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Article

Transcriptome Analysis of Ganoderma lingzhi Liquid Fermentation Process Using Corn Straw as Matrix

by
Sheng Wang
1,†,
Jintao Li
1,†,
Qi Fan
1,
Shufang Wang
1,
Changwei Sun
1,2 and
Meixia Yan
1,*
1
Institute of Special Animal and Plant Sciences, Chinese Academy of Agricultural Sciences, Changchun 130112, China
2
Jilin Provincial Key Laboratory of Traditional Chinese Medicinal Materials Cultivation and Propagation, Changchun 130112, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agriculture 2024, 14(8), 1271; https://doi.org/10.3390/agriculture14081271 (registering DOI)
Submission received: 12 June 2024 / Revised: 21 July 2024 / Accepted: 29 July 2024 / Published: 2 August 2024
(This article belongs to the Special Issue Genetics and Breeding of Edible Mushroom)
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Abstract

:
Ganoderma lingzhi, a species of white rot fungus, possesses the highest abundance of lignocellulose-degrading enzymes among these fungi, as well as a relatively high carbon conversion rate. Corn straw, as an important sustainable resource, is used as a substrate for the liquid culture of G. lingzhi. However, little is known about the genes encoding the lignocellulose degradation and polysaccharide and triterpenoid biosynthetic pathways involved in this process. This paper employs transcriptomics to uncover the key genes involved in lignocellulose degradation and the synthesis of polysaccharides and triterpenoids during the liquid fermentation of G. lingzhi using corn straw as the substrate, as well as their associations. Carbohydrate-Active enzymes analysis of differential genes in the sequencing results was used to analyze the genes related to lignocellulose degradation. Among these, 43 core genes encoding CAZymes were obtained after 0 to 5 days of fermentation, and 25 core genes encoding CAZymes were obtained after 5 to 12 days of fermentation. The differential expression levels of DN3690_c0_g1 (EGL), DN3627_c0_g2 (XYN), DN4778_c0_g1 (XYN), DN2037_c0_g1 (LACC), and DN277_c2_g1 (MnP) were used to identify the key genes. The polysaccharide synthesis metabolic pathway favored mannitol synthesis, and the expression of triterpene precursor-metabolizing enzyme genes revealed higher expression levels of key enzyme genes such as ACAT, HMGS, and MPK. A correlation clustering analysis of genes related to lignocellulose degradation, polysaccharide, and triterpene anabolism during liquid fermentation showed that lignocellulose degradation genes mainly influenced arabinose and mannitol anabolism, as well as the synthesis of triterpene precursors.

1. Introduction

Ganoderma lingzhi, as a widely cultivated edible and medicinal fungus, mainly encompasses such active constituents as polysaccharides, triterpenoids, polypeptides, sterols, alkaloids, and so on, which possess anti-tumor [1], immune-boosting [2], antioxidant [3], hypolipidemic [4], liver-protecting [5], and anti-inflammatory [6] effects. Compared to other white rot fungi, G. lingzhi has the highest abundance of genes encoding lignocellulose-degrading enzymes [7,8], and it also has a higher carbon conversion rate [9]. G. lingzhi breaks down the lignin on the substrate surface by secreting diverse ligninases, uncovering the inner hemicellulose and cellulose. Afterwards, G. lingzhi secretes a series of cellulases and hemicellulases to hydrolyze them into oligosaccharides and monosaccharides that can be absorbed and utilized by the mycelium [10].
Corn straw is a multifunctional and renewable biological resource that is abundant in cellulose (41.93%), hemicellulose (24.71%), and lignin (23.68%), along with other nutrients [11]. However, as a type of crop straw resource in China [12], the utilization rate of corn straw is relatively low, resulting in its random burning and stacking, causing serious resource waste and environmental pollution [13]. At the same time, the large molecular cross-linking structure formed by cellulose, hemicellulose, and lignin in corn straw [14,15] raises the difficulty of utilizing lignocellulose in corn straw [16].
Ganoderma has potential applications in the utilization of corn straw, but the lack of an in-depth understanding of the mechanisms involved in degrading corn straw lignocellulose significantly restricts the efficient use of corn straw using this fungus [16]. In addition, the effectiveness of using Ganoderma to utilize corn straw is not ideal, with a certain level of uncertainty in its utilization. Lignocellulose-degrading enzymes belong to the Carbohydrate-Active EnZymes (CAZy) family in carbohydrate metabolism, playing a crucial role in the growth process of white rot fungi [17]. Lignocellulolytic enzymes are Carbohydrate-Active Enzymes (CAZy) that play an important role in carbohydrate metabolism in organisms [10]. Cellulose-degrading enzymes can be classified into GHs, including endo-β-1,4-glucanase, cellobiohydrolase, and β-glucosidase [17,18]. Hemicellulose-degrading enzymes, mainly endo-β-1,4--xylanase (XYN), β-xylosidase (XYL), and α-glucuronidase (AGU), are also classified as GHs [10,12]. In terms of lignin-degrading enzymes, AAs play a key role, and they are classified as lignin oxidases (LOs) and lignin-degrading auxiliary enzymes (LDAs) [19]. LOs are responsible for the production of highly reactive, nonspecific free radicals that cleave lignin’s carbon-carbon and ether-unit interbonds [17]. LOs include laccase (LACC) and class II heme peroxidases (class II PODs, including lignin peroxidase (LiP), manganese peroxidase (MnP), and versatile peroxidase (VP)). LDAs include glucose/methanol/choline oxidases (GMCs) and copper radical oxidases (CROs) [20,21]. GMCs are mainly composed of cellobiose dehydrogenase (CDH), aryl alcohol oxidase, aryl alcohol oxidase (AAO), glucose oxidase (GOX), and alcohol oxidase (AOX), and CROs are mainly composed of galactose oxidase (GAOX) and glyoxal oxidase (GLOX) [16,19]. However, reports on the downstream synthetic pathways involved in Ganoderma triterpenes are still scarce, and the relationship between these three metabolic components has rarely been investigated in response to the question of how substrate degradation is related to the synthesis of polysaccharides or triterpenes and how these metabolic pathways are altered during liquid fermentation.
The degradation products of lignocellulose, such as glucose and xylose, participate in carbohydrate catabolic pathways like glycolysis (EMP) and the phosphogluconate pathway (HMP), providing energy and coenzymes for the growth of fungus, and large molecular polysaccharides act as cell wall components and carbon source reserves [22,23]. However, there are fewer reports on whether the expression of lignocellulase in Ganoderma is related to the expression of carbohydrate metabolic enzymes or the changes in carbohydrate components. Ganoderma triterpenoids are synthesized through the mevalonate pathway [24], and lanosterol undergoes oxidation, reduction, and acylation by various cytochrome oxidases to generate structurally distinct Ganoderma triterpenoids. However, studies on the downstream synthetic pathways of triterpenoids in G. lingzhi are still very scarce, and further research is needed on how the substrate degradation is associated with polysaccharide or terpene synthesis, as well as how these metabolic pathways are involved in the process of liquid fermentation change, while the relationship between the metabolism of these three components has also seldom been studied.
In addition, studies in fungi such as G. ingzhi have shown that terpene metabolism and glucose metabolism are also related to each other. Since terpene precursor synthesis starts with acetyl CoA, which is a core intermediate in carbon metabolism, the synthesis of terpene precursors can be promoted by adding acetyl CoA or regulating the expression of related metabolizing enzymes [25,26]. Meanwhile, various differences in triterpene content and the expression of triterpene precursor-metabolizing enzymes were found after inhibiting MAPKs or Skn7, transcription factors related to cell wall production and spore production in Ganoderma lucidum [27,28], suggesting that triterpene metabolism is related to the metabolic processes of butyric acid, polysaccharide synthesis, and spore production, and that there is an association between lignocellulase and sugar metabolism, as well as between terpene metabolism and sugar metabolism. However, the current research results are varied, and there is no clear consensus.
In G. lingzhi’s liquid fermentation with corn straw as a matrix, lignocellulose degradation-related genes play a key role, and the process of lignocellulose degradation involves the anabolism of polysaccharides and triterpenes. With cellulose enzymes as the breakthrough point, this study adopts Illumina sequencing technology to identify that cellulase enzyme activity is higher in the fermentation process of samples. Transcriptome sequencing using CAZyme analysis gene sequencing results are used to identify the differences in lignocellulose degradation-related genes and to determine the important genes involved in the process of the liquid fermentation of G. lingzhi with a corn stover substrate. Next, we determine the fermentation process of G. lingzhi polysaccharides in terpene synthesis involving a series of related genes and perform correlation analysis of G. lingzhi with liquid fermentation-related genes during the process of corn straw lignocellulose degradation. The change rules for polysaccharide and terpene synthesis metabolic enzyme gene expression provide new ideas and methods for the subsequent use of G. lingzhi in facilitating corn straw resource utilization. These results also provide an important reference for research into the mechanisms of other medicinal fungi in the process of corn straw liquid fermentation.

2. Materials and Methods

2.1. Fungal Strains and Culture Conditions

The strain LZ-8 of G. lingzhi was maintained at 4 °C in potato dextrose agar (PDA) tubes. Three mycelium-covered cakes with a diameter of 5 mm were pre-cultivated by inoculating them with potato dextrose broth (PDB) on PDA plates. Thereafter, the cakes were left undisturbed for 6 to 7 days at 25 °C, were agitated at 150 rpm for 6 days, and were shielded from light. After being transferred to 250 mL triangular flasks containing 90 mL of corn straw liquid medium (CSL, with 30 g/L corn straw powder with a particle size less than 0.2 mm), the 10 mL mycelial suspension in PDB was incubated at 25 °C under light protection and with 150 rpm oscillation. Periodic sampling was conducted with three replicates for each time point.

2.2. Enzyme Extraction Solution and Enzymatic Activity Assay

The crude enzyme solution was obtained by filtering the fermentation broth via a 0.45 mm sterile membrane. The activities of cellulase and xylanase were evaluated by adopting the DNS method [29,30], while the activities of laccase and manganese peroxidase were determined by using the ABTS and guaiacol methods, respectively [31,32].

2.3. RNA Extraction, Library Construction, and Sequencing

The mycelium cultured in CSL for 0 days was set as the control group CK, that cultured for 5 days was set as the treatment group LM, and that cultured for 12 days was set as the treatment group CH. After filtering the mycelial solution through three layers of gauze, the mycelium was rinsed with sterile water, promptly collected, and then stored in liquid nitrogen.
The total RNA of the mycelium was extracted by using Trizol reagent. Oligo(dT) magnetic beads were employed to enrich the mRNA with a polyA structure in the total RNA, and ion interruption was utilized to cut the RNA into fragments of 300 bp in length. The first strand of cDNA was generated with RNA as the template, 6-base random primers, and reverse transcriptase, while the second strand of cDNA was synthesized with the first strand as the template. After the library was constructed, PCR amplification was applied to enrich the library fragments, and the library was quality tested using an Agilent 2100 Bioanalyzer. Then, the total concentration and the effective concentration of the library were determined. Suzhou Panomic BioPharma Co., Ltd. (Suzhou, China) sequenced the libraries through paired-end (PE) next-generation sequencing (NGS) on the Illumina HiSeq sequencing platform.

2.4. Bioinformatics Analysis of RNA-Seq Data

By utilizing the synthesis sequencing (SBS) technology on the Illumina high-throughput platform to sequence the cDNA libraries, large quantities of high-quality raw data were generated. Subsequently, Trinity-v2.14.0 software was employed to assemble the clean reads obtained from the raw control data to yield transcripts, which were then analyzed [33]. After the assembly, the optimal alignment results were taken from the NCBI non-redundant protein sequences database (NR) [34], Evolutionary Genealogy of Genes: Non-supervised Orthologous Groups (eggNOG), Swiss-Prot [35], Pfam, Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) [36] were used to annotate the Unigene gene functions.
Correlation analysis was carried out on the three repeated experiments to calculate the Pearson correlation coefficient in order to assess the reproducibility between samples. To display the expression levels of each transcript more precisely, normalization was conducted based on the size of the transcripts, and the number of fragments per kilobase of transcript per million mapped reads (FPKM) was used to measure the expression levels of a gene or transcript [37].

2.5. Analysis of CAZyme DEGs Related to the Decomposition of Corn Straw Lignocellulose

Differentially expressed genes (DEGs) were recognized with the criteria of a fold change (FC) ≥2 and a false discovery rate (FDR) < 0.05. GO enrichment analysis was carried out to identify significantly enriched GO functions among the DEGs, disclosing the potential functions of the differentially expressed genes in the samples. KEGG pathway analysis was executed to calculate the number of differentially expressed genes at different levels, determining the main pathways and signaling pathways induced by the DEGs.
The concatenated sets and samples of differential genes from all comparison groups were analyzed by means of bidirectional clustering, based on the expression levels of the same gene in different samples and the expression patterns of different genes in the same samples. The distances were calculated using the Euclidean method and hierarchical clustering was carried out using the method of the longest distance for hierarchical clustering (Complete Linkage). DESeq was utilized for differential gene expression analysis, with the criteria for selecting differentially expressed genes being as follows: |log2FoldChange| > 1 and a significant p-value < 0.05. By comparing the CKvsLM group and the LMvsCH group, the related genes of G. lingzhi for degrading corn straw lignocellulose were analyzed.

2.6. Analysis of Enzyme Genes Involved in Carbohydrate Metabolism and Triterpenoid Metabolism

In order to explore the expression alterations of enzyme genes in polysaccharide and triterpene anabolic pathways when G. lingzhi undergoes liquid fermentation with corn straw as the substrate, the expression of enzymes related to glycolytic metabolic pathways (EMP, HMP, TCA) and glycolytic synthesis pathways (mannitol, arabinitol, alginate, etc.) during the liquid fermentation was subjected to cluster analysis.
The mevalonate pathway, which is involved in triterpene metabolism, was analyzed by clustering gene expression during liquid fermentation.

2.7. Analysis of the Co-Regulation Mechanism of Triterpene Metabolism, Matrix Degradation, and Glucose Metabolism Genes

Gene expression data in the transcriptome were subjected to correlation cluster network analysis and correlation clustering analysis so as to analyze the correlation between matrix degradation and the polysaccharide and triterpene anabolism pathways during the liquid fermentation of G. lingzhi with corn straw as a substrate. This further resolved the regulatory mechanisms of matrix degradation in connection with the processes of triterpene anabolism and polysaccharide anabolism.

2.8. Real-Time Quantitative PCR Experiments

An HiScript III 1st Strand cDNA Synthesis Kit (+gDNA wiper) (Vazyme, Nanjing, China) was utilized to synthesize cDNA from the total RNA in line with the instructions for use in real-time quantitative PCR (RT-qPCR) analysis. The internal reference gene selected was 18S. In order to guarantee the accuracy of our RNA-Seq data, eight genes were picked from the transcriptome of G. lingzhi and their relative expression levels were normalized to that of 18S. The sequences of specific primers (Shenggong Biotech, Shanghai, China) that were synthesized are presented in Table 1.

3. Results

3.1. Lignocellulose-Degrading Enzyme Activities of G. lingzhi

The activities of LACC and MnP reached their peaks on the fifth day of the liquid fermentation of G. lingzhi with corn straw as a substrate. Thereafter, they rapidly decreased and remained at a relatively low level (Figure 1C,D), mainly breaking down the surface lignin and uncovering cellulose and hemicellulose. The activity of the enzymes cellulase and xylanase, which mainly decompose cellulose and hemicellulose, increased with the duration of fermentation, peaked on the 13th day, and then declined (Figure 1A,B).
With the characterization of cellulase’s activity and xylanase’s activity on cellulose and hemicellulose, the level of gene expression of laccase, and manganese peroxidase’s live characterization of lignin, the level of gene expression of the combination of enzyme protein translation lags behind that of gene transcription. We selected the timing of the peak before sampling, choosing 5-day CSL cultures of mycelia as the treatment group LM. For G. lingzhi RNA-Seq, the mycelia cultured in CSL for 5 days were selected as the treatment group LM, the mycelia cultured in CSL for 12 days were selected as the treatment group CH, and the mycelia cultured in CSL for 0 days were selected as the control group (CK).

3.2. Functional Annotation of Novel Genes

Through screening 48.52, 43.96, and 49.25 million raw reads from the G. lingzhi transcriptome samples of the CK, CH, and LM groups, respectively, a total of 46.2, 41.94, and 47.6 million clean reads were obtained. There was no less than 423 Mb of clean reads in each cDNA library, and the percentage of clean reads and the Q30 minimum for the nine cDNA libraries were 94.96% and 94.05%, respectively (Table 2).
From the statistics of the assembly findings, 81,677 overlapping-cluster constructed transcript sequences with an average length of 2044.92 bp and an N50 length of 2800 bp were obtained. To construct single-gene sequences, 18,157 transcript sequences with an average length of 1612.48 bp and an N50 length of 2661 bp were acquired. The single-gene sequences had a GC percentage of 56.65%, an N50 length of 2661 nt, and a fairly good assembly quality (as shown in Table 3).
As presented in Figure 2A, the correlation analysis of the three replicated biological experiment samples, which included the CK, LM, and CH groups, indicated that the correlation coefficients among the samples of each group ranged from 0.9 to 1, signifying that the experiment was repeatable. As demonstrated in Figure 2B, the gene expression levels of the nine samples were typically distributed—that is, the density at the two ends was lower and the density in the middle was higher, and the majority of the genes were concentrated in the range of 10−2 to 102, with little dispersion among the sample groups and samples from the same period presenting the same expression level.
Gene function annotation was carried out on Unigene, and a total of 12,765 single genes were annotated from 18,157 single genes in six databases. The numbers of single genes with significant sequence similarity to the GO, KEGG, PFAM, eggNOG, Swiss-Prot, and NR databases were 5179 (40.6%), 5179 (40.6%), 7376 (57.8%), 9655 (75.6%), 8754 (68.6%), and 12,765 (100%), respectively, and the number of interactions shared by single genes with different databases is presented in Figure 2C.

3.3. Differential Expression Analysis

To gain an in-depth understanding of the gene expression variations of G. lingzhi during the liquid fermentation of corn straw, we compared the DEGs from the G. lingzhi transcriptome samples of the CH, CK, and LM groups. In Figure 3A, 1793 genes were differentially expressed (p < 0.05) between the CK and LM groups, with 758 genes being up-regulated and 1035 genes being down-regulated. Likewise, in Figure 3B, a total of 1375 genes were differentially expressed (p < 0.05) across the LM and CH groups, with 484 being up-regulated and 891 being down-regulated. The number of shared differentially expressed genes between the two groups was computed by utilizing the differential expression values from the samples, as depicted in Figure 3C. There were 1793 and 1375 differentially expressed genes in the CK_vs._LM and LM_vs._CH groups, respectively, amounting to a total of 230 genes between CK_vs._LM and LM_vs._CH.
Differentially expressed genes (DEGs) carried out various functions during the liquid fermentation of G. lingzhi with corn straw as the substrate, and the DEGs were analyzed through GO enrichment analysis for biological process (BP), cellular composition (CC), and molecular function. GO enrichment analysis divided the CK_vs._LM DEGs into 14 related to cellular components, 106 related to molecular functions, and 44 related to biological processes. Figure 4A presents a histogram of the top ten GO-enriched items with the lowest p-value after categorizing the GO items, which were associated with catalytic activity, amino acid synthesis, cellular polysaccharide catabolic processes, oxidoreductase activity, and cellulose metabolism. Additionally, the DEGs of LM_vs._CH were divided into 26 related to cellular components, 95 related to molecular functions, and 101 related to biological processes. Figure 4B presents a histogram of the top ten GO-enriched items with the lowest p-values after categorizing the GO items, which were related to ribosomal structural components, structural molecular activities, cytoplasmic translation, and peptide biosynthetic processes. The majority of the molecular functions mentioned above are connected to the process of lignocellulose breakdown.
DEGs were analyzed using the KEGG program. The KEGG database was employed to classify and count the different genes in the three treatment groups (PDR < 0.05). Then, the screened DEGs were annotated using KEGG pathways. KEGG enrichment bubble plots were generated using the top pathways with the lowest PDR values, with the vertical coordinates representing the pathways and the horizontal coordinates representing the enrichment factor. The CK_vs._LM comparisons indicated that the main metabolic pathways associated with the differentially expressed genes included starch and sucrose metabolism, glutathione metabolism, cyanoamino acid metabolism, and alanine–aspartic acid and glutamate metabolism (Figure 4C).
The LM_vs._CH analysis revealed that the primary pathways related to the differentially expressed genes were oxidative phosphorylation, glyoxylate and dicarboxylic acid metabolism, the citric acid cycle, methane metabolism, and so on (Figure 4D).

3.4. Genes Encoding Putative CAZymes Related to the Decomposition of Corn Straw Lignocellulose

The expression of lignocellulase genes in G. lingzhi was analyzed through clustering (Figure 5). Some genes were relatively highly expressed on days 0 and 5 of liquid fermentation, including two BGLs, one XYN, one MnP, one LAC, two GMCs, and one AOX. Some genes were relatively highly expressed on days 5 and 12 of liquid fermentation, including two EGLs, one BGL, one XYL, one AGL, three LACs, one GMC, and one VP. Clustering was utilized to identify the main lignocellulase genes expressed during G. lingzhi liquid fermentation with corn straw as the substrate, as well as the lignocellulase genes with significant expression changes at specific stages. Some genes were overexpressed on days 0 and 5 of liquid fermentation, such as two BGLs, one XYN, one MnP, one LAC, two GMCs, and one AOX. On days 5 and 12 of liquid fermentation, certain genes were particularly significantly expressed, including two EGLs, one BGL, one XYL, one AGL, three LACs, one GMC, and one VP. Between days 5 and 12 of liquid fermentation, a larger number of genes, including two EGLs, one BGL, one XYL, one AGL, three LACs, one GMC, and one VP, were relatively significantly expressed. During days 5 and 12 of liquid fermentation, a greater number of genes were expressed. The notable genes expressed included seven BGLs, two LPMOs, two CBHs, four XYNs, two XYLs, one AXE, two GMCs, one LAC, one CDH, and one MnP. On day 0, liquid fermentation was manifested at a fairly high level. On day 5 of liquid fermentation, the genes with the greatest expression included three EGLs, two BGLs, two CBHs, two XYLs, one AGL, and seven LACs. After 12 days of liquid fermentation, the following genes showed greater expression: one EGL, two BGLs, two CBHs, one XYN, two AGLs, seven LACs, two MnPs, two VPs, three AAOs, and one AOX. In conclusion, hemicellulase genes expressed more of the enzymes involved in the degradation of xylan, especially during the first five to twelve days of liquid fermentation. Among the lignin enzymes, laccase expressed more than manganese peroxidase, acting as the predominant enzyme system for lignin degradation in G. lingzhi, and the peak expression of these enzymes mostly occurred on day 5. Cellulase expression gradually increased from the beginning of day 0. On day 12, the expression of most of the enzymes reached the highest level. The changes in ligninase and cellulase gene expression followed the trend of the corresponding changes in enzyme activity.
Some example genes are provided to illustrate the specific expression patterns. For instance, two BGL genes were highly expressed on days 0 and 5, while three EGL genes had significant expression on day 5. This shows the dynamic nature of gene expression during different stages of the fermentation process. Additionally, the consideration of the predominant enzyme systems like laccase and manganese peroxidase helps us to understand the specific roles of these enzymes in lignin degradation. The description of the gradual increase in cellulase expression over time provides insights into the progression of cellulose breakdown during the liquid fermentation.
This study utilized the carbohydrate-active enzymes gene database (www.cazy.org, accessed on 11 June 2023) to analyze the differentially expressed genes (DEGs) among G. lingzhi transcriptome samples from the CK, LM, and CH groups in order to mine genes related to the degradation of corn straw by G. lingzhi. Through significant difference analysis of the 1793 DEGs obtained from the CK_vs._LM group, a total of 432 genes associated with the liquid fermentation of corn straw in G. lingzhi were obtained, and from the 1375 DEGs of the LM_vs._CH group, 412 such genes were identified.
In the G. lingzhi transcriptome, CK_vs._LM identified a total of seventeen CAZymes related to cellulose biodegradation, including four endoglucanases (EGLs), two cellobiohydrolases (CBHs), ten β-glucosidases (BGLs), and two lytic polysaccharide monooxygenases (LPMOs). LM_vs._CH identified seven such CAZymes, including three EGLs, one CBH, two BGLs, and one LPMO (Figure 6), indicating that G. lingzhi can produce multiple cellulases that work together to degrade corn straw cellulose. Some genes were down-regulated during liquid fermentation from 0 to 5 days (CK_vs._LM group treatment), but their expression was not obvious during liquid fermentation from 5 to 12 days (LM_vs._CH treatment group). Among them, DN10667_c0_g1 (GH2) was only expressed in the LM_vs._CH treatment group compared to other genes encoding BGLs, such as GH3 and GH1. DN3690_c0_g1 (GH5_15) was up-regulated in both phases, DN38_c0_g1 (GH7) was up-regulated first and then down-regulated, DN6222_c0_g1 (AA9) was down-regulated first and then up-regulated, and several other genes that were significantly expressed in both phases were down-regulated. These results suggest that EGL and LPMO play more important roles in the catabolism of corn straw cellulose than CBH and BGL, with the GH5_15 family of EGLs and the AA9 family of LPMOs being particularly significant.
For example, in other studies on microbial cellulose degradation, similar findings have been reported where certain families of enzymes like the EGLs and LPMOs have shown enhanced activity and importance [38,39]. This study provides valuable insights into the specific enzymes and their roles in the degradation process of G. lingzhi on corn straw, which can guide further studies and potential applications in the field of biomass utilization.
In CK_vs._LM, a total of nine CAZymes related to hemicellulose biodegradation were identified, encompassing four xylanases (XYNs), three xylosidases (XYLs), and two arabinogalactan lyases (AGLs). In LMvsCH, four CAZymes associated with cellulose biodegradation were detected, including three XYNs and one XYL (Figure 6). Among these genes, the single genes DN3627_c0_g2 and DN4778_c0_g1 encoding an XYN (GH10) showed up-regulated expression at both stages. The single gene DN1646_c0_g1 encoding an XYN (GH11) was expressed first in a down-regulated manner and then was up-regulated at both stages, while the single gene DN8106_c1_g1 encoding an XYL (GH5_22) had down-regulated expression at both stages, and these CAZymes might play a more prominent role in the catabolism of corn straw hemicellulose.
A total of seventeen CAZymes were found in CK_vs._LM, including thirteen ligninolytic oxidases (LOs) and four lignin degradation-associated proteins (LDAs) for degrading lignin (Figure 6). The thirteen LOs comprised ten laccase copper oxidases (LACCs) and three manganese peroxidases (MnPs). The four LDAs included two aryl-alcohol oxidases (AAOs) and one cellobiose dehydrogenase (CDH). In LM_vs._CH, fourteen CAZymes were discovered, including twelve LOs and two LDAs for degrading lignin. The thirteen LOs included six LACCs, five MnPs, and one versatile peroxidase (VP). The four LDAs consisted of two AAOs.
Most of the encoded genes, with the exception of some genes encoding CDH, were up-regulated in the LM transcriptome samples compared to the CH transcriptome samples. The results suggest that the breakdown of lignin with LOs is still crucial for utilizing corn straw polysaccharide biomass. Some of the genes exhibited a high fold change, such as DN682_c1_g2 encoding LACs, DN287_c2_g1 encoding MnP gene, and DN287_c2_g1, a single gene encoding VPs.
Important related genes, including DN3690_c0_g1 (EGL), DN3627_c0_g2 (XYN), DN4778_c0_g1 (XYN), DN2037_c0_g1 (LACC), and DN277_c2_g1 (MnP), were identified based on the differential expression levels.

3.5. Expression Cluster Analysis of Enzyme Genes Involved in Carbohydrate and Triterpene Metabolism

We conducted a cluster analysis of the expression of enzyme genes involved in the fundamental anabolic pathways in order to comprehensively examine the expression alterations of these genes in the polysaccharide and triterpene anabolic pathways during the liquid fermentation of G. lingzhi with corn straw. According to the findings of these studies, there were fluctuations in the expression levels of enzyme genes related to glycolytic metabolism, including mannitol, galactose, rhamnose, alginose, and arabinose, as well as in the triterpene anabolic pathway during the fermentation process (Figure 7A).
Firstly, during the fermentation process, a large amount of glucose-6-phosphatic was produced via GK (DN2967_c0_g1), which is a necessity for the synthesis of numerous sugars. Secondly, eight genes with similar overall expression trends controlled GPI, an enzyme crucial for the synthesis of mannitol and alginate precursors. Additionally, the mannitol anabolism-related genes exhibited high overall expression levels that persisted throughout the development stage. Notably, one GALE gene (DN18956_c0_g1) peaked in expression on the fifth day of fermentation. The anabolism of arabinose, galactose, and rhamnose, as well as the transformation of glucose-6-phosphatic acid into glucose-1-phosphate, was regulated by a group of four PGM genes. Among the enzyme genes involved in arabinose anabolism, one UGD (DN2099_c0_g1) and one UGP (DN26509_c0_g1) showed a down-regulated expression. The arabinose-related genes and the enzyme genes involved in the galactose anabolic pathway presented similar expression change tendencies, but the expression of GALE 1 (DN635_c0_g1) was down-regulated. Furthermore, during liquid fermentation, the genes encoding the rhamnose anabolism-related enzymes RFFH and RHS, as well as the alginose anabolism-related enzymes GMDH and GFS, showed decreased expression, though the differences were not very significant. In conclusion, due to sugar catabolism during the liquid fermentation of G. lingzhi using corn straw as the substrate, there may be a bias towards mannitol anabolism.
Both on the fifth day and the twelfth day of fermentation, there was an increase in the expression of enzyme genes in the G. lingzhi mycelium involved in the metabolism of triterpene precursor substances (Figure 7B). Among these genes, HMGS, MPK, MVD, IDI, and ACAT (DN26660_c0_g1) had higher expression levels. The three genes ACAT (DN26660_c0_g1), MPK (DN6504_c0_g1), and MVD (DN5950_c0_g1) are involved in the conversion of acetyl CoA to acetoacetyl CoA, phosphomesylate to mevalonate, and isopentenyl pyrophosphate to mevalonate pyrophosphate. These genes may be the key enzyme genes involved in the synthesis of triterpenoid compound precursors.

3.6. Co-Regulation of Triterpene Metabolism and Matrix Degradation and Glucose Metabolism Genes

We found that the red, blue, and green modules encompassed the majority of the genes associated with matrix degradation, sugar metabolism, and triterpene alterations. (Figure 8A,B). The production of acetoacetyl CoA, the breakdown of lignocellulose, and the generation of the polysaccharide precursors glucose-6-phosphate and mannose-6-phosphate were all related to changes in gene expression within the red module. The acetoacetyl CoA synthesis gene PDH (DN4002_c1_g1) was positively correlated with one EGL (DN3690_c0_g1), one VP (DN287_c2_g1), and one AOX (DN1796_c0_g2) among the genes with highly significant levels (p ≤ 0.001) (Figure 8C,D). In contrast, the other triterpene gene UGT (DN17049_c0_g1) had a correlation that was opposite to that mentioned above for the lignocellulosic genes. This indicates that the genes involved in lignocellulose degradation are crucial for the synthesis of acetoacetyl CoA, and that interactions with genes such as AOX, VP, and EGL might be part of the regulatory mechanism. The hemicellulose degradation-associated gene AGL (DN5160_c0_g1) and the lignin degradation-associated gene LAC (DN10674_c1_g1) had a positive correlation with each other, as did the polysaccharide synthesis precursor gene GPI (DN14304_c0_g1) with GMP (DN18956_c0_g1).
The degradation of lignocellulose, the formation of arabinose, and the conversion of acetyl CoA and a certain pyrophosphate to squalene were all associated with changes in gene expression in the green module. The hemifiber degradation genes LPMO (DN1138_c0_g1) and BGL (DN2254_c0_g1) were positively correlated with FPPS (DN3033_c0_g1); the genes SQS (DN20424_c0_g1) and PDH (DN550_c0_g1) involved in the conversion of acetyl CoA and that of pyrophosphate to squalene were positively correlated with two BGL and three LAC genes. The arabinose synthesis genes UGD (DN2099_c0_g1) and UGP (DN26509_c0_g1) were positively correlated with cellulose degradation-related genes, including five BGLs, two EGLs, two LPMOs, and one CBH (DN712_c0_g1); GK (DN2967_c0_g1) was positively correlated with lignin degradation genes, namely an AAO (DN1268_ c0_g2) and a VP (DN287_c2_g); and PGM (DN4662_c0_g1) was positively correlated with a cellulose degradation gene, a BGL (DN3405_c0_g1).
The gene expression alterations in the blue module were related to lignocellulose degradation, mannitol synthesis, and the conversion of acetoacetyl CoA to 2,3-oxidosqualene production. LAC (DN10674_c1_g1) had a favorable correlation with HMGS (DN17027_c0_g1), which converts acetoacetyl CoA to hydroxymethylglutaryl CoA. Moreover, the genes encoding GPI, GMP, and PMM in the mannitol synthesis process were favorably correlated with four BGL (DN2543_c0_g1), two AGL, and one LAC gene involved in lignocellulose breakdown.

3.7. Validating RNA-Seq Results via RT-qPCR

To validate the G. lingzhi transcriptome in our investigation, RT-qPCR analysis was performed on eight DEGs (Table 1). The gene expression information gleaned from the G. lingzhi transcriptome and the RT-qPCR results agreed rather well (Figure 9). This suggests that the RNA-Seq dataset is a trustworthy source.

4. Discussion

The current investigations of G. lingzhi liquid fermentation mainly aimed to optimize fermentation conditions and the selection of productive strains, with a high-yield of biologically active metabolites (e.g., laccases, polysaccharides, triterpenes, etc.) through liquid fermentation [40,41,42]. Cui [43] discovered that malt wort could substitute the commonly used glucose as the sole carbon source. Studies related to liquid fermentation with corn straw as the substrate have also been carried out using different fungi in terms of enzyme activity and biofermentation related to lignocellulose degradation, such as the study by Wang Mengmeng [44] on the optimization of conditions for the production of xylanase through the liquid fermentation of striga siderophores and that by Chen Qingsen [45] on the improvement of cellulose utilization in a fermentation broth by using corn straw as a raw material for multi-strain mixed fermentation. In this study, we identified the transcript samples through the changes in lignocellulases during liquid culture, and then identified the related genes in G. lingzhi liquid fermentation with corn straw as the substrate through the transcriptional changes, and found that the process of corn straw lignocellulose degradation involves the cooperation of a variety of enzyme genes [36], providing information on G. lingzhi liquid fermentation in the utilization of corn straw at the molecular level.
Eleven and ten CAZymes were found in the transcriptomes of CK_vs._LM and LM_vs._CH, respectively, which are crucial for the exploitation of corn straw. Some genes exhibited high fold changes, such as DN3690_c0_g1 (EGL), DN3627_c0_g2 (XYN), DN4778_c0_g1 (XYN), DN2037_c0_g1 (LACC), and DN277_c2_g1 (MnP), which may play more significant roles in the synergistic decomposition of corn straw lignocellulose. This is similar to the CAZymes gene expression results of Pleurotus ostreatus on corn straw [6]. In summary, the results of this study show that G. lingzhi degraded maize stover, expressing degradation genes related to cellulose, hemicellulose, and lignin, similarly to the action of white rot fungus siderophores on maize stover [38], with the family of GHs still being the mainstay. Meanwhile, similar results were obtained from a study of 22 transcriptome datasets from 10 basidiomycete species [46].
LPMOs can break down components other than cellulose in plant cells by oxidizing the carbon of the sugar ring, resulting in chain scission, which enhances the activity of the classical GHs by introducing new chain ends [47] and is a key factor in the decomposition of lignocellulose [39,48]. VPs are relatively rarer [21], with dual LiP and MnP activities that can degrade phenolic and non-phenolic substrates of lignin [49,50]. In this experiment, DN6222_c0_g1 (LPMO) had a remarkable expression during fermentation, and the single gene encoding VPs, DN287_c2_g1, showed a high fold change, and LPMOs and VPs may act as very important factors in the degradation of the lignocellulose decomposition of corn straw by G. lingzhi.
Mannitol can be produced by fungi through the utilization of carbohydrates. Smiley disclosed that Aspergillus candidus can synthesize mannitol from glucose, and a further study revealed that other carbon sources can also be combined with glucose to generate mannitol. The majority of fungi have the ability to produce mannitol, but their volumetric yield is relatively low when glucose is used as a substrate, and they also consume the generated mannitol [3,4]. The high expression level of GALE (DN18956_c0_g1) and other alterations in the expression levels of genes related to polysaccharide anabolism in this study were more conducive to mannitol anabolism, and further research on these genes may be crucial for enhancing substrate utilization and producing more mannitol. It is generally acknowledged that the MVA pathway is the mechanism by which the common precursor of terpenoids, lanosterol, is synthesized during the upstream metabolism of terpenoids in Ganoderma triterpenoids. ACAT (DN26660_c0_g1), which participates in the conversion of acetyl-CoA to acetoacetyl-CoA, and MPK (DN6504_c0_g1), which participates in the conversion of mevalonate to phosphomelane, were found to have high expression among the genes of individual metabolism enzymes involved in this metabolic pathway according to the results of the current study. Higher expression levels were observed for DN6504_c0_g1 and MVD (DN5950_c0_g1), which are involved in the conversion of mevalonate pyrophosphate to isopentenyl pyrophosphate [51,52,53].
Higher fungi growth is induced by expressing interrelated genes for lignocellulose-degrading and polysaccharide-metabolizing enzymes. These gene expressions can be controlled to modulate the expression of intracellular sugar transporters, glycolytic metabolizing enzymes, and chitinases [54]. Moreover, research on fungi such as G. lingzhi has demonstrated a connection between the metabolism of glucose and terpenes. Since acetyl CoA is the starting point for terpene precursor synthesis and is a key intermediate in carbon metabolism, terpene precursor synthesis can be accelerated by increasing acetyl CoA or controlling the expression of related metabolic enzymes [25,26]. Alternatively, terpene precursor synthesis can be accelerated by inhibiting MAPKs, transcription factors related to the formation of cell walls and the production of spores in G. lingzhi. It was discovered that the production of chitin and β-glucan in the mycelium was reduced along with the triterpene content and the expression of triterpene precursor-metabolizing enzymes, along with transcription factors such as MAPKs and Skn7 in G. lucidum that are associated with cell wall generation and spore production [27,28]. However, these findings of previous investigations are fragmented, making it difficult to correlate metabolic processes, such as matrix breakdown, sugars, and terpenes. In this study, we focused on the analysis of the key genes of three carbon metabolism pathways during liquid fermentation and found that cellulose-degrading enzymes EGLs and lignin-degrading enzymes VPs and AOXs were positively correlated with the acetyl CoA convertase gene PDH (DN4002_c1_g1) and negatively correlated with UGT (DN17049_c0_g1), and that BGLs and LACs were involved in the conversion of acetyl CoA and that of pyrophosphate to squalene. Meanwhile, the arabinose synthesis genes UGD (DN2099_c0_g1), UGP (DN26509_c0_g1), PGM (DN4662_c0_g1), and GK (DN2967_c0_g1) were positively correlated with hemicellulose degradation-related genes, and laccases had an influence on HMGS expression and mannitol synthesis. The polysaccharide synthesis precursor gene GPI (DN14304_c0_g1) was positively correlated with the hemicellulose degradation-related gene AGL and the lignin degradation-related LAC gene, FPPS was correlated with hemicellulose expression. One way to assess the relationships between lignocellulases and sugar metabolism and terpene metabolism and sugar metabolism is to perform a comprehensive examination of metabolic processes such as matrix degradation, sugar metabolism, and terpene metabolism.

Author Contributions

Conceptualization, C.S., M.Y. and S.W. (Sheng Wang); methodology, S.W. (Sheng Wang); validation, S.W. (Sheng Wang), J.L., S.W. (Shufang Wang) and Q.F.; formal analysis, S.W. (Sheng Wang); investigation, S.W. (Sheng Wang) and J.L.; resources, S.W. (Sheng Wang); data curation, S.W. (Sheng Wang); writing—original draft preparation, S.W. (Sheng Wang) and J.L.; writing—review and editing, M.Y., S.W. (Sheng Wang) and J.L.; visualization, S.W. (Sheng Wang); supervision, M.Y. and C.S.; project administration, M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Agricultural Science and Technology Innovation Program (CAAS-ASTIP-2021-ISAPS) and Demonstration and Promotion of Key Agricultural Core Technologies in Jilin Province (Industrial Technology System 202400601).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are openly available in NCBI at BioProject: PRJNA1107921, Biosample: SAMN41216530, SAMN41216531, SAMN41216532, SAMN41216533, SAMN41216534, SAMN41216535, SAMN41216536, SAMN41216537 and SAMN41216538.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Lignocellulolytic enzyme activity of LZ-8. (A) Cellulase activity. (B) Xylanase activity. (C) Laccase activity. (D) Manganese peroxidase activity. Note: Each graph point represents the mean of three biological replicates SD (p ≤ 0.05), the letters represent salience and the error bar represents the standard error.
Figure 1. Lignocellulolytic enzyme activity of LZ-8. (A) Cellulase activity. (B) Xylanase activity. (C) Laccase activity. (D) Manganese peroxidase activity. Note: Each graph point represents the mean of three biological replicates SD (p ≤ 0.05), the letters represent salience and the error bar represents the standard error.
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Figure 2. Annotation of functions and correlation plots of FPKM for each sample. (A) Correlation heatmap between samples. (B) FPKM box plot of each sample. (C) Number of single-gene interactions annotated by six databases.
Figure 2. Annotation of functions and correlation plots of FPKM for each sample. (A) Correlation heatmap between samples. (B) FPKM box plot of each sample. (C) Number of single-gene interactions annotated by six databases.
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Figure 3. Correlation plots of DEGs. (A) MA map of differentially expressed genes (CK_vs._LM). (B) MA map of differentially expressed genes (LM_vs._CH). (C) Shared unique differential genes among groups, Red for CK_vs._LM, blue for LM_vs._CH.
Figure 3. Correlation plots of DEGs. (A) MA map of differentially expressed genes (CK_vs._LM). (B) MA map of differentially expressed genes (LM_vs._CH). (C) Shared unique differential genes among groups, Red for CK_vs._LM, blue for LM_vs._CH.
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Figure 4. GO and KEGG analyses of the differentially expressed genes in the collected hyphae of G. lingzhi following different degradation treatments. (A,B) Quantitative histograms of GO gene enrichment for CK_vs._LM and LM_vs._CH. MF: molecular function; BP: biological process; CC: cell composition. The horizontal axis represents the GO term, and the vertical axis represents −log10 (p-value) enriched by the GO term. (C,D) KEGG enrichment bubble charts. The horizontal axis is the enrichment factor (expressed as the ratio of differentially expressed genes annotated to the pathway to the total number of expressed genes annotated to the pathway). The top pathways with the lowest p-values were used to produce the map, where the ordinate represents the pathway; the abscissa represents the enrichment factor (the number of differences in the pathway is divided by all the numbers); and the circle size indicates the number, where the redder the color, the smaller the p-value. More differentially expressed genes are enriched in the pathways with redder and bigger bubbles.
Figure 4. GO and KEGG analyses of the differentially expressed genes in the collected hyphae of G. lingzhi following different degradation treatments. (A,B) Quantitative histograms of GO gene enrichment for CK_vs._LM and LM_vs._CH. MF: molecular function; BP: biological process; CC: cell composition. The horizontal axis represents the GO term, and the vertical axis represents −log10 (p-value) enriched by the GO term. (C,D) KEGG enrichment bubble charts. The horizontal axis is the enrichment factor (expressed as the ratio of differentially expressed genes annotated to the pathway to the total number of expressed genes annotated to the pathway). The top pathways with the lowest p-values were used to produce the map, where the ordinate represents the pathway; the abscissa represents the enrichment factor (the number of differences in the pathway is divided by all the numbers); and the circle size indicates the number, where the redder the color, the smaller the p-value. More differentially expressed genes are enriched in the pathways with redder and bigger bubbles.
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Figure 5. Differential expression of genes (DEGs) encoding CAZymes related to the decomposition of corn straw lignocellulose biomass in the G. lingzhi trancriptome.
Figure 5. Differential expression of genes (DEGs) encoding CAZymes related to the decomposition of corn straw lignocellulose biomass in the G. lingzhi trancriptome.
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Figure 6. Expression of putative CAZyme DEGs related to the decomposition of corn straw lignocellulose in the G. lingzhi transcriptome.
Figure 6. Expression of putative CAZyme DEGs related to the decomposition of corn straw lignocellulose in the G. lingzhi transcriptome.
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Figure 7. Cluster analysis of carbohydrate metabolism and triterpene metabolism gene expression in liquid fermented mycelium of G. lingzhi. (A) Polysaccharides. (B) Triterpenes.
Figure 7. Cluster analysis of carbohydrate metabolism and triterpene metabolism gene expression in liquid fermented mycelium of G. lingzhi. (A) Polysaccharides. (B) Triterpenes.
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Figure 8. Correlation between genes. (A) Correlation cluster network (module). (B) Correlation cluster network (classification). (C) Heat map of association between matrix degradation and triterpene metabolism. (D) Heat map of association between matrix degradation and polysaccharide metabolism. p significance level: *, <0.05; **, <0.01; ***, <0.001.
Figure 8. Correlation between genes. (A) Correlation cluster network (module). (B) Correlation cluster network (classification). (C) Heat map of association between matrix degradation and triterpene metabolism. (D) Heat map of association between matrix degradation and polysaccharide metabolism. p significance level: *, <0.05; **, <0.01; ***, <0.001.
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Figure 9. Validation of the RNA−Seq of 8 selected DEGs in the G. lingzhi transcriptome by RT−qPCR.
Figure 9. Validation of the RNA−Seq of 8 selected DEGs in the G. lingzhi transcriptome by RT−qPCR.
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Table 1. Primer sequences used in qRT-PCR.
Table 1. Primer sequences used in qRT-PCR.
Gene IDDescriptionForward Primer Sequences (5′-3′)Reverse Primer Sequences (5′-3′)
DN4778_c0_g1Endo-1,4-beta-xylanase 3TGAAGAACCTTGCTGCACTCTCTCAAAGACACAAGTCGAGC
DN1646_c0_g1Endo-1,4-beta-xylanase AGGGACTTCACCGACAAGTATTCTGCCACGATACCGTTGTAC
DN5168_c0_g1Manganese dependent endoglucanase Eg5ATGGTTCAGCAACTTCTACGACCAGACGCAAACTGGTCATTG
DN38_c0_g1Exoglucanase 1CTGGATGGTGCTGACTACGAATATCTCGTACTTGGCGTCG
DN377_c2_g1Manganese transporter pdt1TCATCTTCGCACTCGCACGCCGATGAGTCTGGTGATAAT
DN3627_c0_g2Beta-xylanaseTTAACCAGCTCAACGGTCCCACCAAAAGCGATGCAAGAG
DN682_c2_g1Laccase 1TCGTGGTCAATGGTGTCTTCGTTCGTGCCCTTTTGGAAG
DN2037_c0_g1Laccase CTCACTGCCACATTGACTGGTGGGATCGTTTGCTATGGAC
Table 2. Summary of the sequencing.
Table 2. Summary of the sequencing.
SampleRead No.Clean Read No.Clean Read %N (%)Q20 (%)Q30 (%)
CK_149,716,67047,483,60895.50.00148598.3295.15
CK_244,655,27442,407,34894.960.00151298.3295.23
CK_351,196,28048,724,77695.170.00147198.3395.25
LM_148,361,61446,107,67295.330.00145798.395.15
LM_244,591,09642,515,00895.340.00143698.2895.08
LM_354,795,24852,157,26295.180.0014798.3295.17
CH_143,475,62441,499,25495.450.0015798.0994.67
CH_244,928,99842,852,55095.370.00183697.8594.05
CH_343,479,44841,455,39695.340.00155398.0694.53
Note: Sample: sample name; Read No: Total number of reads; Clean Read No: read number of high-quality sequences; Clean Read %: the percentage of high-quality sequence reads in sequencing reads; N (%): the percentage of ambiguous bases; Q20 (%): the percentage of bases with a base recognition accuracy above 99%; Q30 (%): the percentage of bases with a base recognition accuracy above 99.9%.
Table 3. Assembly results of the G. lingzhi transcriptome.
Table 3. Assembly results of the G. lingzhi transcriptome.
Total Length (bp)Sequence NumberMax. Length (bp)Mean Length (bp)N50 (bp)N90 (bp)GC%
Transcript167,022,85481,67715,4292044.922800108957.07
Unigene29,277,83618,15715,4291612.48266165456.65
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Wang, S.; Li, J.; Fan, Q.; Wang, S.; Sun, C.; Yan, M. Transcriptome Analysis of Ganoderma lingzhi Liquid Fermentation Process Using Corn Straw as Matrix. Agriculture 2024, 14, 1271. https://doi.org/10.3390/agriculture14081271

AMA Style

Wang S, Li J, Fan Q, Wang S, Sun C, Yan M. Transcriptome Analysis of Ganoderma lingzhi Liquid Fermentation Process Using Corn Straw as Matrix. Agriculture. 2024; 14(8):1271. https://doi.org/10.3390/agriculture14081271

Chicago/Turabian Style

Wang, Sheng, Jintao Li, Qi Fan, Shufang Wang, Changwei Sun, and Meixia Yan. 2024. "Transcriptome Analysis of Ganoderma lingzhi Liquid Fermentation Process Using Corn Straw as Matrix" Agriculture 14, no. 8: 1271. https://doi.org/10.3390/agriculture14081271

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