Mol. Cells 2019; 42(4): 363~375  https://doi.org/10.14348/molcells.2019.0019
Comparative Transcriptomic Analysis of MAPK-Mediated Regulation of Sectorization in Cryphonectria parasitica
Jeesun Chun1, Kum-Kang So1, Yo-Han Ko2, Jung-Mi Kim3, and Dae-Hyuk Kim1,2,4,*
1Institute for Molecular Biology and Genetics, Chonbuk National University, Chonbuk 54896, Korea, 2Department of Bioactive Material Sciences, Chonbuk National University, Chonbuk 54896, Korea, 3Department of Bio-Environmental Chemistry, Institute of Life Science and Natural Resources, Wonkwang University, Chonbuk 54538, Korea, 4Department of Molecular Biology, Chonbuk National University, Chonbuk 54896, Korea
Received February 12, 2019; Accepted February 21, 2019.; Published online April 4, 2019.
© Korean Society for Molecular and Cellular Biology. All rights reserved.

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit (http://creativecommons.org/licenses/by-nc-sa/3.0/).
ABSTRACT

Fungal sectorization is a complex trait that is still not fully understood. The unique phenotypic changes in sporadic sectorization in mutants of CpBck1, a mitogen-activated protein kinase kinase kinase (MAPKKK) gene, and CpSlt2, a mitogen-activated protein kinase (MAPK) gene, in the cell wall integrity pathway of the chestnut blight fungus Cryphonectria parasitica have been previously studied. Although several environmental and physiological factors cause this sectoring phenotype, genetic variants can also impact this complex morphogenesis. Therefore, RNA sequencing analysis was employed to identify candidate genes associated with sectorization traits and understand the genetic mechanism of this phenotype. Transcriptomic analysis of CpBck1 and CpSlt2 mutants and their sectored progeny strains revealed a number of differentially expressed genes (DEGs) related to various cellular processes. Approximately 70% of DEGs were common between the wild-type and each of CpBck1 and CpSlt2 mutants, indicating that CpBck1 and CpSlt2 are components of the same MAPK pathway, but each component governs specific sets of genes. Functional description of the DEGs between the parental mutants and their sectored progenies revealed several key pathways, including the biosynthesis of secondary metabolites, translation, amino acid metabolism, and carbohydrate metabolism; among these, pathways for secondary metabolism and translation appeared to be the most common pathway. The results of this comparative study provide a better understanding of the genetic regulation of sector formation and suggest that complex several regulatory pathways result in interplays between secondary metabolites and morphogenesis.

Keywords: MAPK pathway, RNA-Seq, sectorization, transcriptomic analysis
INTRODUCTION

Filamentous fungal sectorization, which is called ‘woolly degeneration’ and was first reported in the model organism Neurospora crassa (Sheng, 1951), occurs during subculture on artificial media showing loss or reduced asexual sporulation, fruiting, and sexual propagation (Ryan et al., 2002; Wang et al., 2004). These degenerative morphological states are accompanied by a decrease in secondary metabolite production, causing substantial commercial losses (Li et al., 1994; Magae et al., 2005). However, the underlying mechanism of colony sectorization is not fully understood.

In Cryphonectria parasitica, mitogen-activated protein (MAP) kinases is a great concern because several components in MAPK pathways and their downstream effectors are modulated by the presence of the single-stranded RNA Cryphonectria Hypovirus 1 (CHV1) or are involved in hypoviral symptom development, such as reduced pathogenicity and hydrophobicity (Choi et al., 2005; Deng et al., 2007; Kim et al., 2010; Park et al., 2004; 2012; So and Kim, 2017; Sun et al., 2009; Turina et al., 2006). Among these MAPK pathways, the cell wall integrity (CWI) MAPK pathway is unique because a mutant of CpBck1, an ortholog of yeast Bck1 (a CWI MAPKKK), and CpSlt2, an ortholog of yeast Slt2 (a CWI MAPK), have shown characteristic phenotypic changes of sporadic sectorization. Moreover, when the sectored mycelia of each mutant strain were successively transferred to new media, the progenies of the sectored mutant maintained sectored phenotypes, indicating that, once sectored, the sectored phenotype was stably inherited. In addition, we found that the sectored progenies of CpBck1 and CpSlt2 had drastic impaired virulence, although they had partially retrieved the mycelial growth (Kim et al., 2016; So et al., 2017). A recent study on the underlying genetic mechanism governing these sectored phenotypes identified global epigenetic changes in sectored progenies (So et al., 2018). However, further studies are required to understand both the genes affected in sectored progenies and their regulation.

Massive transcripts analyses are useful to obtain comprehensive information on the regulation of genes involved in stable phenotypic changes such as sectorization. Differential mRNA display (Chen et al., 1996; Kang et al., 2000) and cDNA microarray representing approximately 2,200 unique genes (Allen et al., 2003) were conducted in C. parasitica to determine the genes affected by hypovirus infection or mutations in one or more key regulatory pathways. However, differential mRNA display requires considerable additional efforts to determine the identity of differentially expressed genes (DEGs) and microarray can provide information only on given sequences. In contrast, using high-throughput sequencing, genome-wide transcript profiling is possible by directly sequencing the mRNAs in a sample. Therefore, RNA sequencing technology (RNA-Seq) is considered to be the most powerful tool for transcriptomic analysis. In this study, we conducted transcriptomic analysis using RNA-Seq and compared transcript profiles between the mutant strains and the wild-type. Further comparison was performed between the sectored progenies of the mutant strains and their corresponding parental strains to obtain comprehensive information on the genes involved in sectorization to identify the specific genes underlying sectorization.

MATERIALS AND METHODS

Fungal strains and paired-end RNA sequencing

C. parasitica wild-type strain EP155/2 (ATCC 38755) its isogenic hypovirus-CHV1-713-containing strain UEP1, the CWI MAPKKK deletion mutant TdBCK1 and its sectored progeny TdBCK1-S1, and the CWI MAPK deletion mutant TdSLT2-69 and its sectored progeny TdSLT2-69-S1 were used for the RNA-Seq analysis. The strains were grown on PDAmb under standard growth conditions at 25°C under low constant light. For the RNA-Seq analysis, 5-day-old mycelia were harvested from the cellophane membrane. Total RNA was extracted as described previously (Kim et al., 1995). The cDNA libraries for three biological repeats for each sample were sequenced using the Illumina HiSeq 2000 system, producing 47.5 Gbp of 101-bp paired-end reads. The extracted sequences were trimmed at a phred score of 20 and kept paired reads with a length of 25-bp using SolexaQA (Langmead et al., 2009). Qualified clean reads were mapped on annotated gene transcripts of the reference C. parasitica genome (http://genome.jgi-psf.org/Crypa2/Crypa2.home.html) using Bowtie 2 (v2.1.0) software (Chae et al., 2017; Li et al., 2010; Moon et al., 2018). Read counts were normalized by DESeq library (Anders and Huber, 2010) implemented in software R package.

RNA-Seq data analysis

The differentially expressed value of the assembled unique transcripts was calculated and normalized using the Fragments Per Kilobase of exon per Million (FPKM) method by dividing the number of fragments mapped to each gene by the size of its transcripts. False Discovery Rate (FDR), obtained by converting the statistical score to p-value using binomial distribution, was used to rule out any possibility of false positive and identify the threshold p-value. Genes with p-value less than 0.01 were regarded as significantly expressed. Fold Change (FC) analyses were performed to identify differentially expressed genes (DEGs) between each samples. DEG were identified by twofold change in reads for all pairwise comparisons based on the FPKM counts. To assess the differential transcript level between pairwise comparisons, a threshold of log2-fold change larger than 1 and smaller than −1 in expression were taken into consideration as significantly up- or down-regulated in this study, respectively. For functional annotation of the DEGs, the assembled unique transcripts were compared with the amino acid sequences using the BLASTX analysis against the non-redundant database with cutoff E-value of 1e–10. The DEGs were annotated to generate Gene Ontology (GO) terms according to biological process, cellular component and molecular function (counts ≥ 1). In addition, biochemical and metabolic pathway of the DEGs were analyzed according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database of the reference strain Magnaporthe oryzae.

Validation of DEGs by qRT-PCR analysis

To validate the DEGs, qRT-PCR analysis was conducted for 18 selected DEGs, selected based on the significant fold changes caused by the presence of hypovirus as well as sectorization. RNA was extracted from all tested strains cultured for five days on PDAmb plates, and first-strand cDNA was synthesized from 500 ng of RNA using SuperScript III reverse transcriptase (Invitrogen Corp., USA) with random primers. Real-time PCR was performed using an Applied Biosystems 7500 system with SYBR premix Ex Taq II (TaKaRa, Japan). Analyses were conducted in triplicate for each transcript as technical replicates, and at least two biological replications were performed using independent RNA preparations of the tested sample. Transcript levels were normalized to the mRNA values of the internal control gene of glyceraldehyde-3-phosphate dehydrogenase (GenBank No. P19089), and the relative gene expression level was analyzed with the 2−ΔΔCT method (Livak and Schmittgen, 2001). Table 4 lists the genes used for the qRT-PCR analysis. The correlation between two platforms were analyzed by linear regression analysis and statistical significance of the regression coefficient was determined by t-test.

Statistical analysis

All quantitative real-time RT-PCR transcripts were analyzed with analysis of variance using SPSS software (ver. 23.0, SPSS Inc., USA). The significance of all effects was determined using the Student-Newman-Keuls method at a significance level of p = 0.01.

RESULTS AND DISCUSSION

Overview of RNA-Seq

To characterize the MAPK-mediated transcriptional regulation of sectorization, RNA-Seq analyses were performed on the mutant strain of TdBCK1 referring a transformant deleted the CpBck1 gene, an ortholog of yeast CWI MAPKKK, and on TdSLT2-69 referring a transformant deleted the CpSlt2 gene, an ortholog of yeast CWI MAPK, in comparison with the wild-type EP155/2 strain. In addition, the corresponding sectored progenies of TdBCK1 and TdSLT2-69, TdBCK1-S1 and TdSLT2-69-S1, were included to obtain information on sector-specific genome-wide transcriptional changes. RNA samples were isolated after 5 days of cultivation on potato dextrose agar medium supplemented with methionine and biotin (PDAmb), cDNA libraries for three biological replicates for each sample were prepared, and 15 datasets were sequenced using Illumina HiSeq 2000 platform. More than 2.85 × 108 qualified 101-bp paired-end reads were generated, and approximately 62.3% of short reads were successfully mapped either uniquely or multiply on annotated gene transcripts of the reference C. parasitica genome (http://genome.jgi-psf.org/Crypa2/Crypa2.home.html). The unmapped reads might be derived from mapping errors in spite of quality filtration or an incomplete annotation of the reference genome. However, the unmapped reads in this study appeared lower than those reported by others ranging from 46.2% to 47.6% (Cox et al., 2010).

Using a log2-fold change (FC) cutoff, DEGs were identified from a pair-wise comparison (Fig. 1). In total, 458 and 433 unique transcripts of TdBCK1 and TdSLT2-69, respectively, were identified as DEGs compared to the wild-type (Supplementary Table S1). Of these, 224 genes were upregulated, and 234 were down-regulated in TdBCK1, whereas 224 genes were up-regulated, and 209 were down-regulated in TdSLT2-69. Interestingly, 321 genes were common DEGs in the TdBCK1 and TdSLT2-69 mutants, which represented 70% and 74% of the DEGs in each mutant strain, respectively. The high proportion of common DEGs indicated that the CpBck1 and CpSlt2 genes were in the same regulatory pathway, confirming that CpBck1 is a MAPKKK and CpSlt2 is a MAPK in the same CWI MAPK signaling pathway. Moreover, approximately 30% of DEGs were identified as independently regulated, suggesting that the existence of component-specific regulatory pathways.

Transcriptomic analysis of the sectored strain of the CpBck1-null mutant (TdBCK1-S1) compared to that of the parental CpBck1-null mutant strain (TdBCK1) revealed that a total of 343 genes were differentially expressed; 185 genes of these were up-regulated, and 158 were down-regulated. Comparing TdSLT2-69 and TdSLT2-69-S1, a total of 123 genes were differentially regulated; 58 of these genes were up-regulated, and 65 were down-regulated. Table 1 presents the highly expressed transcripts for each strain showing |log2 (fold change)|>4. There were similar number of DEGs (i.e., 458 and 433) between the mutants and the wild-type (TdBCK1 vs. EP155/2 and TdSLT2-69 vs. EP155/2), but there was a 2.8-fold difference in DEGs (i.e., 343 and 123) between the sectored progenies and the corresponding parental mutant strains (TdBCK1-S1 vs. TdBCK1 and TdSLT2-69-S1 vs. TdSLT2-69). Considering the severe phenotypic changes in the CpBck1 mutant lines compared to those of CpSlt2 mutant lines, the larger number of DEGs between TdBCK1 and TdBCK1-S1 is consistent with the degree of phenotypic changes. Accordingly, a large number of DEGs between TdBCK1 and TdBCK1-S1 might be involved in processes other than sectorization, and the DEGs between TdSLT2-69 and TdSLT2-69-S1 were more specific to sectorization.

Among the 466 genes that were differentially expressed based on the two comparisons (TdBCK1-S1 vs. TdBCK1 and TdSLT2-69-S1 vs. TdSLT2-69), 73 genes were identified as common DEGs affected in both comparisons (TdBCK1-S1 vs. TdBCK1 and TdSLT2-69-S1 vs. TdSLT2-69), showing transcriptional alteration in the same direction in both comparisons. Of these 73 genes, 34 were up-regulated, and 39 were down-regulated compared with the parental mutants and their corresponding sectored progenies. Moreover, 59 of the 73 genes were affected in the comparison between the wild-type and the parental mutant TdBCK1, and 51 of the 73 genes were differentially expressed between the wild-type and the parental mutant TdSLT2-69. All 51 DEGs were differentially expressed between the wild-type and TdBCK1. Therefore, 51 DEGs were affected by mutation and further affected by sectorization. Interestingly, 22 of the 73 genes were affected by the presence of CHV1, indicating a significantly overlapped difference in transcription between EP155/2 and UEP1 (data not shown). However, the transcriptional changes of these 22 DEGs were not altered in the same direction i.e., 14 viral up-regulated genes were divided into 8 up-regulated and 6 down-regulated, whereas 8 viral down-regulated genes were divided into 7 up-regulated and 1 down-regulated in sectorization comparisons.

Functional analysis of DEGs

GO analysis was performed for the up-regulated genes in TdBCK1 compared to the wild-type (Fig. 2). GO analysis revealed enrichment in the biological process and molecular function categories. Metabolic process was the dominant enriched biological process category, followed by the ATP binding, binding, zinc ion binding, proteolysis, and nucleic acid binding categories, with at least 10 up-regulated genes. In the cellular component ontology, intracellular, integral component of membrane, membrane, and nucleus were four dominant enriched categories, with at least 13 DEGs. Catalytic activity and oxidoreductase activity were the two most strongly influenced ontologies in the molecular function category. Down-regulated genes showed similar enrichment of the biological process category as up-regulated genes, except that the number of down-regulated genes in the ATP binding category was markedly reduced compared to that of up-regulated genes. Integral component of membrane, membrane, and nucleus were the top three enriched cellular component categories, with more than nine DEGs. Catalytic activity and oxidoreductase activity were again the top two enriched categories in molecular function, followed by transporter activity, transcription factor activity, and monooxygenase activity. Interestingly, the biological process included categories showing a large difference between upregulated and down-regulated genes. For example, a more than two-fold increase in down-regulated DEGs was observed in iron ion binding and carbohydrate metabolic process categories when categories with more than 10 DEGs were included.

GO analysis was performed in TdSLT2-69 compared to the wild-type (Fig. 2). GO analysis revealed enrichment in the biological process and molecular function categories. In the biological process categories, zinc II ion transport and gluconeogenesis were the top two most enriched categories in terms of both up- and down-regulated genes; this was followed by cellular metabolic process, cation transport, and sequence specific DNA binding. Most DEGs in the cation transport category were up-regulated, whereas other enriched categories showed similar numbers of up- and downregulated genes. In the cellular component ontology, ribosome and intracellular were the first and second most influenced categories, respectively, and intracellular was also an enriched category in TdBCK1. In the molecular function category, ATPase activity, coupled to the transmembrane movement of substances, was the dominant enriched category, followed by urate oxidase activity, methionine adenosyltransferase activity, transferase activity, amidophosphoribosyltransferase activity, oxidoreductase activity, adenosylhomocysteinase activity, adenosine kinase activity, and protein tyrosine/serine/threonine phosphatase activity. Among these enriched categories, adenosylhomocysteinase activity and adenosine kinase activity were enriched with mostly upregulated genes, whereas amidophosphoribosyltransferase activity, oxidoreductase activity, and protein tyrosine/serine/threonine phosphatase activity were enriched with more down-regulated genes.

Considering that a large proportion of DEGs were commonly obtained, common categories of ontology were expected. In total, 179 common categories of the 270 and 230 categories obtained in the comparisons of TdBCK1 vs. EP155/2 and TdSLT2-69 vs. EP155/2, respectively were identified. In addition, the enriched pattern of categories were maintained in common categories i.e., dominant categories for each comparison remained dominant in common categories. However, there were unique categories in each comparison. These results suggest that, although there are significant overlaps in terms of downstream target genes, component-specific pathways exist, and the effects of the component on each target gene differ significantly in the CWI MAPK pathway. GO analysis of common categories indicated that CpBck1 and CpSlt2, components of the well-known CWI MAPK pathway, have multiple roles in governing the cell development process via diverse metabolisms related to amino acid, carbohydrate, secondary metabolites, and transport. These broad spectrums of GO enrichment are consistent with the fact that the fungal cell wall is composed primarily of chitin, glucans, other polysaccharides, and glycoproteins and cell wall formation involves numerous biosynthetic pathways of hundreds of gene products (Bowman and Free, 2006). In addition, the enriched common cellular components included the membrane, which is known to harbor many cell wall-synthesizing enzymes for the primary cell wall components that form extracellular cross-linkage. Therefore, we examined the expression profiles of representative cell wall synthesizing enzymes (Fig. 3). Chitin, a linear homopolymer of β-1,4-linked N-acetylglucosamine, synthesized by chitin synthase, is an important cell wall component for cell wall integrity. Four and three putative chitin synthases were differentially expressed in TdBCK1 and TdSLT2-69 compared to the wild-type, respectively (Fig. 3A). These chitin synthases were similarly regulated, showing similar up- and down-regulation i.e., two down-regulated DEGs of TdSLT2-69 were also down-regulated in TdBCK1 and one up-regulated DEG was up-regulated in TdBCK1 as well. Thus, only one DEG was specific to TdBCK1, suggesting that CpBck1 and CpSlt2 were in the same regulatory pathway, although there also appeared to be a component-specific downstream effect. In addition, not all chitin synthases were regulated by the CWI MAPK pathway, because the genome database survey indicated that there were nine putative chitin synthases, of which only four were specifically affected by the CWI MAPK component mutations. Similarly, 18 of 21 glycoside hydrolases, which were assumed to be involved in the cross-linking of cell wall components, were differentially regulated in TdBCK1; most of these were down-regulated, and only two were up-regulated. Nine genes were affected in TdSLT2-69, all of which were also affected in TdBCK1 in the same regulatory direction.

GO analysis was performed in TdBCK1-S1 compared to its parental TdBCK1 strain (Fig. 4), revealing enrichment of the biological process and molecular function categories. In the biological process categories, metabolic process was the dominant category, followed by binding, ATP-binding, transport, zinc ion binding, and proteolysis, with more than 15 DEGs. The top two categories in the cellular component ontology were integral component of membrane and membrane. In addition, catalytic activity and oxidoreductase activity were the top categories in molecular function. GO analysis of the comparison between TdSLT2-69 and TdSLT2-69-S1 revealed enrichment of the biological process and molecular function categories. Metabolic process, proteolysis, transport, and binding were the top four categories in the biological process ontology. Although the dominant categories of the TdSLT2-69-S1 comparison were similar to those of the TdBCK1-S1 comparison i.e., integral component of membrane and membrane were the top two categories in the cellular component and catalytic activity and oxidoreductase activity were the top two categories in the molecular function categories, respectively, there were specific enriched categories for each comparison.

More GO categories per DEG were detected from the comparisons of the sectored progeny and their corresponding parental strains than between the mutant strains and the wild-type. This suggested that sectorization affected a broader and different spectrum of genes. Among the three chitin synthases affected in the mutants from the wild-type, only one chitin synthase gene was differentially regulated in TdBCK1-S1 but not in TdSLT2-69-S1, and the direction of alteration differed (Fig. 3A). Likewise, among the 18 glycoside hydrolase DEGs in TdBCK1, 8 were affected, all of which showed opposing regulatory directions (Fig. 3B). More interestingly, three new glycoside hydrolases were up-regulated in TdBCK1-S1 (Fig. 3B). Of the nine affected DEGs in TdSLT2-69, only one was differentially expressed in TdSLT2-69-S1 with the opposite regulatory direction, which was the only one differentially regulated in both comparisons i.e., downregulated in both mutants from the wild-type and upregulated in the sectored progenies from the parental mutant strains. Considering that phenotypic changes occurred to a lesser extent in TdSLT2-69 than in TdBCK1, it was expected that more DEGs of cell wall-synthesizing enzymes would be identified in the comparison of TdBCK1. In addition, robust mycelial growth in the sectored progenies was evidenced by the opposite direction of the transcriptional alteration of the sectored progenies compared to the parental mutant strains. However, only one DEG encoding a putative glycoside hydrolase family 16 protein was common in all comparisons, i.e., up-regulated in the TdBCK1 and TdSLT2-69 mutants vs. the wild-type and down-regulated in the TdBCK1-S1 and TdSLT2-69-S1 vs. the parental mutants. These results suggest that a sectorization-specific, rather than a cell wall integrity-specific, transcriptional regulatory mechanism exists and common DEGs of the comparisons between TdBCK1-S1 vs. TdBCK1 and TdSLT2-69-S1 vs. TdSLT2-69 are likely to play important roles in sectorization. Thus, GO analysis was performed for the 73 common DEGs, affected in both TdBCK1-S1 vs. TdBCK1 and TdSLT2-69-S1 vs. TdSLT2-69 (Fig. 5). Proteolysis, metabolic process, and ATP-binding were the top three categories in the biological process ontology. Membrane and integral component of membrane were the two dominant cellular component categories. Aspartic-type endopepsidase activity, catalytic activity, and oxidoreductase activity were the top three assign terms in the molecular function. These results suggest that sectorization is a complex trait involving a broad spectrum of target genes that affect fungal physiology, membrane function, and redox potential.

Additionally, DEGs were subjected to KEGG pathway analysis. Biosynthesis of other secondary metabolites was the most prominent pathway in the TdBCK1 mutant (Fig. 6). Moreover, amino acid metabolism, carbohydrate metabolism, and lipid metabolism pathways were significantly represented. Signal transduction pathways, including the MAPK signaling pathway and phosphatidylinositol signaling system, were also represented in up- and down-regulated genes in TdBCK1. In the TdSLT2-69 mutant, the biosynthesis of other secondary metabolites was again the most represented pathway, whereas the other significantly represented KEGG pathways were similar to those in TdBCK1. When comparing the sectored progenies to those of the parental mutant strains, biosynthesis of other secondary metabolites, amino acid metabolism, and carbohydrate metabolism pathways were significantly represented in TdBCK1-S1 (Fig. 7). However, the dominance of metabolic process pathways, such as biosynthesis of other secondary metabolites, amino acid metabolism, and carbohydrate metabolism, were diminished in TdSLT2-69-S1. For the 73 common DEGs, biosynthesis of other secondary metabolites was one of the two most represented pathways, with a total of 5 DEGs (Fig. 8). However, other metabolic KEGG pathways, such as amino acid metabolism, carbohydrate metabolism, and lipid metabolism pathways, were represented by only one or two DEGs. Interestingly, the MAPK signaling pathway was continuously represented with a single DEG, and the translation pathway was the most represented, with 5 DEGs. Considering that there were no DNA sequence changes in the sectored progenies from their corresponding parental strains but that there were enormous inheritable phenotypic changes, the numerous DEGs in RNA metabolism, such as RNA degradation, mRNA surveillance, and RNA transport, suggested the existence of a genetic mechanism governing these inheritable phenotypic changes. A recent study revealed the global epigenetic changes accompanying sectorization (So et al., 2018). Thus, it would be interesting to examine the regulation of genes related to the biosynthesis of other secondary metabolites.

The 73 genes were further categorized by protein function (Table 2). In total, 24 genes were classified as genes involved in metabolic processes, and 19 were classified as genes involved in signal transduction, such as transcription factors or effectors. Nine genes were identified in both of structural proteins and transport, and two were involved in redox. Interestingly, five Kelch-domain containing isoforms were identified as overrepresented genes in the sectored progenies. Five genes were identified as unknown functions. Therefore, genes related to transcription factors should be analyzed for their regulation and downstream effectors for inheritable sectored phenotypes. Among these 73 DEGs, 22 DEGs which were affected by the hypovirus CHV1 infection are interesting (Table 2: * indicates DEGs affected by the hypovirus infection.). Hypoviral infection in C. parasitica resulted in characteristic phenotypic changes of hypovirulence and other associated symptoms such as reduced pigmentation and conidiation i.e., in general preventing appropriate development of a host fungus and having the fungal host remain in juvenile stage without further differentiation (Dawe and Nuss, 2001). Moreover, other studies demonstrated the occurrence of sectoring morphology due to the mycovirus infections (Bhatti et al., 2011; Hammond et al., 2008; Wu et al., 2007). Therefore, these genes might represent the least number of hypoviral-specific sectorization effectors. The predicted function analysis of these hypoviral-and sectorization-specific genes suggested that five were classified as those related to genetic information processing such as helicase, DNA-binding protein, and DNA methyltransferase, which suggested the implication of specific regulatory mechanism for the process leading to sectorization. These results were in good agreement with recent study demonstrating global changes in DNA methylation in sectored progenies (So et al., 2018). Interestingly, the cryparin gene, a highly expressed hydrophobin gene specifically down-regulated by hypovirus infection, was one of seven structural genes. A recent study revealed that cryparin expression was transcriptionally affected by three fungal representative MAPK signal transduction pathways (So and Kim, 2017). Our transcriptomic analysis showed down-regulation of cryparin in the CWI mutants such as TdBCK1 and TdSLT2-69 compared to the wild-type and up-regulation in the sectored progenies compared to the corresponding parental strains, although cryparin expression was not recovered to the level of that in the wild-type, which was consistent with previous real time RT-PCR results (So and Kim, 2017). Considering the cellular location and biological function of cryparin (Kazmierczak et al., 2005; Kim et al., 2008), cryparin like other structural genes is highly unlikely to be the cause of sectorization; it is more likely the result of sectorization because it is necessary to support robust growth of mycelia. However, the regulatory hierarchy of cryparin expression helps to clarify the onset of the sectorization phenotype.

Validation of DEGs by qRT-PCR analysis

To validate the DEGs, quantitative real-time RT-PCR (qRT-PCR) analysis was conducted on 18 candidate genes (Table 3), which represented all genes showing the significant fold changes caused by the presence of hypovirus as well as sectorization. Among these, 10 genes were upregulated and eight were downregulated in the presence of hypovirus. In addition, compared to the corresponding parental strains, 11 genes were upregulated and seven were downregulated in the sectored progenies. The expression of each gene was calculated using the 2−ΔΔCT method, as previously described (Livak and Schmittgen, 2001). All selected genes showed expression levels in good agreement with those from the RNA-Seq analysis. Moreover, the linear regression analysis between two platforms of RNA-Seq and qRT-PCR indicated that the changes in the expression levels of target genes estimated by both platforms were highly correlated with R2 values of 0.9181, 0.9512, and 0.7958 for comparisons between TdBCK1-S1 vs. TdBCK1, TdSLT2-69-S1 vs. TdSLT2-69, and UEP1 vs. EP155/2, respectively. Therefore, the qRT-PCR results validated the RNA-Seq analysis results (Fig. 9, Supplementary Table S2).

CONCLUSION

Genome-wide transcriptomic analysis of Cryphonectria parasitica mutants in components (i.e., orthologs of yeast Bck1 and Slt2) of CWI-related MAPK pathways revealed a broad spectrum of metabolic changes accompanying the CWI MAPK pathway. Additionally, 70% of differentially expressed genes were common, and the remainder were specific to the components, suggesting that the orthologs of yeast Bck1 and Slt2 were in the same CWI MAPK pathway, although some component-specific genes existed. Then, sectorization-specific transcripts analysis between sectored progenies and their corresponding parental mutant strains demonstrated that 73 genes were commonly affected between sectored progenies and parental mutants. Moreover, according to gene ontology (GO) analysis, all 73 genes were enriched in secondary metabolism and translation. Functional analysis of the 73 genes indicated that many genes were implicated in signaling pathways, suggesting that several regulatory pathways are important in sectorization. Fungal sectorization, defined as robust growth without differentiation, occurs in many fungi. However, our understanding of the genetic mechanism underlying sectorization is limited. This study helps to clarify the genes of importance in sectored progenies and the molecular consequences of fungal sectorization.

Supplementary Information
ACKNOWLEDGMENTS

This work was supported by the NRF grants by MSIP (2015R1A2A1A10055684 and 2018R1A2A1A05078682). Y-H. Ko were supported by BK21 PLUS program in the Department of Bioactive Material Sciences.

FIGURES
Fig. 1. Venn diagram of DEGs in a pair-wise comparison of the strains. The DEGs are represented between the strains compared, and the arrows indicate up- and down-regulation. The 51 overlapping DEGs caused by the CpBck1 and CpSlt2 mutations as well as sectorization are indicated at the ends of the arrows from the two Venn diagrams.
Fig. 2. GO analysis of up- and down-regulated DEGs in TdBCK1 (blue) and TdSLT2-69 (red) compared to the wild-type. All DEGs were further classified into biological process, cellular component, and molecular function sub-categories. The filled bars represent the upregulated DEGs, and the dashed bars represent the down-regulated DEGs.
Fig. 3. Heat maps showing up- and down-regulated (A) chitin synthase and (B) glycoside hydrolase involved in fungal cell wall formation. Green shading represents up-regulated expression, and red shading represents down-regulated expression. Strains (column) and genes (row) are hierarchically clustered based on Pearson’s correlation.
Fig. 4. GO analysis of up- and down-regulated DEGs in TdBCK1-S1 (blue) and TdSLT2-69-S1 (red) compared to their respective parental strains. All DEGs were further classified into biological process, cellular component, and molecular function sub-categories. The filled bars represent the up-regulated DEGs, and the dashed bars represent the down-regulated DEGs.
Fig. 5. GO analysis of up- and down-regulated common DEGs in TdBCK1-S1 and TdSLT2-69-S1 compared to their parental strains. All DEGs were further classified into biological process, cellular component, and molecular function sub-categories. The black-filled bars represent the up-regulated DEGs, and the gray-filled bars represent the down-regulated DEGs.
Fig. 6. KEGG pathway analysis of DEGs in TdBCK1 (blue) and TdSLT2-69 (red) compared to the wild type. The filled bars represent the up-regulated DEGs, and the dashed bars represent the down-regulated DEGs.
Fig. 7. KEGG pathway analysis of DEGs in TdBCK1-S1 (blue) and TdSLT2-69-S1 (red) compared to their parental strains. The filled bars represent the up-regulated DEGs, and the dashed bars represent the down-regulated DEGs.
Fig. 8. KEGG pathway analysis of common DEGs in TdBCK1-S1 and TdSLT2-69-S1 compared to their parental strains. The black-filled bars represent the up-regulated DEGs, and the gray-filled bars represent the down-regulated DEGs.
Fig. 9. The expression patterns of 18 genes were validated by RT-PCR. The X-axis represents log2 FC expression levels based on RNA-seq data. The Y-axis represents log2 FC expression levels based on real-time RT-PCR data. The data of linear regression analysis represented a significant correlation between the two platforms. R squared correlation values are indicated for each regression line.
TABLES

Table 1

DEGs with |log2 (fold change)|>4 in TdBCK1-S1 compared to TdBCK1 and TdSLT2-69-S1 compared to TdSLT2-69, respectively

JGI gene IDLog2 (FC): TdBCK1-S1 vs. TdBCK1Function description
2459029.59Cytochrome P450
356398.52Cytochrome P450
3249478.31Hypothetical protein
2484988.02Trichothecene 3-O-acetyltransferase
328247.67AndM
2838136.37Hypothetical protein
2450116.37Polyketide synthase
3388526.32Polyketide synthase 4
3237066.13
2583425.60Putative sodium phosphate protein
855785.49
3468105.47Putative nacht domain protein
1003835.39Cryparin
2783075.13Putative 3-amino-3-carboxypropyl transferase
3560224.62Putative acid phosphatase
396634.41Putative mfs multidrug protein
2872304.20Putative major facilitator superfamily transporter phosphate
793184.18Putative 3-phytase a
336241−4.28Putative calcium-translocating p-type atpase
323670−4.43Specific serine endopeptidase
354483−4.64LysM domain protein, putative
86125−5.56Acyl transferase/acyl hydrolase/lysophospholipase
13424−7.22Putative TPA
343514−9.83Putative aristolochene synthase protein

JGI gene IDLog2 (FC): TdSLT2-69-S1 vs. TdSLT2-69Function description

8557810.19
2583424.77Putative sodium phosphate protein

Table 2

Functional study of common DEGs in comparisons between TdBCK1-S1 vs. TdBCK1 and TdSLT2-69-S1 vs. TdSLT2-69

JGI gene IDClassificationFunction description
338852MetabolismPolyketide synthase 4
258342TransportPutative sodium phosphate protein
85578*Unknown
356022*MetabolismPutative acid phosphatase
287230TransportPutative major facilitator superfamily transporter phosphate
358623MetabolismPutative Acetyl-coenzyme A carboxylase carboxyl transferase
245772TransportPutative potassium uptake protein
104198*StructureCryparin
67838*Unknown
330996*Signal transductionS-adenosylmethionine-dependent methyltransferase-like protein
259317MetabolismPredicted protein
321522RedoxFerric reductase transmembrane
89535MetabolismPutative 3-phytase b precursor
355825*TransportPutative mfs peptide
348629*Signal transductionPhosphoesterase
273248TransportPutative low affinity copper transporter
109204*Signal transductionPutative zinc finger SWIM domain protein
358511*protein-protein contact siteKelch domain protein
356120*protein-protein contact siteKelch domain protein
357650*protein-protein contact siteKelch domain protein
322606*protein-protein contact siteKelch domain protein
358636*protein-protein contact siteKelch domain protein
346194Signal transductionPutative ankyrin repeat protein nuc-2
357023MetabolismPutative 5 3nucleotidase
355095UnknownHypothetical protein UCRPA7_2558
248975*UnknownPutative gpi anchored
284134*RedoxPutative ferric-chelate reductase
324303MetabolismPutative like family protein
280920MetabolismPutative fatty acid desaturase
320470StructurePutative cell wall protein
297116TransportH/K ATPase alpha subunit
322307MetabolismPutative perilipin mpl1-like protein
356370TransportHypothetical protein CY34DRAFT_9264
58880Structure2og-fe oxygenase
82322MetabolismPutative glycoside hydrolase family 16 protein
355877MetabolismCarboxypeptidase
346721MetabolismPutative dipeptidyl-peptidase 3
276559Signal transductionDCL2_CRYPA RecName
357263*Signal transductionHypothetical protein V492_01086
255785Signal transductionMultiple ankyrin repeats single kh domain protein, putative
82840MetabolismIBR domain-containing protein
257472Signal transductionDEAD/DEAH box helicase
349858MetabolismHypothetical protein SCLCIDRAFT_13763
287030*Signal transductionPutative wd g-beta repeat containing protein
256253StructurePutative von willebrand factor
233964MetabolismHypothetical protein PFICI_15243
270725Signal transductionSir2 family protein
338832MetabolismFtsJ-like methyltransferase family protein
297135UnknownHypothetical protein UCDDA912_g00277
267037StructureHeterokaryon incompatibility protein (het-6OR allele)
320080Signal transductionPutative rRNA-processing protein efg1
320079Signal transductionPutative zinc finger protein 251
70047StructureHypothetical protein S40293_10774
356222StructurePutative tetraspanin tsp3
249345MetabolismHypothetical protein S40285_08606
291140MetabolismPutative nad-dependent malic enzyme
345956MetabolismPutative aaa family
92416Signal transductionHypothetical protein UCDDA912_g10323
287505MetabolismAdenine phosphoribosyltransferase
260943Signal transductionP-loop containing nucleoside triphosphate hydrolase protein
290551MetabolismPutative secreted aspartic proteinase protein
234408Signal transductionPutative ankyrin-like protein
356265*StructurePutative erythrocyte band 7 integral membrane protein
355167Signal transductionPutative cysteine and glycine-rich protein 3
225273*StructureHypothetical protein CMQ_390
108074TransportHypothetical protein UCRPA7_801
105038*MetabolismPutative catalase 1
324353*MetabolismAspergillopepsin-2
342244*Signal transductionPutative aaa family
285834Signal transductionPutative BTB/POZ domain protein
342243Signal transductionPutative geranylgeranyl pyrophosphate synthetase protein
358242MetabolismPutative pepsin a1
336241TransportPutative calcium-translocating p-type ATPase

*

indicates DEGs affected by the hypovirus infection


Table 3

Genes used for qRT-PCR analysis

JGI gene IDClassificationFunction description
356022MetabolismPutative acid phosphatase
104198StructureCryparin
330996Signal transductionS-adenosylmethionine-dependent methyltransferase-like protein
355825TransportPutative mfs peptide
348629Signal transductionPhosphoesterase
358511Protein-protein contact siteKelch domain
284134RedoxPutative ferric-chelate reductase
356265StructurePutative erythrocyte band 7 integral membrane protein
225273StructureHypothetical protein
105038MetabolismPutative catalase
324353MetabolismAspergillopepsin-2
342244Signal transductionPutative aaa family
287030UnknownPutative WD-40, G-beta repeat containing
357263UnknownHypothetical protein
109204UnknownUnknown
248975UnknownUnknown
67838UnknownUnknown
85578UnknownUnknown

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30 April 2019 Volume 42,
Number 4, pp. 285~377

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