Skip to main content
PLOS One logoLink to PLOS One
. 2021 Dec 16;16(12):e0261487. doi: 10.1371/journal.pone.0261487

Genomic analysis of Elsinoë arachidis reveals its potential pathogenic mechanism and the biosynthesis pathway of elsinochrome toxin

Wenli Jiao 1, Mengxue Xu 1, Rujun Zhou 1,*, Yiwei Fu 1, Zibo Li 1, Caiyun Xue 1
Editor: Zonghua Wang2
PMCID: PMC8675698  PMID: 34914789

Abstract

Elsinochromes (ESCs) are virulence factors produced by Elsinoë arachidis which is the cause of peanut scab. However, the biosynthesis pathway of ESCs in E. arachidis has not been elucidated and the potential pathogenic mechanism of E. arachidis is poorly understood. In this study, we report a high-quality genome sequence of E. arachidis. The size of the E. arachidis genome is 33.18Mb, which is comparable to the Ascomycota genome (average 36.91 Mb), encoding 9174 predicted genes. The self-detoxification family including transporters and cytochrome P450 enzymes were analysis, candidate effectors and cell wall degrading enzymes were investigated as the pathogenicity genes by using PHI and CAZy databases. Additionally, the E. arachidis genome contains 24 secondary metabolism gene clusters, in which ESCB1 was identified as the core gene of ESC biosynthesis. Taken together, the genome sequence of E. arachidis provides a new route to explore its potential pathogenic mechanism and the biosynthesis pathway of ESCs.

Introduction

Elsinoë arachidis is a phytopathogenic fungus that causes peanut scab on Arachis hypogaea Linn., resulting in tremendous yield loss (regional losses can be greater than 50%) in peanut planting regions in China [1, 2]. Currently, disease occurrence patterns have been determined. However, the mechanism of host-pathogen interactions is largely unknown, indicating that new and effective prevention and control mechanisms of E. arachidis are urgently needed [36].

Interestingly, several Elsinoë produce elsinochromes (ESCs) [7], which are red, photosensitive, perylenequinone toxins. Previously, ESCs have been shown to promote electrolyte leakage, peroxidation of the plasma membrane, and production of reactive oxygen species such as superoxide (O2). Additionally, ESCs contribute to pathogenesis and are essential for full virulence which was validated by constructing mutants in E. fawcettii of a polyketide synthase-encoding gene which is the core gene of ESC biosynthesis [810]. Cercosporin (Cercospora spp.) is the most well-known member of the group of perylenequinone fungal toxins. The biological functions and biosynthetic pathway of cercosporin have been clarified. Like many toxins identified in ascomycete fungi, its metabolic pathway is dependent on polyketide synthase (PKS) [11], and the other gene functions in the PKS gene clusters have also been determined. However, the biosynthetic pathway of ESCs in E. arachidis and their potential pathogenic mechanism remain to be explored. For instance, it is unclear whether, in addition to ESCs, there exist cell wall degrading enzymes or effectors that act as virulence factors in E. arachidis [12].

A growing number of studies have applied genome sequencing technology to the study of phytopathogenic fungi, such as Magnaporthe oryzae [13], Fusarium graminearum [14], Sclerotinia sclerotiorum and Botrytis cinerea [15], which has provided new research avenues for a better understanding of their genetic evolution, secondary metabolism, and pathogenic mechanisms.

The present study was aimed at exploring the possible virulence factors of E. arachidis during host invasion. We report on the 33.18Mb genome sequence of E. arachidis, the secondary metabolism gene cluster, and the discovery of 6 PKS gene clusters in E. arachidis including the ESC biosynthetic gene cluster and the core gene ESCB1. Through our analysis of the whole genome, we show that E. arachidis has a complex pathogenesis, with, in addition to the toxin, several candidate virulence factors including effectors, enzymes, and transporters. Moreover, the putative pathogenicity genes provide new horizons to unravel the pathogenic mechanism of E. arachidis.

Materials and methods

Whole-genome sequencing and assembly

In this paper, we used E. arachidis strain LNFT-H01, which was purified by single spores and cultured on potato dextrose agar (PDA) under 5 microeinstein (μE) m-2s-1. The genome of LNFT-H01 was sequenced by PacBio RS II using a 20kb library of LNFT-H01 genomic DNA under 100 ×sequencing depth and assembled by Canu [1618]. The assembled whole-genome sequence, totaling 33.18 Mb and containing 16 scaffolds, was submitted to NCBI (GenBank accession JAAPAX000000000). The characteristics of the genome were mapped in a circus-plot.

Phylogenetic and syntenic analysis

The evolutionary history can be deduced from conserved sequences and conserved biochemical functions. In addition, clustering the orthologous genes of different genomes can be helpful to integrate the information of conserved gene families and biological processes. We calculated the closest relatives to sequences from E. arachidis within reference genomes by OrthoMCL, then constructed a phylogenetic tree by SMS implemented in the PhyML (http://www.atgc-montpellier.fr/ phyml-sms/) [19, 20]. Syntenic regions between E. arachidis and E. australis were analyzed using MCScanX, which can effectively determine the changes in chromosome structure and reveal the history of the gene family expansion [21].

Repetitive sequence

Due to the low conservation of repetitive sequence (RS) between species according to MITE Hunter, LTR FINDER, Repeat Scout, and PILER [2225], we exploited the genome sequence to established a RS database, classified and merged by PASTEClassifier and Repbase [26, 27]. Finally, we predicted the repetitive sequences with RepeatMasker [28].

Gene prediction and annotation

The ab initio-based and homology-based methods were performed to predict gene numbers in the E. arachidis genome. A combination of Augustus, Glimmer HMM, Genscan GeneID, and SNAP [2932] homology-based methods were used by GeMoMa [33] and the results were integrated using EVM [34]. Non-coding RNA including rRNA, tRNA, and other RNAs were also classified and analyzed. According to the structural characteristics of different non-coding RNAs, different strategies were used to predict different non-coding RNAs. Based on the Rfam [35] database, Blastn [36] was used to identify rRNA. We used tRNAscan-SE [37] to identify tRNA. As for the pseudogenes, which have similar sequences to functional genes but have lost their original functions due to mutations, we searched for homologous sequences in the genome through BLAT [38] alignment, and we then used GeneWise [39] to search for immature stop codons and frameshift mutations in the gene sequence to obtain pseudogenes. The preliminary functional annotation was conducted with multiple databases, including the Pfam, NR, KOG/COG, KEGG, and GO databases [4043]. The pathogen-host interaction (PHI) database, carbohydrate-active enzymes (CAZy) database, and transporter classification database (TCDB) were used to identify potential virulence-related proteins [4446].

Identification and characterization of polyketide synthases (PKSs) and secondary metabolite clusters

Secondary metabolite clusters were predicted by performing antiSMASH2 (https://fungismash.Secondarymetabolites.org). In order to confirm the function of polyketide synthase (PKS), which is the core protein that responsible for the biosynthesis of mycotoxin in different organisms, PKS sequences were used to construct the phylogenetic tree by MEGA 10.0.5. The detailed information on PKS is reported in S9 Table. Domains of PKSs were identified via InterPro (https://www.ebi.ac.uk/interpro) and their location visualized by DOG 2.0.

ESCB1 expression and toxin determination

Elsinochrome extraction and quantitation were performed as previously described [12]. As for ESCB1 expression, the strain used for the colony culture was the same as for toxin extraction. Total RNA extraction was done using TransZolTM Up Plus RNA kit (Beijing, TransGen Biotech). RT-PCR was performed using TransScript® One-Step gDNA Removal and cDNA Synthesis (Beijing, TransGen Biotech). qPCR was done using SuperMix TransStart® Green qPCR SuperMix with primers ESCB1F (ATCCGAGGTCATTGGTGATG) and ESCB1R (GAGGTTGACATCTGGC ATTTG).

Results

The characteristics of the whole-genome

Whole genome sequencing of E. arachidis was performed using PacBio RS II (100×coverage). A total of 6.28 Gb high-quality sequencing raw data were assembled by CANU into 16 scaffolds (N50, 3,376,838bp) and the characteristics that are displayed in a circus-plot (Fig 1). We analyzed the genome sequence through Augustus [29] and we identified 7,950 genes. In order to obtain accurate information, we further performed a combination of Glimmer HMM (9,277), Genscan (6,599), GeneID (11,100), and SNAP (10,175) [3032]. By homology-based methods using GeMoMa [33], taking E. australis as a reference genome, 8,339 genes were predicted. The above results were integrated by EVM [34] showing that the E. arachidis genome contains 9,174 genes (Table 1). KOG, KEGG, and GO annotation were in S1 Fig.

Fig 1. Circos-plot of E. arachidis.

Fig 1

The outermost circle is the size of the genome, each scale is 5 Kb; the second circle and third circle are the genes on the positive and negative strands of the genome, respectively (different colors represent different COG functional); the fourth circle is repeated sequence; the fifth circle is tRNA and rRNA (blue: tRNA, purple: rRNA); the sixth circle is GC content (light yellow: the GC content is higher than the average GC content, blue: the GC content is lower than the average GC content); the innermost circle is GC-skew (dark gray: the G content is greater than C, red: the C content is greater than G).

Table 1. Gene annotation summary statistics.

Genome features
Genome assembly (Mb) 33.18
Number of coding sequence genes 9,174
GC Content (%) 48.24
PHI 2,752
Secreted protein 734
Transmembrane protein 1,829
TCDB 124

The assembled size of the E. arachidis genome (33.18 Mb) was comparable in size to the Ascomycota genome (36.91 Mb) [47], as well as M. oryzae, (38.10 Mb), Fusarium graminearum (35.45 Mb), and Sclerotinia sclerotiorum (38.68 Mb). However, phylogenetic analysis showed that the species used in this comparative study were distinct from one another. Notably, E. arachidis was only close to Sphaceloma murrayae and E. australis (S2A Fig), but in terms of genome size, E. arachidis was larger than S. murrayae (20.72 Mb) or E. australis (23.34 Mb). Additionally, synteny analysis indicated the highest synteny between E. arachidis and E. australis (S2B Fig). Concerning the identification of repetitive DNA sequences, among 33,184,353bp of the E. arachidis genome, a total of 7,033,311bp (21.20%) repeat sequences were identified including LTR retrotransposons and DNA transposons (S1 Table).

Genes associated with detoxification

Transporters

Transporters are membrane-associated proteins that can assist the movement of ions, amino acids, and macromolecules across the membrane, which plays an important role in a broad range of cellular activities such as nutrient uptake, the release of secondary metabolites, and signal transduction [48]. The major facilitator superfamily (MFS) and ATP-binding cassette (ABC) transporter superfamily are the two largest families of fungal transporters [48]. Among these, the ABC transporters are the primary active transporters, usually as part of multicomponent transporters, that transport different compounds including polysaccharides, heavy metals, oligopeptides, and inorganic ions. In addition, MFS transporters are secondary carriers that facilitate the secretion of endogenous fungal toxins, such as aflatoxins, trichothecenes, and cercosporin. A large number of ABC genes (57) and MFS genes (190) were found in E. arachidis (S2 Table), which represents 57% of the total number of transporters (Fig 2A). EVM0006810.1, EVM0008188.1, EVM0008646.1, EVM0004073.1, EVM0001603.1, EVM0008241.1, EVM0001951.1, EVM0000776.1, EVM0002663.1 are related to CTB4 (Table 2), which encodes the MFS transporter, and are located in the cercosporin biosynthetic gene cluster. They play a role in the secretion of cercosporin in Cercospora nicotianae and are involved in cercosporin resistance [49]. ESC, biosynthesized by E. arachidis, produces reactive oxygen species in the light acting on the cell membranes and destroying the cellular structure. Meanwhile, E. arachidis can grow and develop in the presence of high concentrations of reactive oxygen species, which suggesting the certain detoxification of E. arachidis. The ABC and MFS transporters may play functional roles in the secretion of toxins and play an important role in the virulence toward the plant.

Fig 2. Characteristic of E. arachidis genome.

Fig 2

(A) Transporters of E. arachidis genome. (B) pathogen-host interaction genes in E. arachidis genome. (C) CAZymes in compared genomes. (D) Annotation of pectin and cellulase in E. arachidis.

Table 2. Detoxification genes in E. arachidis genome involved in PHI data.
gene ID PHI annotation ID Species
EVM0008224.1 MoCYP51B G4MZG5 Magnaporthe oryzae
EVM0001153.1
EVM0007235.1 CYP52X1 E2EAF6 Beauveria bassiana
EVM0005183.1
EVM0001975.1
EVM0006634.1
EVM0000493.1
EVM0002264.1
EVM0004742.1
EVM0009006.1
EVM0000504.1
EVM0001795.1
EVM0000711.1 cyp51 ABO93363 Mycosphaerella graminicola
EVM0007202.1
EVM0001836.1 CYP51C I1S2M5 Fusarium graminearum
EVM0002479.1
EVM0006459.1
EVM0007408.1 CYP1 AAG13968 Magnaporthe oryzae
EVM0001986.1
EVM0006925.1 Cyp51A I6YDU0 Fusarium graminearum
EVM0007146.1 GcABC-G1 F0XP73 Grosmannia clavigera
EVM0002882.1
EVM0000604.1 ABC2 BAC67162 Magnaporthe oryzae
EVM0002213.1 ABC3 Q3Y5V5 Magnaporthe oryzae
EVM0005164.1
EVM0004737.1
EVM0003047.1
EVM0005246.1
EVM0005274.1
EVM0001881.1
EVM0000747.1
EVM0001190.1
EVM0008962.1
EVM0000397.1
EVM0003703.1 ABC4 MGG_00937 Magnaporthe oryzae
EVM0007439.1
EVM0002950.1
EVM0008152.1
EVM0003958.1
EVM0000032.1 MgMfs1 A4ZGP3 Mycosphaerella graminicola
EVM0007852.1
EVM0005092.1
EVM0007310.1
EVM0003855.1
EVM0006410.1
EVM0002601.1
EVM0005626.1
EVM0004539.1 BCMFS1 AAF64435 Botrytis cinerea
EVM0006582.1
EVM0002249.1
EVM0002459.1
EVM0006810.1 CTB4 A0ST42 Cercospora nicotianae
EVM0008188.1
EVM0008646.1
EVM0004073.1
EVM0001603.1
EVM0008241.1
EVM0001951.1
EVM0000776.1
EVM0002663.1

Cytochrome P450

The cytochrome P450 enzymes (CYPs) are multifunctional oxidoreductases that can aid in the detoxification of natural and environmental pollutants, involved in the primary and secondary metabolism [50]. A total of 78 CYPs (S3 Table) were predicted in E. arachidis genome, of which 20 CYPs were analyzed in the PHI data (Table 2), mainly including the CYP51 and CYP52 families. The CYP51 families, the conserved fungal P450, are involved in the biosynthesis of membrane ergosterol. MoCYP51B and MoCYP51A both encode a sterol 14α-demethylase enzyme in M. oryzae that is required for conidiogenesis and mediating the action of sterol demethylation inhibitor (DMI) fungicides [51]. CYP52X1, a member of the CYP52 family, are involved in the degradation of specific epidermal lipid components in the insect waxy layer [52]. In general, the CYPs may be involved in the detoxification of the pathogen’s own toxins.

Analyses of pathogenicity proteins encoded by the E. arachidis genome

Through the pathogen-host interaction database, 2,752 potential pathogenic genes were screened in E. arachidis (Fig 2B), mainly concerning the increased virulence and effectors, the loss of pathogenicity, and reduced virulence as shown in S4 Table.

Effectors

During the interaction between pathogens and hosts, pathogens can produce different effector proteins to change the cell structure and metabolic pathways of the host plants, thereby promoting successful infection of the host plants or triggering host defense reactions. In total, 734 genes were predicted to code for secreted proteins in the E. arachidis genome. Analysis of the PHI database revealed 25 candidate effectors (Table 3) including EVM0006757.1, a gene homologous to PemG1, an elicitor-encoding gene of Magnaporthe oryzae which triggered the expression of phenylalanine ammonia-lyase gene [53] and EVM0003806, a gene homologous to glucanase inhibitor protein GPI1 [54] secreted by Phytophthora sojae, which inhibits the EGaseA mediated release of elicitor active glucan oligosaccharides from P. sojae cell wall. The function of candidate effectors from E. arachidis needs further testing and verification, but also provides a novel research direction for the elucidation of pathogenic mechanisms.

Table 3. Effector candidates of E. arachidis in PHI database.
Effector Candidates PHI annotation ID Species
EVM0000548.1 ANP1 Q6FM27 Candida glabrata
EVM0002759.1 Atf1 I1S0C0 Fusarium graminearum
EVM0005988.1 ACE1 CAG28797 Magnaporthe oryzae
EVM0003884.1 BEC1005 CCU82697 Blumeria graminis
EVM0000372.1
EVM0004193.1
EVM0007602.1
EVM0008348.1
EVM0007402.1 BEC1019 KJ571201 Blumeria graminis
EVM0004104.1 BEC1040 CCU82707 Blumeria graminis
EVM0002180.1 FRE3 J9VNH2 Cryptococcus neoformans
EVM0005699.1
EVM0000237.1
EVM0003806.1 GIP1 AAL11720 Phytophthora sojae
EVM0003007.1 hopI1 AAL84247 Pseudomonas syringae
EVM0006701.1
EVM0001739.1 mkkA A0A068BFA5 Epichloe festucae
EVM0003220.1 MgSM1 MGG 05344 Magnaporthe oryzae
EVM0006757.1 PemG1 ABK56833 Magnaporthe oryzae
EVM0001649.1 So (soft) K9Y567 Epichloe festucae
EVM0005038.1
EVM0005550.1
EVM0009148.1
EVM0003400.1 T6SS2 Q6TKU1 Escherichia coli
EVM0008814.1

Carbohydrate-active enzymes

The cuticle and cell wall of plants are the primary barriers that prevent the invasion of pathogens. Therefore, the ability to degrade complex plant cell wall carbohydrates such as cellulose and pectin is an indispensable part of the fungal life cycle. The CAZymes secreted by pathogenic fungi are capable of degrading complex plant cell wall carbohydrates to simple monomers that can be used as carbon sources to help pathogen invasion [55]. Mapped E. arachidis genomes with CAZy database detected 602 genes potentially encoding CAZymes (S6 Table). Subsequently, we compared the CAZyme content to other ascomycetes including necrotrophic plant pathogens (S. sclerotiorum and B. cinerea), a biotrophic pathogen (B. graminis), and hemi-biotrophic pathogens (M. oryzae and F. graminearum) (Fig 2C, S7 Table). The CAZyme-content in E. arachidis is the largest in all compared fungi genomes. This suggests that the CAZymes content does not directly correlate with the lifestyle of the fungus. Further analysis showed, that the pectin and cellulase content of E. arachidis (39) was smaller than that of the necrotrophic plant pathogens S. sclerotiorum (53) and B. cinerea (62). However, it was significantly larger than that of B. graminis (2) (Fig 2D). In addition to cell wall degrading enzymes, different pathogens likely use different strategies to penetrate plant tissues.

Secondary metabolism

Gene clusters of PKS in E. arachidis

E. arachidis encodes 24 secondary metabolism clusters, including PKS (6), nonribosomal peptide synthetase (NRPS) (11), NRPS-PKS (1), terpene (6) (S3 Fig). The number of PKS clusters in E. arachidis were lower than in M. oryzae, similar to E. fawcettii and F. graminearum, but the number of NRPS clusters was twice that of E. fawcettii, indicating significant differences in metabolic pathways between E. fawcettii and E. arachidis (S4 Fig). We analyzed the PKS proteins from E. arachidis for conserved domains by InterProScan and visualized them using DOG 2.0. (Fig 3). We found that E. arachidis contains 8 different domains including KS, AT, TE, ER, KR, MeT, ACP, and DH. According to their domain structures, the 6 PKS genes could be further divided into reduced (EVM0002563, EVM0005988, EVM0006869) and non-reduced (EVM0003759, EVM0004732, EVM0005880) due to the reducing activity of ER and KR.

Fig 3. Structure of polyketide synthases proteins.

Fig 3

The conservative domain of polyketide synthases was clarified by InterProScan, and the visualization of different domains by using DOG 2.0.

In order to further differentiate the 6 PKS genes, 19 different PKS genes were analyzed (S8 Table). Among the 6 PKS from E. arachidis, EVM0003759 was in the same clade as EaPKS which is encoding for ESC biosynthesis in E. australis and therefore we named it ESCB1 (Elsinochrome Biosynthesis gene 1). Interestingly, EVM0004732 and EVM0005880 are related to the biosynthesis of melanin (Fig 4). This is the first time that melanin has been predicted in this pathogen. Whether melanin in E. arachidis plays a role in pathogenicity as it does in M. oryzae by aiding to penetrate the host plant remains to be verified.

Fig 4. Phylogenetic analyses of E. arachidis and other fungal polyketide synthases (PKS).

Fig 4

Phylogenetic tree was constructed with PKS sequences from different organisms which classified with the types of reducing domains are divided into five clades.

Expression of ESCB1 and analysis of flanking genes in E. arachidis

Noteworthily, we previously determined that the content of ESC in E. arachidis was obviously decreased under dark conditions [12]. We compared the toxin content and ESCB1 expression under light and dark conditions, as expected, the change tendency was similarity (Fig 5). 13 putative Open Reading Frames were identified in the flanking of ESCB1 (Fig 6), including EVM0001135 and EVM0007299 which encode O-methyltransferase, EVM0006582 and EVM0006794 similarity to MFS transporter, EVM0002495 Cytochrome P450, and EVM0002638 zinc finger transcription factor.

Fig 5. ESC and expression levels analysis of ESCB1.

Fig 5

ESC and expression levels of ESCB1 was investigated in light and dark condition, respectively.

Fig 6. Distribution of the ESCB1 gene cluster.

Fig 6

BLASTX was used to search the NCBI database to predict the function of related genes.

Discussion

Elsinoë species cause scab and spot anthracnose on various crops including peanut, cassava, citrus, mango, and grape. In this paper, the first whole genome sequence of E. arachidis were reported and revealed the complex gene structures that may be involved in its pathogenic mechanism. Additionally, we predicted the ESC toxin biosynthesis gene cluster. The genome size of E. arachidis is 33.18Mb, which was comparable in size to the Ascomycota genome size, however, compared with E. australis (23.34 Mb), E. arachidis has a larger genome size. This may be due to the lower proportion of repeat sequences in the E. fawcettii genome [56]. The GC content was 48.24% and CDSs percentage of the genome was 43.94%.

Mycotoxins play an important part in the pathogenic mechanisms of pathogens. Mycotoxin ESCs, perylenequinones photosensitive toxins, can produce reactive oxygen species (ROS) and act on the cell membrane to destroy the cell structure. E. arachidis can maintain growth and development even in the presence of high toxin levels, which indicates an efficient self-detoxification mechanism. We identified ABC transporters and MFS transporters in E. arachidis indicating the complex transportation of substances in E. arachidis and that some of them may have an effect on the secretion of ESCs. Cytochrome P450 enzyme system, a multifunctional oxidoreductase, may involve in the self-detoxification of E. arachidis by providing redox conditions to maintain its own steady state for various physiological and biochemical reactions.

ESC is a crucial virulent factor in the pathogenic process of E. arachidis. However, compared with mycotoxins such as aflatoxins, fumonisin, and trichothecenes, and host-selective toxins such as T-toxin, still little is known about the biosynthetic pathways of perylenequinone mycotoxins. Cercosporin, the same group of perylenequinone toxins with ESC, has been proved that CTB1 (cercosporin synthase gene 1) which encoding polyketide synthase is the core gene of cercosporin biosynthesis pathway [10]. Efpks1 has been shown to function the in ESC biosynthesis in E. fawcettii, but the specific biosynthesis pathway still needs to be further clarified [8, 9]. With the prediction of the secondary metabolism gene cluster of E. arachidis, 6 gene clusters related to polyketide synthase were obtained. The core genes were EVM0002563, EVM0003759, EVM0004732, EVM0005880, EVM0005988, and EVM0006869. Phylogenetic tree constructions showed that EVM0003759 is involved in ESCs synthesis, while EVM0004732 and EVM0005880 play a role in melanin synthesis. To our knowledge, this is the first time that melanin has been identified in E. arachidis. Interestingly, analysis of the position between the core genes of ESCs and melanin gene clusters, we found that the three genes are all located in Contig00003. This result also cast some doubt on whether PKS synthesis pathways from ESC and melanin are interrelated or competing.

Pathogens employ complex mechanisms to break through the defenses of plants, including toxins, enzymes, and other pathogenic factors to help invasion and colonization. Analysis of the CAZy and PHI databases revealed that, in addition to ESCs, enzymes, effectors, and certain transcription factors may be involved in the pathogenic process. Increased virulence factors (3%) that cause increased pathogenicity include O-methylsterigmatocystin oxidoreductase, AK-toxin biosynthetic gene 7 (AKT7) and bZIP transcription factor MeaB. EVM0005728, EVM0001699 and EVM0004784 are related to AKT7, which encodes a cytochrome P450 monooxygenase in Alternaria alternata and can limit the host-selective toxin AK-toxin production [57]. EVM0002472 is endowed with a basic leucine zipper (bZIP) domain similar to the MeaB transcription factor in Fusarium oxysporum [58], which activates a conserved nitrogen responsive pathway to control the virulence of plant pathogenic fungi (S5 Table).

In conclusion, we reported the whole-genome sequence of E. arachidis. Analysis of its assembly and annotation allowed the identification of the presumptive PKS gene clusters. Based on our results, we hypothesize that ESCB1 maybe the core gene of the biosynthesis of ESC. Additionally, pathogenic factors including CAZymes and effectors may help E. arachidis to circumvent the defense mechanisms of peanuts. Our work lays the foundation of future research aimed at elucidating the detailed pathogenic mechanisms of E. arachidis.

Conclusions

In conclusion, this is the first report of the high-quality genome of E. arachidis by PacBio RS II. The basic information of the sequence, gene family and metabolic gene cluster of E. arachidis were clarified. Through further analysis of the key genes in different PKS gene clusters, the expression of ESCB1 (EVM0003759) under light and dark condition was initially determined to participate in the ESC biosynthetic pathway, and the flanking sequences of this gene cluster were annotation, including major facilitator superfamily transporter, cytochrome P450, monooxygenase and O-methyltransferase. In addition to ESC toxins, genes related to mycotoxin biosynthesis such as melanin are also noted. This information provides new ideas for further exploration of the pathogenic mechanism of E. arachidis.

Supporting information

S1 Fig. GO, KOG and KEGG annotation of E. arachidis.

(TIF)

S2 Fig. Collinear analysis and evolutionary analysis of E. arachidis.

(A) A phylogenetic tree constructed the evolutionary relationships of E. arachidis and other fungi. (B) Collinear analysis.

(TIF)

S3 Fig. Gene clusters in E. arachidis.

(TIF)

S4 Fig. PKS, NRPS and NRPS-PKS hybrid in different genome.

(TIF)

S1 Table. Repetitive sequence in E. arachidis.

(DOC)

S2 Table. ABC transporter and major facilitator superfamily in E. arachidis.

(XLSX)

S3 Table. Cytochrome P450 in E. arachidis.

(XLSX)

S4 Table. The loss of pathogenicity and reduced virulence genes in E. arachidis.

(DOCX)

S5 Table. Increased virulence genes in E. arachidis.

(DOCX)

S6 Table. CAZyme_family in E. arachidis.

(XLSX)

S7 Table. CAZymes in E. arachidis and compared genome.

(DOCX)

S8 Table. The information of the different PKSs.

(DOC)

Acknowledgments

We would like to thank BioMarker for the much-valued help.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

Natural Science Foundation of Liaoning Province (CN) (2019-MS-278). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Zhou R J, Xu Z, Fu J F, Cui J C, He J J, Xue C Y. Resistance evaluation of peanut varieties to peanut scab and the epidemic dynamics in Liaoning Province. Acta Phytophylacica Sinica. 2014;41(5):597–601. doi: 10.13802/j.cnki.zwbhxb.2014.05.033 [DOI] [Google Scholar]
  • 2.Fang S M, Wang Z R, Ke Y Q, Chen Y S, Huang C M, Yu J X. The Evaluation of Resistance and Resistant Mechanisms of Peanut Varieties to Scab Disease. Scientia Agricultura Sinica. 2007; 40(2):291–297. doi: [DOI] [Google Scholar]
  • 3.Fan X L, Barreto R W, Groenewald J Z, Bezerra J D P, Pereira O L, Cheewangkoon R, et al. Phylogeny and taxonomy of the scab and spot anthracnose fungus Elsinoë (Myriangiales, Dothideomycetes). Studies in Mycology. 2017; 87(C):1–41. doi: 10.1016/j.simyco.2017.02.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Zhao J F, Zhou R J, Li Y J, Lu L, Fu J F, Xue C Y. Infectious condition of Sphaceloma arachidis and physiological responses to pathogen infection in peanut. Chinese Journal of Oil Crop Sciences. 2017; (1) doi: 10.7505/j.issn.1007-9084.2017.01.015 [DOI] [Google Scholar]
  • 5.Lu Liu, Wenli Jiao, Rujun Zhou, Yuanjie Li, Mengxue Xu, Junfan Fu. Extraction Technology and Activity Analysis of Elsinoë arachidis Toxin. Journal of Shenyang Agricultural University. 2018; 49(3): 272–278. doi: 10.3969/j.issn.1000-1700.2018.03.003 [DOI] [Google Scholar]
  • 6.Fang SM, Wang ZR, Guo JM. Fungicides selection for peanut scab disease, Chinese Journal of Oil Crop Sciences. 2006; 28, 220–223. [Google Scholar]
  • 7.Weiss U, Flon H, Burger WC. The photodynamic pigment of some species of Elsinoë and Sphaceloma. Arch Biochem Biophys. 1957;69:311–319. doi: 10.1016/0003-9861(57)90497-6 [DOI] [PubMed] [Google Scholar]
  • 8.Liao HL, Chung KR. Cellular toxicity of elsinochrome phytotoxins produced by the pathogenic fungus, Elsinoë fawcettii causing citrus scab. New Phytol. 2008;177(1):239–250. doi: 10.1111/j.1469-8137.2007.02234.x [DOI] [PubMed] [Google Scholar]
  • 9.Daub ME, Herrero S, Chung KR. Photoactivated perylenequinone toxins in fungal pathogenesis of plants. FEMS Microbiol Lett. 2005;252(2):197–206. doi: 10.1016/j.femsle.2005.08.033 [DOI] [PubMed] [Google Scholar]
  • 10.Choquer M, Dekkers KL, Chen HQ, et al. The CTB1 gene encoding a fungal polyketide synthase is required for cercosporin biosynthesis and fungal virulence of Cercospora nicotianae. Mol Plant Microbe Interact. 2005;18(5):468–476. doi: 10.1094/MPMI-18-0468 [DOI] [PubMed] [Google Scholar]
  • 11.Daub ME. Cercosporin, a photosensitizing toxin from Cercospora species. Phytopathology. 1982; 72, 370–374. doi: 10.1094/Phyto-77-370 [DOI] [Google Scholar]
  • 12.Jiao W, Liu L, Zhou R, Xu M, Xiao D, Xue C. Elsinochrome phytotoxin production and pathogenicity of Elsinoë arachidis isolates in China. PLoS One. 2019;14(6):e0218391. doi: 10.1371/journal.pone.0218391 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Dong Y, Li Y, Zhao M, et al. Global genome and transcriptome analyses of Magnaporthe oryzae epidemic isolate 98–06 uncover novel effectors and pathogenicity-related genes, revealing gene gain and lose dynamics in genome evolution. PLoS Pathog. 2015;11(4):e1004801. doi: 10.1371/journal.ppat.1004801 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Walkowiak S, Rowland O, Rodrigue N, Subramaniam R. Whole genome sequencing and comparative genomics of closely related Fusarium Head Blight fungi: Fusarium graminearum, F. meridionale and F. asiaticum. BMC Genomics. 2016;17(1):1014. doi: 10.1186/s12864-016-3371-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Amselem J, Cuomo C A, van Kan JA, et al., Genomic Analysis of the Necrotrophic Fungal Pathogens Sclerotinia sclerotiorum and Botrytis cinerea. PLoS Genetics. 2011, 7(8):e1002230. doi: 10.1371/journal.pgen.1002230 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Chin CS, Alexander DH, Marks P, et al. Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data. Nat Methods. 2013;10(6):563–569. doi: 10.1038/nmeth.2474 [DOI] [PubMed] [Google Scholar]
  • 17.Berlin K, Koren S, Chin C S, Drake J P, Landolin J M, and Phillippy A M. Assembling large genomes with single-molecule sequencing and locality-sensitive hashing. Nature biotechnology. 2015; 33, 623–630 doi: 10.1038/nbt.3238 [DOI] [PubMed] [Google Scholar]
  • 18.Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Res. 2017;27(5):722–736. doi: 10.1101/gr.215087.116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Lefort V, Longueville JE, Gascuel O. SMS: Smart Model Selection in PhyML. Mol Biol Evol. 2017;34(9):2422–2424. doi: 10.1093/molbev/msx149 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Li L, Stoeckert CJ Jr, Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13(9):2178–2189. doi: 10.1101/gr.1224503 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Wang Y, Tang H, Debarry JD, et al. MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 2012;40(7):e49. doi: 10.1093/nar/gkr1293 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Xu Z, Wang H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 2007;35(Web Server issue):W265–W268. doi: 10.1093/nar/gkm286 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Han Y, Wessler SR. MITE-Hunter: a program for discovering miniature inverted-repeat transposable elements from genomic sequences. Nucleic Acids Res. 2010;38(22):e199. doi: 10.1093/nar/gkq862 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Price AL, Jones NC, Pevzner PA. De novo identification of repeat families in large genomes. Bioinformatics. 2005; 21, 351–358. doi: 10.1093/bioinformatics/bti1018 [DOI] [PubMed] [Google Scholar]
  • 25.Edgar RC, Myers EW. PILER: identification and classification of genomic repeats. Bioinformatics. 2005;21 Suppl 1:i152–i158 doi: 10.1093/bioinformatics/bti1003 [DOI] [PubMed] [Google Scholar]
  • 26.Wicker T, Sabot F, Hua-Van A, et al. A unified classification system for eukaryotic transposable elements. Nat Rev Genet. 2007;8(12):973–982. doi: 10.1038/nrg2165 [DOI] [PubMed] [Google Scholar]
  • 27.Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, Walichiewicz J. Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 2005;110(1–4):462–467. doi: 10.1159/000084979 [DOI] [PubMed] [Google Scholar]
  • 28.Tarailo-Graovac M, Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinformatics. 2009;Chapter 4. doi: 10.1002/0471250953.bi0410s25 [DOI] [PubMed] [Google Scholar]
  • 29.Stanke M, Waack S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003; 19, 215–225. doi: 10.1093/bioinformatics/btg1080 [DOI] [PubMed] [Google Scholar]
  • 30.Blanco E, Parra G, Guigó R. Using geneid to identify genes. Curr Protoc Bioinformatics. 2007;Chapter 4. doi: 10.1002/0471250953.bi0403s18 [DOI] [PubMed] [Google Scholar]
  • 31.Majoros WH, Pertea M, Salzberg SL. TigrScan and GlimmerHMM: two open source ab initio eukaryotic gene-finders. Bioinformatics. 2004;20(16):2878–2879. doi: 10.1093/bioinformatics/bth315 [DOI] [PubMed] [Google Scholar]
  • 32.Korf I. Gene finding in novel genomes. BMC Bioinformatics. 2004;5:59. doi: 10.1186/1471-2105-5-59 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Keilwagen J, Hartung F, Grau J. GeMoMa: Homology-Based Gene Prediction Utilizing Intron Position Conservation and RNA-seq Data. Methods Mol Biol. 2019;1962:161–177 doi: 10.1007/978-1-4939-9173-0_9 [DOI] [PubMed] [Google Scholar]
  • 34.Haas BJ, Salzberg SL, Zhu W, et al. Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments. Genome Biol. 2008;9(1):R7. doi: 10.1186/gb-2008-9-1-r7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Griffiths-Jones S, Moxon S, Marshall M, Khanna A, Eddy SR, Bateman A. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 2005;33(Database issue):D121–D124. doi: 10.1093/nar/gki081 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. Journal of molecular biology. 1990, 215:403–410. doi: 10.1016/S0022-2836(05)80360-2 [DOI] [PubMed] [Google Scholar]
  • 37.Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25(5):955–964. doi: 10.1093/nar/25.5.955 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Kent WJ. BLAT—the BLAST-like alignment tool. Genome research 2002, 12:656–664. doi: 10.1101/gr.229202 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Birney E, Clamp M, Durbin R. GeneWise and genomewise. Genome research. 2004; 14:988–995. doi: 10.1101/gr.1865504 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Finn RD, Bateman A, Clements J, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42(Database issue):D222–D230. doi: 10.1093/nar/gkt1223 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Tatusov RL, Galperin MY, Natale DA, Koonin EV. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 2000;28(1):33–36. doi: 10.1093/nar/28.1.33 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004;32(Database issue):D277–D280. doi: 10.1093/nar/gkh063 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Ashburner M, Ball C A, Blake J A. Gene Ontology: tool for the unification of biology. Nature genetics. 2000; 25, 25–29. doi: 10.1038/75556 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42(Database issue):D490–D495. doi: 10.1093/nar/gkt1178 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Saier MH Jr, Tran CV, Barabote RD. TCDB: The Transporter Classification Database for membrane transport protein analyses and information. Nucleic Acids Res. 2006;34(Database issue):D181–D186. doi: 10.1093/nar/gkj001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Winnenburg R, Baldwin TK, Urban M, Rawlings C, Köhler J, Hammond-Kosack KE. PHI-base: a new database for pathogen host interactions. Nucleic Acids Res. 2006;34(Database issue):D459–D464. doi: 10.1093/nar/gkj047 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Mohanta TK, Bae H. The diversity of fungal genome. Biol Proced Online. 2015;17:8. doi: 10.1186/s12575-015-0020-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Perlin MH, Andrews J, Toh SS. Essential letters in the fungal alphabet: ABC and MFS transporters and their roles in survival and pathogenicity. Adv Genet. 2014;85:201–253. doi: 10.1016/B978-0-12-800271-1.00004-4 [DOI] [PubMed] [Google Scholar]
  • 49.Choquer M, Lee MH, Bau HJ, Chung KR. Deletion of a MFS transporter-like gene in Cercospora nicotianae reduces cercosporin toxin accumulation and fungal virulence. FEBS Lett. 2007;581(3):489–494. doi: 10.1016/j.febslet.2007.01.011 [DOI] [PubMed] [Google Scholar]
  • 50.Guengerich FP. Cytochrome P450 research and The Journal of Biological Chemistry. J Biol Chem. 2019;294(5):1671–1680. doi: 10.1074/jbc.TM118.004144 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Yan X, Ma WB, Li Y, et al. A sterol 14α-demethylase is required for conidiation, virulence and for mediating sensitivity to sterol demethylation inhibitors by the rice blast fungus Magnaporthe oryzae. Fungal Genet Biol. 2011;48(2):144–153. doi: 10.1016/j.fgb.2010.09.005 [DOI] [PubMed] [Google Scholar]
  • 52.Pedrini N, Ortiz-Urquiza A, Huarte-Bonnet C, Zhang S, Keyhani NO. Targeting of insect epicuticular lipids by the entomopathogenic fungus Beauveria bassiana: hydrocarbon oxidation within the context of a host-pathogen interaction. Front Microbiol. 2013;4:24. doi: 10.3389/fmicb.2013.00024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Qiu D, Mao J, Yang X, Zeng H. Expression of an elicitor-encoding gene from Magnaporthe grisea enhances resistance against blast disease in transgenic rice. Plant Cell Rep. 2009;28(6):925–933. doi: 10.1007/s00299-009-0698-y [DOI] [PubMed] [Google Scholar]
  • 54.Rose JK, Ham KS, Darvill AG, Albersheim P. Molecular cloning and characterization of glucanase inhibitor proteins: coevolution of a counterdefense mechanism by plant pathogens. Plant Cell. 2002;14(6):1329–1345. doi: 10.1105/tpc.002253 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.van den Brink J, de Vries RP. Fungal enzyme sets for plant polysaccharide degradation. Appl Microbiol Biotechnol. 2011;91(6):1477–1492. doi: 10.1007/s00253-011-3473-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Jeffress S, Arun-Chinnappa K, Stodart B, Vaghefi N, Tan YP, Ash G. Genome mining of the citrus pathogen Elsinoë fawcettii; prediction and prioritisation of candidate effectors, cell wall degrading enzymes and secondary metabolite gene clusters. PLoS One. 2020;15(5):e0227396. doi: 10.1371/journal.pone.0227396 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Takaoka S, Kurata M, Harimoto Y, et al. Complex regulation of secondary metabolism controlling pathogenicity in the phytopathogenic fungus Alternaria alternata. New Phytol. 2014;202(4):1297–1309. doi: 10.1111/nph.12754 [DOI] [PubMed] [Google Scholar]
  • 58.López-Berges MS, Rispail N, Prados-Rosales RC, Di Pietro A. A nitrogen response pathway regulates virulence functions in Fusarium oxysporum via the protein kinase TOR and the bZIP protein MeaB. Plant Cell. 2010, 22(7):2459–75. doi: 10.1105/tpc.110.075937 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. GO, KOG and KEGG annotation of E. arachidis.

(TIF)

S2 Fig. Collinear analysis and evolutionary analysis of E. arachidis.

(A) A phylogenetic tree constructed the evolutionary relationships of E. arachidis and other fungi. (B) Collinear analysis.

(TIF)

S3 Fig. Gene clusters in E. arachidis.

(TIF)

S4 Fig. PKS, NRPS and NRPS-PKS hybrid in different genome.

(TIF)

S1 Table. Repetitive sequence in E. arachidis.

(DOC)

S2 Table. ABC transporter and major facilitator superfamily in E. arachidis.

(XLSX)

S3 Table. Cytochrome P450 in E. arachidis.

(XLSX)

S4 Table. The loss of pathogenicity and reduced virulence genes in E. arachidis.

(DOCX)

S5 Table. Increased virulence genes in E. arachidis.

(DOCX)

S6 Table. CAZyme_family in E. arachidis.

(XLSX)

S7 Table. CAZymes in E. arachidis and compared genome.

(DOCX)

S8 Table. The information of the different PKSs.

(DOC)

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.


Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES