexed for 30 s, and homogenized for 4 min, then sonicated for 5 min, repeated three times. The mixture was incubated at −40 °C for 1 h to precipitate proteins. Then the samples were centrifuged at 12,000 rpm for 15 min at 4 °C. The supernatant was transferred to a fresh glass vial for further analysis.
UHPLC-MS/MS analyses were performed using a UHPLC system (Vanquish, Thermo Fisher Scientific, Waltham, MA, USA) with a Waters ACQUITY UPLC BEH Amide (2.1 mm × 50 mm, 1.7 μm) coupled to an Orbitrap Exploris 120 mass spectrometer (Orbitrap MS, Thermo). The mobile phase A consisted of 25 mmol/L ammonium acetate and 25 ammonia hydroxide in water, and mobile phase B was acetonitrile. The Orbitrap Exploris 120 mass spectrometer was operated on information-dependent acquisition (IDA) mode in the control of the acquisition software (Xcalibur v 4.4, Thermo) as following: sheath gas flow rate as 50 Arb, Aux gas flow rate as 15 Arb, capillary temperature 320 °C, full MS resolution as 60,000, MS/MS resolution as 15,000, collision energy: SNCE 20/30/40, spray voltage as 3.8 kV (positive) or −3.4 kV (negative), respectively.
The raw data were converted to the mzXML format using ProteoWizard MSConvert (Palo Alto, CA, USA) and processed with an in-house program, which was developed using self-developed R (Tsingke Biotechnology Co., Ltd., Beijing, China) and based on XCMS, for feature detection, extraction, alignment, and integration. The R package (Tsingke Biotechnology Co., Ltd., Beijing, China) and the BiotreeDB (V3.0) were applied in metabolite identification and annotation [37]. The final dataset was scaled and logarithmic transformed using SIMCA18.0.1 software package (Sartorius Stedim Data Analytics AB, Umea, Sweden) for principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and permutation test for OPLS-DA model. The metabolites with a variable importance in projection (VIP) > 1, |Log2 (fold change)| ≥ 1, and p-value < 0.05 (student t test) were considered as significantly differential metabolites. In addition, KEGG (http://www.genome.jp/kegg/, accessed on 17 January 2025) and MetaboAnalyst (http://www.metaboanalyst.ca/, accessed on 17 January 2025) were used for pathway enrichment analysis, which was considered statistically significant when the p-value < 0.05. Visualization of data was performed with R.

2.3. RNA Extraction and Sequencing

Total RNA was extracted and purified using the TRIzol® Reagent (Invitrogen, Carlsbad, CA, USA) extraction kit and the Plant RNA Purification Reagent (Invitrogen) purification kit. After extraction, the integrity of RNA was detected using 1% agarose gel, and the concentration and purity of RNA were measured using a Nanodrop 2000 spectrophotometer (Thermo). Samples for transcriptome sequencing were further subjected to precise detection of RNA integrity using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Samples passing the detection could be used for library construction. Sequencing libraries were constructed and sequenced by Tsingke Biotechnology Co., Ltd. in Beijing, China. The qualified libraries were sequenced on the Illumina Novaseq 6000 platform (Illumina, San Diego, CA, USA) with a PE150 strategy.

2.4. Differential Expression Analysis

Quality of raw data was evaluated using FastQC software v 0.12.0 with default parameters. Then, Trimmomatic v0.33 software [38] was used for filtering and trimming to remove adapter-containing and contaminated reads to obtain clean data. Highly qualified reads were mapped to the reference genome (GeneBank ID: GCA_024713935.1) by HISAT2 v2.2.1 [39], then assembled by StringTie v2.0.4 [40]. Gene expression quantification was applied via StringTie software [40], and standardized expression levels using both FPKM and TPM methods. Principal component analysis (PCA) and the Pearson correlation coefficient were employed to evaluate the biological repeatability of the two groups. The R package DESeq2 v1.34.0 [41] was used to identify the differentially expressed genes (DEGs). Genes with an adjusted p-value < 0.05 and |log2 (fold change)| ≥ 1 were designated as differentially expressed. Gene annotation was performed with the NCBI non-redundant (Nr) database, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) database using the Blast v2.2.31+. GO and KEGG enrichment analysis was performed using clusterProfiler [42]. We annotated protein families and conserved domains by using InterProScan v5.72−103.0 and HMMER v3.4 [43] to search the InterPro [44], Pfam [45] and Swiss-Prot [46] databases. Transcription factors were identified based on conserved domains using the Fungal Transcription Factor Database (FTFD) [47] and the Plant Transcription Factor Database (PlantTFDB) V5.0 [48]. Carbohydrate-active enzymes (CAZymes) were annotated by dbCAN v9.0 [49], HMMER v3.0 [43], and DIAMOND v2.0.11 [50] to blast against CAZyme databases (http://www.cazy.org, accessed on 8 April 2025) [51].

2.5. Quantitative Real-Time PCR (qRT-PCR) Validation

RNA isolation and cDNA synthesis were performed as previously reported [52]. Total RNA was extracted using the phenol/SDS method, and reverse transcribed using HiScript III RT SuperMix (Vazyme, Nanjing, China) according to the protocol of the manufacturer. Then qRT-PCR was carried out in a 96-well optical plate with a CFX Connect Real-Time System (Applied with Bio-Rad, Hercules, CA, USA). The reaction system consisted of 5 μL of SYBR Green Master Mix (AceQTM qPCR, Vazyme), 2.5 nM of each primer, and 1 μL of diluted cDNA in a final volume of 10 μL. CYC3 gene was selected as the reference gene based on our previous experiment [52], and relative gene expression level was analyzed by the 2−∆∆Ct method. Primers used for qRT–PCR are shown in Supplementary Table S1.

3. Results

3.1. Metabolomics Analysis

Both sclerotium-producing (M1) and sclerotium-deficient (M8) Morchella eximia strains demonstrated normal growth (Figure 1a,b). To investigate the mechanisms underlying sclerotia formation, transcriptomic and metabolomic analyses were conducted.
PCA (Figure 2a) and OPLS-DA analysis (Supplementary Figure S1) showed that metabolites among the two groups formed distinct clusters, indicating that normal and defective-sclerotium formation strains have different characteristic metabolites. Permutation test was conducted to avoid the over-fitting problem and to evaluate the statistical significance of the OPLS-DA model (Supplementary Figure S2). A total of 2567 metabolites were annotated, of which 2207 compounds were classified into 13 superclasses, 143 classes, and 368 subclasses. According to the superclass, it can be divided into 13 categories: organoheterocyclic compounds, organic acids and derivatives, lipids and lipid-like molecules, benzenoids, organic oxygen compounds, phenylpropanoids and polyketides, nucleosides, nucleotides, and analogs, organic nitrogen compounds, alkaloids and derivatives, organosulfur compounds, lignans, neolignans and related compounds, hydrocarbon derivatives, and organohalogen compounds. Organoheterocyclic compounds accounted for the highest proportion with 590 compounds, while organohalogen compounds had the least abundant with only 1 compound (Figure 2b, Supplementary Table S2).
Metabolites with VIP scores ≥ 1, |Log2 (fold change)| ≥ 1, and p-values < 0.05 were identified as DEMs through differential expression analysis and OPLS-DA analysis. A total of 741 DEMs were screened, including 272 up-regulated and 469 down-regulated metabolites (Figure 2c and Supplementary Table S3). The heatmap of DEMs is shown in Supplementary Figure S3, and the top 20 up- and down-regulated DEMs are shown in Figure 2d. KEGG enrichment analysis of the DEMS showed that DEMS were mainly enriched in pathways involving metabolism, biosynthesis of secondary metabolites, membrane transport, amino acid metabolism, carbohydrate metabolism, and protein digestion and absorption (Figure 3a,b, and Supplementary Table S4).

3.2. Differentially Expressed Genes Analysis

Transcriptome analysis was performed to compare a sclerotium-forming culture (M1) with a non-sclerotial strain (M8) of M. eximia. A total of 46.04 Gb clean data were generated with Q20 ≥ 99.33% and Q30 ≥ 97.79% after filtering the adapters and low-quality bases (Supplementary Table S5). The PCA results revealed a clear separation between the two groups, with the three biological replicates clustering closely together, indicating variation at two distinct stages (Figure 4a). Furthermore, the strong correlation among the replicates reinforces the reliability of our data (Supplementary Figure S4). Overall, these findings confirm that the quality of the transcriptomes is adequate for further analysis.
A total of 1059 genes were up-regulated, and 1255 genes were down-regulated (Figure 4b and Supplementary Table S6), with the heatmap of differentially expressed genes (DEGs) shown in Figure 4c. Pathway analysis indicated that a large number of DEGs were enriched in the extracellular region (GO:0005576) and oxidoreductase activity (GO:0016491) (Supplementary Table S7 and Supplementary Figure S5). DEGs predominantly enriched in lipid metabolism, biosynthesis of secondary metabolites, cell cycle-yeast, carbon metabolism, and biosynthesis of amino acids (Figure 4d, Supplementary Table S8, and Supplementary Figure S6). This indicates significant changes in amino acid and bioactive compound synthesis during sclerotium development.
Conservative domain analysis revealed the most significant differential expression in Ankyrin repeat (ANK), Zinc finger superfamily, Protein kinase domain, Major facilitator superfamily (MFS), WD40 domain, F-box-like domain, Fungal specific transcription factor domain (FSTFD), Short-chain dehydrogenases reductases (SDR) family, Tetratricopeptide repeat (TPR), CorA-like Mg2+ transporter protein, and SNF2 family (Supplementary Table S9). Among them, ANK, FSTFD, TPR, CorA-like Mg2+ transporter protein, and SNF2 were mostly down-regulated. These gene families are associated with protein regulation, activation, interaction, and transport, indicating that they are actively involved in the sclerotium development process.

3.3. Nutrition Utilization and Stress Response

Transcription factors (TFs) play a central role in gene regulation. In total, 269 TFs were identified in this study, with 71 differentially expressed genes (DEGs) classified as TFs (Supplementary Table S10). The five most abundant transcription factor families are presented in Figure 5a, with C2H2 (31.6%) and C6 (29.7%) domains being predominant. The C2H2 domain is primarily associated with fungal environmental responses, development, and morphogenesis. The C6 type is a fungal-specific class of TFs extensively involved in the regulation of primary metabolism, secondary metabolite synthesis, and cell division. During sclerotium formation, C6 exhibited the highest number of up-regulated genes (18 out of 49). Functional annotation of homologous proteins indicated that these transcription factors are mainly involved in nitrogen metabolism, stress response, developmental regulation, and morphogenesis (Supplementary Table S11). These findings suggest that the identified transcription factors participate in signal response, nutrient uptake, and morphological development during sclerotium formation.
The formation of sclerotia is closely linked to nutritional conditions, with nutrient absorption and utilization involving the degradation and conversion by various enzymes. How morels acquire nutrients for sclerotia growth warrants exploration. CAZyme plays a crucial role in the degradation of lignocellulose [53], continuously supplying carbohydrates and nutrients for sclerotia formation. A total of 395 cazymes were annotated in the M. eximia, with 83 showing significant differential expression (M1 vs. M8: 53 down-regulated DEGs, 30 up-regulated DEGs). During sclerotium formation, genes from the GH6, AA11, GH5, and GH3 families exhibited significant expression (Supplementary Table S12). These CAZyme coding genes participate in the degradation of various plant/fungal cell wall components, such as lignin, cellulose, pectin, chitin, and glucan (Figure 5b). This broad degradative capacity facilitates the provision of nutrients to sclerotia under nutrient-poor conditions.
In the sclerotium formation process, D1AGene06250 (PF01055.30) showed the highest expression. It is annotated as an alpha-1,4-glucan lyase (EC 4.2.2.13), a member of the glycosyl hydrolase family 31, which degrades glycogen, starch, and maltose oligosaccharides through elimination reactions, producing 1,5-anhydro-D-fructose [54]. Although CAZyme annotation does not classify this gene within the GH family (Supplementary Table S12), conserved domain analysis indicates that its N-terminal domain belongs to the GH31_N family, supporting its classification within GH31 [54]. The second highest expressed CAZyme gene was D1AGene03426, annotated as a GH6 family member with a CBM1 domain involved in cellulose degradation.
Stress response signals triggered by environmental signals can initiate sclerotium formation. We identified several heat shock protein (HSP) genes associated with stress responses, which function by stimulating the ATPase activity of heat shock proteins, including Hsp70 protein encoding genes (D1AGene08774, D1AGene00593, D1AGene08696, D1AGene04977), and Hsp9/12 protein encoding genes (D1AGene04220, D1AGene00344, D1AGene06943, D1AGene00428), which were significantly up-regulated in sclerotia-forming strain (M1). This suggests that high expression of stress-related signaling genes may participate in regulating morel sclerotia formation (Supplementary Table S9). The MAPK signaling pathway has been confirmed to be significantly associated with sclerotium development and maturation in several species. In this study, genes belonging to the MAPK-related small GTPase subfamily, specifically the ARF/Rho/Ras/Rab family, exhibited high expression during the sclerotium formation stage (Supplementary Table S9).

3.4. Determination of Lipogenic Pathways

KEGG co-enrichment analysis of DEGs and DEMs revealed multiple lipid synthesis and metabolism pathways activated during sclerotium formation, including unsaturated fatty acid biosynthesis, glycerophospholipid metabolism, fatty acid biosynthesis, glycerolipid metabolism, ether lipid metabolism, and sphingolipid metabolism (Supplementary Table S13). In the metabolic profile of the M1, we detected the accumulation of various fatty acids, such as cis-11-eicosenoic acid (20:1n-9), nervonic acid (24:1n-9), cis-11,14-eicosadienoic acid, α-linolenic acid (ALA), γ-linolenic acid, and palmitic acid derivatives 16-hydroxypalmitic acid and 2-hydroxypalmitic acid (Supplementary Table S3). Concurrently, genes involved in fatty acid synthesis were significantly up-regulated (Figure 6a), including gene D1AGene10661, D1AGene10091, and D1AGene10089 encoding acetyl-CoA carboxylase (ACC); D1AGene06355 encoding fatty acid synthase (FASN); D1AGene09319 encoding fatty acid elongase (ELOVL); D1AGene03521 and D1AGene01042 encoding stearoyl-CoA desaturase (SCD) and fatty acid desaturase (FADS), respectively. Lipids in morel sclerotia are typically stored as triacylglycerols (TAGs) within oil droplets (lipid droplets, LDs) to prevent cellular toxicity from excessive free fatty acid accumulation. Transferase and phosphatase genes involved in TAG biosynthesis, such as D1AGene04967, D1AGene06509, D1AGene07525, and D1AGene10752, show significant expression during sclerotium formation.
Additionally, we detected active expression of the phospholipid metabolic network during sclerotium formation (Figure 6a). Core precursors and intermediates of phospholipid biosynthesis, such as sn-glycerol-3-phosphate and 1-Palmitoyllysophosphatidate, are direct precursors for phosphatidic acid (PA) synthesis. More importantly, multiple structurally distinct phospholipid compounds were identified, including phosphatidic acids, phosphatidylcholine (PC), and phosphatidylethanolamine (PE) molecules (Supplementary Table S3). PC, PE, and sterols are the most abundant phospholipids in fungal cell membranes, and their synchronous accumulation indicates active membrane system biosynthesis during sclerotium formation. Concurrently, enrichment of phosphatidylinositol derivatives was observed. These inositol phospholipids are not only membrane structural components but also play central roles in cellular signal transduction and vesicular trafficking, the latter being crucial for the morphogenesis of complex structures like sclerotia. To validate the expression patterns of these lipid synthesis genes, we randomly selected three differentially expressed genes for qRT-PCR analysis. The expression patterns of the selected genes were consistent with their FRKM values in the transcriptomic data (Figure 6b), confirming the accuracy and reliability of the transcriptomic data.

4. Discussion

Despite significant advances in research on M. eximia, the physiological and molecular mechanisms regulating sclerotium formation remain poorly understood [3,33,34,55]. Addressing this knowledge gap is essential for optimizing cultivation strategies, as these mechanisms are fundamental to achieving high and stable yields. Sclerotium formation constitutes a pivotal stage in the morel life cycle. Sclerotia are generated through repeated hyphal branching and node expansion; however, due to their atypical structure, they are classified as pseudosclerotia [31,55,56]. The present study utilized integrated metabolomic and transcriptomic analyses to delineate the molecular regulatory framework underlying sclerotium formation in M. eximia.
Metabolomics identified a total of 2567 metabolites, primarily comprising organoheterocyclic compounds, organic acids and derivatives, lipids and lipid-like molecules (Figure 2b, Supplementary Table S2). Organoheterocyclic compounds often serve as secondary metabolites or key cofactors in fungi [57]. Metabolites significantly up-regulated in sclerotium formation samples were mainly involved in the fine-tuning of photo/oxidative protection, signal transduction, and metabolic reprogramming. Oxidative stress promotes sclerotium formation, while signal transduction and cell cycle reprogramming assist the adaptation from growth to dormancy. Organic acids are central components of biosynthesis and metabolism [58,59]. This study reveals that central metabolic pathways are strongly activated, generating fumaric acid, malic acid, amino acids (glutamic acid, aspartic acid, serine, etc.), and peptides, accompanied by substantial ATP production. This indicates that sclerotium formation involves extensive protein degradation and resynthesis, with highly active energy metabolism and carbon skeleton supply, providing precursors for the synthesis of lipids, nucleic acids, and other substances. Lipids serve not only as crucial energy storage molecules but also participate in cell membrane construction and membrane signaling [60,61]. Previous studies have documented the accumulation of fats as high-energy substances within the sclerotia and mycelium cells of Morchella species [62]. The identification of numerous lipids and lipid-like molecules in this study suggests that sclerotium formation involves the remodeling of cell membrane lipids and the storage of energy.
A total of 2314 DEGs were identified (Figure 4b), genes associated with sclerotia formation are primarily enriched in Protein families: signaling and cellular processes, Lipid metabolism, Transcription factors, and Peroxisome (Figure 4d). Conservative domain analysis revealed that gene families involved in protein regulation, activation, interaction, and transport were relatively activated in the sclerotium development process (Supplementary Table S9). C2H2 and C6 transcription factors (TFs) participate in signal response, nitrogen metabolism and uptake, and morphological development (Figure 5a, Supplementary Table S11), while the abundant expression of cell wall degradation enzymes (CWDEs) enables fungi to seek nutrients under unfavorable conditions and provides the basis for the morphological transition from mycelium to sclerotia (Figure 5b).
A regulatory conceptual model for morel sclerotium formation (Figure 7) was proposed, aiming to provide insights for future elucidation of the molecular mechanisms underlying sclerotium development.
  • External and internal stimuli trigger sclerotia initiation
The transition of fungi from vegetative to reproductive growth depends on sensing and responding to external environmental signals. The formation of fungal sclerotia is a tightly regulated developmental process that involves multiple levels of signal perception, transmission, and regulation. Exogenous factors influencing this process include nutrients and culture substrates such as ion, carbon and nitrogen sources, which can either stimulate or inhibit the formation of fungal sclerotia. Differences in fungal sclerotia formation induced by various nutrient sources may be related to variations in genetic backgrounds and metabolic pathways among different fungi [4,5,6]. Environmental conditions including temperature, humidity, pH, light, oxygen, and oxidative stress could increase the internal oxidative stress to trigger sclerotial biogenesis [7,8,9,10]. In some cases, host plant participation is also required for sclerotia formation [63]. Endogenous factors comprise various signaling proteins and pathways, including the MAPK signaling pathway, protein kinases, and GTPases [9,12,19,25,26]. In S. sclerotiorum, activation of a mitogen-activated protein kinase (MAPK) is required for sclerotial development and maturation [9], while addition of cyclic AMP (cAMP) inhibited MAPK activation and sclerotial development [20]. Small GTPases are key signal transducing enzymes that regulate polar growth, cytoskeleton morphogenesis, generation of reactive oxygen species (ROS) and formation of sclerota in filamentous fungi. A recent study has confirmed that inhibition of Rho/Rac/CDC42 family GTPases significantly reduces polarized hyphal growth and sclerota production [25,26]. Furthermore, GTPase family (such as Arf and Ras) serves as key regulators of multiple intracellular pathways, including hyphal growth, membrane/protein trafficking and virulence [64].
This study revealed that multiple gene families involved in protein regulation, activation, interaction, and transport were activated during sclerotial development. Additionally, genes from the small GTPase subfamily including Rho, Ras, Rab, and Arf, exhibited differential expression, consistent with previous research [28]. This family typically participates in processes such as cell polarization, vesicle trafficking, cytoskeleton reorganization and ROS generation in eukaryotic cells. Therefore, small GTPase may regulate oxidative stress, such as ROS production, or control polarized hyphal growth, thereby facilitating the morphological transition from hyphae to sclerotia. In summary, these differentially expressed genes collectively constitute a complex regulatory network that may integrate internal and external signals to precisely control the differentiation, maturation, and subsequent dormancy or germination processes of sclerotia, leading to morphological characteristics like sclerotial texture and size.
  • Transcriptional regulatory networks launch sclerotia morphogenesis
Many fungi are capable of producing sclerotia, and advances in multi-omics technologies have facilitated the investigation of transcriptional regulatory networks underlying sclerotia morphogenesis [2]. Transcription factors are widely distributed in fungi and regulate diverse biological processes, including growth, development, spore formation, morphogenesis, and stress responses. Sclerotia formation is a complex process in which multiple activities, such as gene expression and metabolism, are regulated by transcription factors. In V. dahliae, the MADS-box transcription factor VdMcm1 regulates microsclerotia formation, conidiation, virulence, and secondary metabolism [65]. The APSES transcription factor Vst1 negatively regulates G-protein/cAMP signaling and positively regulates MAPK and Rho signaling, thereby influencing microsclerotium development [66]. In Metarhizium rileyi, microsclerotia formation is regulated by bZIP [67] and C2H2 [68] transcription factors.
The present study demonstrates that fungal transcription factors are significantly expressed during M. eximia sclerotia formation. The five most highly expressed transcription factor domains include two C2H2 TFs associated with nitrogen metabolism, two bHLH TFs, and one C6 transcription factor. C2H2 domains are widely distributed among plants, animals, and fungi, and represent one of the largest classes of DNA-binding factors involved in transcription. bHLH TF contain a core helix-loop-helix domain, and are commonly involved in growth, development, signal transduction, and stress responses. The C6 domain is a zinc finger TF unique to fungi. Previous reports indicate that C6 transcription factors in Morchella sextelata are associated with heat stress responses [69]. In this study, transcription factors regulate sclerotium morphogenesis by modulating carbohydrate metabolism, including nitrogen metabolism, gene transcription activation, and signal responses such as pH response.
The functions and mechanisms of transcription factors in fungal sclerotium development require further investigation. As more fungal genomes are sequenced, the annotation of transcription factors will become increasingly comprehensive. Sclerotium formation is regulated by interactions within signaling pathway networks rather than by single signals. Additional research is necessary to identify both conserved and species-specific pathways across fungi. Functional analysis of transcription factors through omics annotation and mutant construction will further elucidate the transcriptional regulatory mechanisms involved in sclerotium development.
  • Material and energy foundations for sclerotia development
Nutritional availability is a critical determinant of sclerotia formation. Previous studies have demonstrated that sclerotia typically develop under nutrient deprivation or environmental stress, indicating that fungi must efficiently acquire and utilize nutrients to support sclerotial development. Integrated transcriptomic and metabolomic analyses revealed co-enrichment in pathways related to lipid, carbohydrate, amino acid, and energy metabolism (Supplementary Table S12), consistent with previous findings [29]. These metabolism processes provide the material and energy foundation for fungal growth, reproduction, and environmental adaptation.
During sclerotia formation, substantial quantities of nutrients are absorbed, degraded, and utilized. Cell wall-degrading enzymes (CWDEs), particularly carbohydrate-active enzymes (CAZymes), are essential for breaking down complex carbohydrates in the cell wall and other substrates, thereby facilitating nutrient mobilization for sclerotia development. The primary constituents of sclerotia in certain fungi, such as W. cocos [19] and P. tuber-regium [70], are linear polysaccharides, predominantly β-glucans. In these species, CAZyme family genes including GH15, GH16, GH18, and GH72, which encode enzymes for glucan hydrolysis and chitinase activity, as well as glucan synthases, collectively contribute to the conversion of complex substrates such as wood into soluble sugars, providing carbon sources and energy for sclerotium development [19].
In alignment with previous research, the present study identified 53 genes across 37 CAZyme families that are significantly up-regulated during sclerotium formation (Supplementary Table S12). These enzyme genes are involved in the degradation of diverse substrates, including lignin (e.g., AA3, AA11, AA1), cellulose, hemicellulose, and glucans (e.g., GH31, GH6, GH65, GH51, GH47, GH27, CE1, GH5), pectin (e.g., PL1, PL4), chitin (e.g., CE4, CBM18), and starch (e.g., GH31, GH63). The concurrent up-regulation of these substrate-specific CAZymes suggests that, under nutrient-limited or environmentally stressed conditions, morels may maintain carbohydrate and nutrient supplies for sclerotium formation by synergistically utilizing multiple available substrates.
Liu et al. (2019) [29] compared the transcriptomes of three stages of M. importuna: vegetative mycelia (VM), initial sclerotia (IS), and mature sclerotia (MS). They observed significant gene expression differences between the VM stage and both sclerotium development stages, while differences between the IS and MS stages were relatively minor. Two glycogen/starch degradation domain, a CBM48 and an alpha-1,4-glucan lyase (annotated as CH31 family by conserve domain analysis), were highly expressed in VM stages, and down-regulated at the SI and SM stages [29]. In this study, the highest expression of alpha-1,4-glucan lyase gene (D1AGene06250, GH31) was also detected, although its function in sclerotium development requires further validation. Additionally, the cellulose-degrading gene GH6 (D1AGene03426) was highly expressed. However, GH6 was absent in sclerotium-formation fungi, such as W. cocos [19] and Tuber melanosporum [71]. Its specific expression in morel suggests that it may provide an essential carbohydrate source for sclerotium development.
Furthermore, a glycosyl hydrolase family 16 member (PF00722.25) with an incompletely characterized function was significantly up-regulated and classified within the glucanases subgroup. Another up-regulated gene encodes a glycosyl hydrolase family 3 C-terminal domain (PF01915.26), which is predicted to possess catalytic activity and may be associated with β-glucan binding. Beyond carbohydrate metabolism, genes related to the transport and metabolism of amino acids, nucleotides, lipids, inorganic ions, and energy were also activated. These include mycolic acid cyclopropane synthetase (PF02353.24), flavin containing amine oxidoreductase protein (PF01593.28), permease family protein (PF00860.24), carboxylesterase family protein (PF00135.32), and flavin-binding monooxygenase-like protein (PF00743.23), all of which were up-regulated. Genes encoding major facilitator superfamily (MFS) transporters, which mediate cross-membrane transport of carbohydrates, amino acids, and ions, as well as genes involved in vesicular transport, also showed significant up-regulation. In summary, the formation of M. eximia sclerotia is characterized by active metabolism and cross-membrane transport of multiple nutrients, including carbohydrates, amino acids, and ions. These processes collectively support the structural development and energy storage of sclerotia.
  • Lipid biosynthesis and core substance accumulation
Lipids function as essential signaling molecules in signal transduction, cellular regulation, and environmental adaptation. Fungi adapt to environmental stress by modifying the saturation of membrane lipids, increasing the proportion of unsaturated fatty acids such as linoleic acid and linolenic acid to enhance membrane fluidity. Certain polyunsaturated fatty acids (PUFAs) also possess free radical scavenging activity, which supports resistance to oxidative damage. Tan et al. (2019) reported that lipids serve as carbon sources for M. importuna during the decomposition of exogenous nutrient bags (ENB), accompanied by elevated lipase activity, and continuous synthesis and accumulation of crude fat and triacylglycerol (TAG) [72].
Previous studies have shown that lipids are the primary components of morel sclerotia [62], whereas in other fungi, sclerotia mainly consist of linear polysaccharides, predominantly β-glucans [24,63,70]. Notably, comparative analysis revealed that genes related to lipid biosynthesis, secondary metabolites related to disease, and the melanogenesis pathway are highly expressed during the mycelium stage of M. importuna, with a continuous decline during the sclerotium stage. These findings suggest that decomposition and utilization of exogenous substances primarily occur during the mycelia growth, while internal energy metabolism is central to sclerotial development [29].
In the present study, metabolomics identified an enrichment of lipids and lipid-like molecules (360, 14.02%) (Figure 2b), and genes involved in fatty acid synthesis were significantly up-regulated (Figure 6a). KEGG co-enrichment analysis of DEGs and DEMs revealed activation of multiple lipid synthesis and metabolism pathways during sclerotium formation, including unsaturated fatty acid biosynthesis, fatty acid biosynthesis, and glycerolipid metabolism. Glycerolipids (GL), including mono-, di-, and trisubstituted glycerols, are primarily represented by TAGs. To prevent cellular lipotoxicity from excessive free fatty acids, fungi store fatty acids in the form of TAGs within cytoplasmic lipid droplets (LDs). Lipid droplets not only store lipids but also supply energy through interactions with peroxisomes and mitochondria. For example, stored TAGs undergo lipolysis to release fatty acids, which are then activated and transported into mitochondria for β-oxidation, resulting in acetyl-CoA production and subsequent energy generation via the tricarboxylic acid (TCA) cycle.
The sclerotial development process also showed up-regulation of short-chain dehydrogenase/reductase (SDR) (Supplementary Table S9), a superfamily of NAD (P)-dependent oxidoreductases and related enzymes that play essential roles in the metabolism of lipoids, amino acids, carbohydrates, and steroid hormones, as well as in cellular signaling pathways. Lipid synthesis is fundamental for sclerotia material accumulation, enabling fungi to survive environmental stress through dormancy and resume growth under favorable conditions.
  • Cellular membrane remodeling and structural maturation
The hard outer shell of sclerotia is essential for enduring harsh environments and prolonged dormancy, relying on extensive synthesis and modification of cell walls and membranes. Specifically, the cell wall thickens and densifies through the synthesis of chitin and glucan, while the lipid composition of the cell membrane is altered to improve stability and barrier function. Lipids fulfill multiple critical roles in fungal life cycles: phospholipids (such as phosphatidylcholine PC, phosphatidylethanolamine PE, and phosphatidylglycerol PG) and sterols (especially ergosterol), are major components of cell membranes and organelle membranes, maintaining membrane fluidity and integrity. Sphingolipids regulate cell cycle progression, stress responses, programmed cell death, and pathogenicity. Phosphatidic acid, a key precursor for phospholipid synthesis, mediates processes including membrane transport and cytoskeletal reorganization.
Integrated analysis of DEGs and DEMs indicated enrichment in the glycerophospholipid metabolism, ether lipid metabolism, and sphingolipid metabolism pathway. Glycerophospholipids, ether lipid, and sphingolipid are essential components of eukaryotic cellular membranes, contributing to membrane structure, signaling, and metabolism. They provide stability, fluidity, and permeability to membranes, and support the function of membrane proteins, receptors, and ion channels. Glycerophospholipids are broadly classified into PC, PE, phosphatidylserine (PS), phosphatidylinositol (PI), PG, and cardiolipin. Free fatty acids such as α-linolenic acid, γ-linolenic acid, and cis-11-eicosenoic acid may function as signaling molecules regulating developmental processes and/or be incorporated into membrane phospholipids, thereby modifying physicochemical properties and enhancing sclerotia tolerance to stresses.
  • Mature sclerotia confer adversity tolerance
In morel species, sclerotia act as nutrient storage organs capable of withstanding oxidative stress, drought, elevated temperatures, ultraviolet radiation, and microbial infection, which likely improves overwintering survival rates. The biogenesis of fungal sclerotia is triggered by biotic or abiotic stresses, with cells responding to environmental challenges that disrupt homeostasis and initiate adaptive responses. One of the earliest and most conserved responses is the activation of the heat shock response, which induces transcription of heat shock proteins (HSPs), molecular chaperones involved in protein folding, assembly, and degradation. The up-regulation of heat shock protein-coding genes, such as hsp70 and hsp9/12, is associated with stress response and likely assists cells with coping with internal pressures and environmental stresses during development, thereby increasing the stress tolerance of the developing sclerotia. The specific role of heat shock proteins in morel sclerotium formation requires further validation.
Accumulation of secondary metabolites, including fungal pigments, enhances the stress resistance of sclerotia. Consequently, sclerotia maintain metabolic dormancy under arid, extreme temperatures, and nutrient-deficient conditions, resuming growth only when the environment becomes favorable. This process represents an evolutionary survival strategy developed by fungi, demonstrating their ability to adapt to environmental stress through complex cellular restructuring. Despite these advances, the biosynthetic mechanisms underlying pigment production in sclerotia remain poorly characterized. Recent studies have shown that V. dahliae forms darkly pigmented resting structures known as sclerotia, with dihydroxynaphthalene (DHN)-melanin as the responsible pigment. Deletion mutants of genes involved in DHN-melanin biosynthesis exhibit impaired melanin production and sclerotia development [73]. Similarly, the gray to black coloration of Botrytis cinerea sclerotia results from melanin accumulation [74].
Several genes related to secondary metabolite synthesis are up-regulated in sclerotia. For instance, two highly expressed multicopper oxidase (PF07731.18) genes belong to the SufI family, which includes cell division protein FtsP and spore coat protein CotA. These proteins are primarily involved in cell cycle regulation, cell division, chromosome partitioning, inorganic ion transport and metabolism, and cell wall, membrane, or envelope biogenesis. CotA is a highly thermotolerant laccase incorporated into the spore coat to confer resistance to hydrogen peroxide and ultraviolet radiation [75]. Previous studies have shown that laccase genes are involved in melanin synthesis [76,77]. Morel sclerotia are initially light white and darken to yellowish-brown as they mature. Whether this color change is linked to pigment accumulation remains unclear. Future research should examine the physiological and biochemical differences in fungal pigment production and examine whether pigment synthesis is governed by common or species-specific mechanisms.

5. Conclusions

This study systematically elucidates the molecular regulatory network underlying sclerotium formation in Morchella eximia by integrating transcriptomic and metabolomic analyses. The results demonstrate that sclerotium development involves complex transcriptional and metabolic reprogramming. Genes related to matrix degradation, protein synthesis, amino acid metabolism, and stress responses are significantly up-regulated, while central carbon metabolism and lipid synthesis pathways are strongly induced, as evidenced by the activation of the TCA cycle and the accumulation of various fatty acids and phospholipids. Integrated transcriptomic and metabolomic analyses further suggest that morel sclerotia development represents a complex morphogenesis process through which fungi respond to environmental stress to ensure survival and reproduction. This process encompasses environmental perception and response, transcriptional regulation, and morphogenesis, as well as nutrient conversion and absorption to provide energy. Ultimately, it results in the formation of a nutrient storage organ capable of enduring harsh environments and achieving long-term dormancy. The regulatory model proposed in this study provides a theoretical framework for advancing the understanding of morel sclerotia development mechanisms and offers a basis for optimizing sclerotia formation through metabolic engineering to improve the yield and stability in artificial cultivation.
The formation of sclerotia has been identified as a synergistically regulated signaling network. Therefore, further research, including mutant construction and omics data annotation, is required to identify both conserved and species-specific pathways across fungi. Collectively, these data can provide a more precise and comprehensive understanding of the signaling transduction and reprogramming events that occur during fungal sclerotia development.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jof12020134/s1, Figure S1: OPLS-DA analysis of metabolites composition for M1 VS. M8; Figure S2: Permutation test of OPLS-DA model for M1 VS. M8; Figure S3: Heatmap of DEMs among M1 VS. M8; Figure S4: Pearson correlation analysis between all transcriptome samples; Figure S5: The top 20 enriched GO terms of DEGs; Figure S6: DEGs number of the most enriched Pathway; Table S1: List of primers used in qRT-PCR; Table S2: Qualitative and quantitative results of all metabolites; Table S3: Differential expression metabolites in M1vs. M8; Table S4: KEGG pathways enrichment list of DEMs; Table S5: Summary of RNA-seq data quality for all samples; Table S6: Differential expression genes (DEGs) in M1 vs. M8; Table S7: List of GO enrichment of DEGs in M1 vs. M8; Table S8: List of KEGG enrichment of DEGs in M1 vs. M8; Table S9: Domain analysis of significantly differentially expressed genes; Table S10: Llist of TFs family from all samples; Table S11: List of up-regulated TFs in sclerotium formation samples; Table S12: Cazyme annotation of DEGs; Table S13: List of co-enriched KEGG pathways of DEMs and DEGs in M1 vs. M8.

Author Contributions

Conceptualization, Q.Z. and C.G.; methodology, C.W. and J.L.; software, J.S., Q.Z.; validation, M.T.; formal analysis, Q.Z.; investigation, Z.M.; data curation, D.Z., X.C. and F.L.; writing—original draft preparation, C.W. and J.L.; writing—review and editing, Q.Z., W.L. and J.S.; supervision, Q.Z.; project administration, C.G.; funding acquisition, C.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Artificial Domestication and Experimental Demonstration of Wild Edible Fungi in the Wuling Mountain Region (grant number: 2024QNJ-02), Research on Key Technologies for Breeding and Domestication of Morchella Species (grant number: 2023TSKY-64), Mushroom Technology System of Shandong Province (grant number: SDAIT-07-06), and Shandong Provincial Key Research and Development Program (Rural Revitalization Science and Technology Innovation Boost Action Plan) Project (grant number: 2024TZXD075).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The RNA-seq data in this study have been deposited in the NCBI repository, accession number PRJNA1393881. Metabolomic data have been deposited in the CNCB (China National Center for Bioinformation) database under the BioProject ID of PRJCA054534 (BioSample accession: subSAM165263).

Acknowledgments

We acknowledge the Guizhou Provincial Higher Education Food and Medicinal Fungi Engineering Research Center for providing the original commercially cultivated strains used in this study. We sincerely thank the following colleagues for their valuable support: Chunye Mou, Rongjian He, and Wanyan Feng (Tongren Polytechnic University) for their technical assistance in figure preparation; Luhan Chen and Xinzhe Liu (Shandong Agricultural University) for their careful work in verifying the accuracy of the transcriptome quantification data.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The cultural characteristics of sclerotia-producing (M1) and non-sclerotia-producing (M8) isolates of Morchella eximia. (a) The morphological characteristics of M. eximia isolates grow in a PDA plate. (b) The mycelial growth rate of M. eximia isolates.
Figure 1. The cultural characteristics of sclerotia-producing (M1) and non-sclerotia-producing (M8) isolates of Morchella eximia. (a) The morphological characteristics of M. eximia isolates grow in a PDA plate. (b) The mycelial growth rate of M. eximia isolates.
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Figure 2. Metabolomics analysis between sclerotia-producing (M1) and non-sclerotia-producing (M8) isolates of M. eximia. (a) PCA analysis of metabolite composition. (b) Classification of all metabolites, percentages do not sum to 100% due to rounding to two decimal places. (c) Volcano plot of the DEMs among M1 vs. M8. (d) Top 20 DEMs among M1 vs. M8.
Figure 2. Metabolomics analysis between sclerotia-producing (M1) and non-sclerotia-producing (M8) isolates of M. eximia. (a) PCA analysis of metabolite composition. (b) Classification of all metabolites, percentages do not sum to 100% due to rounding to two decimal places. (c) Volcano plot of the DEMs among M1 vs. M8. (d) Top 20 DEMs among M1 vs. M8.
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Figure 3. KEGG enrichment analysis between sclerotia-producing (M1) and non-sclerotia-producing (M8) isolates of M. eximia. (a) KEGG enrichment analysis of the DEMs among M1 vs. M8. (b) The top 20 enriched KEGG pathways with the most DEMs, different font colors represent different KEGG pathway maps.
Figure 3. KEGG enrichment analysis between sclerotia-producing (M1) and non-sclerotia-producing (M8) isolates of M. eximia. (a) KEGG enrichment analysis of the DEMs among M1 vs. M8. (b) The top 20 enriched KEGG pathways with the most DEMs, different font colors represent different KEGG pathway maps.
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Figure 4. Transcriptomics analysis between sclerotia-producing (M1) and non-sclerotia-producing (M8) isolates of M. eximia. (a) PCA analysis of gene expression profiles of all the samples. (b) Volcano plot of the DEGs. (c) Heatmap of DEGs in M1 vs. M8. (d) KEGG enrichment analysis of the DEGs in M1 vs. M8.
Figure 4. Transcriptomics analysis between sclerotia-producing (M1) and non-sclerotia-producing (M8) isolates of M. eximia. (a) PCA analysis of gene expression profiles of all the samples. (b) Volcano plot of the DEGs. (c) Heatmap of DEGs in M1 vs. M8. (d) KEGG enrichment analysis of the DEGs in M1 vs. M8.
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Figure 5. Transcription factors and CAZyme analysis. (a) The distribution of the top 5 TF families. Black represents total TFs, orange represents differential expressed TFs. (b) DEGs coding for CAZymes involved in decomposition of plant/fungal cell wall polysaccharides. Abbreviation: lpmo: lytic polysaccharide monooxygenase; b-gluc: β-glucan; b-1,3-gluc: β-1,3-glucan; a-gluc: α-glucan; a-man: α-mannan; cell: cellulose; hemi: hemi cellulose; lign: lignin; star: starch; chit: chitin.
Figure 5. Transcription factors and CAZyme analysis. (a) The distribution of the top 5 TF families. Black represents total TFs, orange represents differential expressed TFs. (b) DEGs coding for CAZymes involved in decomposition of plant/fungal cell wall polysaccharides. Abbreviation: lpmo: lytic polysaccharide monooxygenase; b-gluc: β-glucan; b-1,3-gluc: β-1,3-glucan; a-gluc: α-glucan; a-man: α-mannan; cell: cellulose; hemi: hemi cellulose; lign: lignin; star: starch; chit: chitin.
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Figure 6. Analysis and qRT-PCR validation of lipid metabolism during the sclerotium formation process of M. eximia. (a) RNA-seq heatmap showing the DEGs related to lipid biosynthesis. (b) qRT-PCR validation of DEGs that are involved in lipid synthesis in M. eximia. The bar graphs present the results of the qRT-PCR, and the line graphs present the RNA-seq results. The scale on the left axis represents the relative expression level, and the scale on the right axis represents the FPKM value. Data are the means ± SD of three biological replicates.
Figure 6. Analysis and qRT-PCR validation of lipid metabolism during the sclerotium formation process of M. eximia. (a) RNA-seq heatmap showing the DEGs related to lipid biosynthesis. (b) qRT-PCR validation of DEGs that are involved in lipid synthesis in M. eximia. The bar graphs present the results of the qRT-PCR, and the line graphs present the RNA-seq results. The scale on the left axis represents the relative expression level, and the scale on the right axis represents the FPKM value. Data are the means ± SD of three biological replicates.
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Figure 7. Model summarizing influencing factors and molecular mechanisms proposed to be involved in sclerotial development. To better elucidate key factors in sclerotia development, the figure integrates the fungal sclerotium formation factors and molecular mechanisms reported in the literature, labeled as FS: fungal sclerotium formation. The molecular mechanism of morel sclerotium formation proposed in this study is marked with differently colored boxes, labeled as MS: morel sclerotium formation. The arrow indicates positive regulatory effects.
Figure 7. Model summarizing influencing factors and molecular mechanisms proposed to be involved in sclerotial development. To better elucidate key factors in sclerotia development, the figure integrates the fungal sclerotium formation factors and molecular mechanisms reported in the literature, labeled as FS: fungal sclerotium formation. The molecular mechanism of morel sclerotium formation proposed in this study is marked with differently colored boxes, labeled as MS: morel sclerotium formation. The arrow indicates positive regulatory effects.
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MDPI and ACS Style

Wei, C.; Li, J.; Mo, Z.; Liu, W.; Zheng, D.; Chen, X.; Li, F.; Tai, M.; Song, J.; Gu, C.; et al. Lipid Metabolism and Membrane Remodeling Drive Sclerotium Formation in Morchella eximia: Insights from Integrated Transcriptomics and Metabolomics. J. Fungi 2026, 12, 134. https://doi.org/10.3390/jof12020134

AMA Style

Wei C, Li J, Mo Z, Liu W, Zheng D, Chen X, Li F, Tai M, Song J, Gu C, et al. Lipid Metabolism and Membrane Remodeling Drive Sclerotium Formation in Morchella eximia: Insights from Integrated Transcriptomics and Metabolomics. Journal of Fungi. 2026; 12(2):134. https://doi.org/10.3390/jof12020134

Chicago/Turabian Style

Wei, Chunmou, Jimeng Li, Zhongmei Mo, Wei Liu, Dan Zheng, Xueyan Chen, Fulin Li, Mingfeng Tai, Jiaxin Song, Changhua Gu, and et al. 2026. "Lipid Metabolism and Membrane Remodeling Drive Sclerotium Formation in Morchella eximia: Insights from Integrated Transcriptomics and Metabolomics" Journal of Fungi 12, no. 2: 134. https://doi.org/10.3390/jof12020134

APA Style

Wei, C., Li, J., Mo, Z., Liu, W., Zheng, D., Chen, X., Li, F., Tai, M., Song, J., Gu, C., & Zhang, Q. (2026). Lipid Metabolism and Membrane Remodeling Drive Sclerotium Formation in Morchella eximia: Insights from Integrated Transcriptomics and Metabolomics. Journal of Fungi, 12(2), 134. https://doi.org/10.3390/jof12020134

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