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Comparative genomics of plant pathogenic Diaporthe species and transcriptomics of Diaporthe caulivora during host infection reveal insights into pathogenic strategies of the genus

BMC Genomics volume 23, Article number: 175 (2022) Cite this article

Abstract

Background

Diaporthe caulivora is a fungal pathogen causing stem canker in soybean worldwide. The generation of genomic and transcriptomic information of this ascomycete, together with a comparative genomic approach with other pathogens of this genus, will contribute to get insights into the molecular basis of pathogenicity strategies used by D. caulivora and other Diaporthe species.

Results

In the present work, the nuclear genome of D. caulivora isolate (D57) was resolved, and a comprehensive annotation based on gene expression and genomic analysis is provided. Diaporthe caulivora D57 has an estimated size of 57,86 Mb and contains 18,385 predicted protein-coding genes, from which 1501 encode predicted secreted proteins. A large array of D. caulivora genes encoding secreted pathogenicity-related proteins was identified, including carbohydrate-active enzymes (CAZymes), necrosis-inducing proteins, oxidoreductases, proteases and effector candidates. Comparative genomics with other plant pathogenic Diaporthe species revealed a core secretome present in all Diaporthe species as well as Diaporthe-specific and D. caulivora-specific secreted proteins. Transcriptional profiling during early soybean infection stages showed differential expression of 2659 D. caulivora genes. Expression patterns of upregulated genes and gene ontology enrichment analysis revealed that host infection strategies depends on plant cell wall degradation and modification, detoxification of compounds, transporter activities and toxin production. Increased expression of effectors candidates suggests that D. caulivora pathogenicity also rely on plant defense evasion. A high proportion of the upregulated genes correspond to the core secretome and are represented in the pathogen-host interaction (PHI) database, which is consistent with their potential roles in pathogenic strategies of the genus Diaporthe.

Conclusions

Our findings give novel and relevant insights into the molecular traits involved in pathogenicity of D. caulivora towards soybean plants. Some of these traits are in common with other Diaporthe pathogens with different host specificity, while others are species-specific. Our analyses also highlight the importance to have a deeper understanding of pathogenicity functions among Diaporthe pathogens and their interference with plant defense activation.

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Background

Species of the genus Diaporthe and their asexual morph Phomopsis are endophytes, pathogenic, and saprophytic on a wide range of hosts worldwide [1,2,3]. Pathogenic Diaporthe species cause diseases on a wide range of plant hosts, including forest trees [4], citrus [5], pepper [6], sunflower [7], and soybean [8], among others. In soybean, Diaporthe caulivora (syn. Diaporthe phaseolorum var. caulivora) and Diaporthe aspalathi (syn. Diaporthe phaseolorum var. meridionalis) are the causal agents of soybean stem canker (SSC) [8,9,10,11]. Diaporthe longicolla also causes soybean stem canker, and it is usually found in association with D. caulivora and D. aspalathi [12, 13]. SSC causes important losses in soybean growing regions around the world [14,15,16,17,18], and disease symptoms are associated mainly to necrosis of the stem, with discoloration and leaf wilting [10, 13]. Currently, SSC management is performed by agronomic practices (crop rotation, avoidance of crop residue), fungicide applications, and the use of resistant cultivars [12, 19]. Host-plant resistance is currently the most effective strategy and therefore the identification of sources of resistance in soybean germplasms and breeding lines is of great importance. At present, five major genes that confer resistance to SSC caused by D. aspalathi and one for D. caulivora have been identified [10, 20, 21]. However, no commercial varieties with resistance to D. caulivora are available. Since D. caulivora is one of the principal causal agents of SSC in soybean producing countries [13, 19, 22], more information is needed to understand the interaction of this pathogen with the host plant in order to develop breeding strategies to control the disease.

Genomic information has allowed the identification of virulence factors and effector proteins in different pathogenic fungal species [23, 24], including plant cell wall degrading enzymes (PCWDEs) and enzymes involved in toxin production that are important for host colonization [25,26,27]. At present, 14 nuclear genome sequences of different Diaporthe species are available at NCBI [6, 26, 28,29,30,31,32,33,34,35,36,37]. However, genomic resources of the important pathogen D. caulivora are not available and transcriptomic profiling of Diaporthe species during host infection has not been performed. Genomic and transcriptomic approaches will give insights into the molecular basis of the pathogenicity strategies used by different Diaporthe species. In the present work, we report for the first time the genome sequence of D. caulivora and performed a comparative analysis with five available pathogenic Diaporthe genomes from different hosts [6, 13, 35,36,37,38,39,40,41]. Moreover, we present RNAseq data of D. caulivora during host plant infection. Our findings reveal the presence of pathogenicity factors that are shared between all Diaporthe species and D. caulivora-specific virulence components involved in host colonization and plant defense evasion.

Results and discussion

Diaporthe caulivora de novo genome sequencing, assembly and comparison with available Diaporthe genomes

The nuclear genome assembly of D. caulivora was resolved taking the advantages of PacBio sequences reads using FlyE v2.7. The assembly consisted in 10 contigs with a total length of 57,864,239 bp and a coverage of 270X (Table 1). The polishing step was performed using Minimap2 v2.18 by mapping the Pacbio raw reads back to the genome assembly [42], with a mapping rate of 97.8%. Genome assembly and the annotation was defined with high precision and completeness, determined by BUSCO analysis; 98% completeness in fungi_odb10.2019-11-20 database. We further looked for nuclear genomes of other Diaporthe species available at NCBI and selected five genomes, D. capsici, D. citri, D. destruens, D. longicolla and D. phragmitis, according to the quality of the assembly based on the total genome size, N50, number of contigs and number of N’s (Additional file 1, Table 1). Although D. longicolla has a lower quality assembly, it was included since this species has high occurrence in SCC lesions [13]. D. capsici, D. citri, D. destruens and D. phragmitis are pathogens with different host range (Additional file 2). Phylogenetic analysis with the internal transcribed spacer (ITS) and translation elongation factor 1-alpha gene (TEF1α) sequences of the six Diaporthe species, ex-type strains as well as other pathogenic Diaporthe species, confirmed the identity of the isolates used (Additional file 3). The genome assembly sizes of D. capsici, D. destruens and D. phragmitis ranged from 56,1 Mb to 58,3 Mb [6, 36, 37], and were comparable in size with D. caulivora genome (57,86 Mb), while D. longicolla and D. citri were ~ 7 Mb larger than the other species analyzed [26, 35] (Table 1).

Table 1 General features of Diaporthe genomes
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Syntenic blocks analysis provides insights into genomic evolution between related species and defines regions of chromosomes between genomes that share a common order of homologous genes derived from a common ancestor [43]. A whole genome nucleotide alignment using MUMmer [44], implemented in SyMap V 5.0.6, allowed the identification of large regions in syntenic blocks between D. caulivora and the other five Diaporthe species with an average of 96% (Additional file 4). This analysis showed that D. caulivora genome has a range of 82 to 92 syntenic blocks with a coverage of 95% of the total length of the genomes respect to D. capsici, D. citri, D. destruens and D. phragmitis genomes. It is worth noting that D. caulivora contig 10 has a complete synteny with contig 19 of D. capsici. The other contigs of D. caulivora were highly conserved with a few rearrangements compared with the four Diaporthe species mentioned above. The high syntenic similarity among these five Diaporthe species indicates their close genetic relationship, evidenced by a strong overall conservation of genomic organization. The D. longicolla assembly was the only one obtained only by Illumina technology and showed the lowest values of synteny, which is consistent with the degree of fragmentation of the assembly.

Gene prediction, functional annotation and orthologs

The annotation of the D. caulivora genome assembly was performed using a Fungap pipeline with combined abinitio strategies, homology-based searches and RNA-seq data, resulting in the annotation of 18,385 protein-coding regions. The functional annotation of predicted protein-coding genes was completed with the Blast2GO (B2Go) program [45]. The predicted genes were compared by BlastP against NCBI nr database (TaxId: Fungi) and classified by InterProScan v.5.19., Pfam protein families, InterPro domains, Gene Ontology Classification (GO), and metabolic pathways were recovered from proteins identified by BLAST using the B2Go annotation system (Additional file 5). Functional description could be assigned to 16,068 coding genes (87.4%). The remaining 2317 genes were searched at NCBI and 1787 of them did not show any hit and were considered as putative D. caulivora-specific genes validated by transcriptomic data (Additional file 5). Additional genome sequencing of more Diaporthe species will aid to confirm if these genes are only present in D. caulivora and not in other genomes of the genus. In order to make comparisons at gene levels with the five genomics assemblages of the other Diaporthe species included in this work, we performed gene prediction of the Diaporthe genomes with the Augustus web server (http://bioinf.uni-greifswald.de/webaugustus/), using D. caulivora protein-coding gene models as a training set. The total predicted coding genes were 15,675 genes models for D. capsici, 15,921 for D. citri, 13,754 for D. destruens, 16,606 for D. longicolla, and 12,393 for D. phragmitis (Table 1).

OrthoFinder analysis combined with an all-versus-all protein BLAST strategy was used to cluster protein orthologous groups and infer a phylogeny tree with the six Diaporthe species and Fusarium graminearum as outgroup (Fig. 1). The results show that an overall 94% (86–97%) of genes were assigned to orthologous groups shared by the six Diaporthe species. Phylogenetic analysis showed that the six Diaporthe species were divided into two main clusters (Fig. 1A). Diaporthe caulivora is phylogenetically distant from D. capsici, D. citri and D. phragmitis, which can be explained by the lower number of orthologous genes (87%) shared with these three species. Diaporthe capsici, D. citri and D. phragmitis are phylogenetically closely related, largely due to sharing nearly 98.,4% of the orthologous genes, which is in agreement with what has been shown by Gai et al. [35]. The second cluster included D. destruens and D. longicolla. Although D. caulivora and D. longicolla are soybean pathogens and they could be expected to be closely related, D. longicolla shares a similar number of genes assigned to orthologous groups with the other Diaporthe species regardless of the host specificity (Fig. 1B). Taking into account the quality of the analyzed assemblages, it is possible that specific genes of each species have not been fully recovered, unlike the D. caulivora genome presented in this work. Besides identifying the orthologous genes, we analyzed the average nucleotide identity (ANIm) of the species, which measure the nucleotide-genomic similarity between two genomes. The ANIm values between the different Diaporthe species varied from 78.6 to 96.4% and similar clusters as those obtained by orthologous analyses were obtained, except for D. caulivora which grouped together with D. longicolla and D. destruens (Fig. 1C).

Fig. 1
figure 1

Orthologs and phylogenomic relationships of D. caulivora and other pathogenic Diaporthe species. A The species tree based on the orthologous groups was inferred by STAG and rooted by STRIDE. Fusarium graminearum was used as outgroup. B Heatmap showing the number of genes of each fungal species included in the orthogroups obtained by OrthoFinder. The scale represents the number of genes in orthogroups. C Heatmap of identity percentage amongs Diaporthe species based on the average nucleotide identity (ANI) analisys performed using PYANI v0.3. 0-alpha with MUMer to align the input sequences (ANIm). Scale in (A) represents divergence and substitutions per site

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Diaporthe caulivora-specific and shared Diaporthe virulence components

Fungal secreted proteins mediate communication with the environment including host plants, and have important function in pathogenesis [46]. To define the set of proteins considered within the secretome of the six Diaporthe species analyzed in our work, we used SignalP v5.0, WoLF PSORT for Fungi and Phobius. Diaporthe caulivora has a total of 1501 genes encoding predicted secreted proteins. The predicted secretome of D. capsici, D. longicolla and D. phragmitis was similar in number (ranging from 1588 to 1535 proteins), while D. citri and D. destruens have a smaller secretome, represented by 1383 and 1298 proteins, respectively (Fig. 2A; Additional file 6). Diaporthe caulivora shared between 1007 and 1208 secreted proteins with the other five Diaporthe species (Fig. 2B).

Fig. 2
figure 2

Number of predicted secreted proteins and effector candidates in D. caulivora and five other Diaporthe species. A Distribution of effectors in the different secretomes. Number of secreted proteins (B) and effector candidates (C) shared between D. caulivora and other Diaporthe species

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In total 439 secreted proteins were in common between all Diaporthe species and were defined as the core secretome (Fig. 3A; Additional file 6). This core secretome includes virulence components related to plant cell wall degradation and modification, such as pectin and pectate lyases, glycoside hydrolases, carbohydrate esterases, endoglucanases and exoglucanases, a xylanase, as well as proteases, peptidases, lipases, peroxidases, among others. Combining the results of the six secretomes with a NCBI search that include recently sequenced Diaporthe and other fungal species, we identified 1375 D. caulivora predicted proteins that have conserved homologs in other Diaporthe and fungal pathogens (Fig. 3B, Additional file 7). The rest comprised 53 conserved proteins among D. caulivora and other fungal species, 46 Diaporthe-specific proteins and 27 putative D. caulivora-specific proteins that exhibited no hit with any other organism. Interestingly, five of the Diaporthe-specific proteins belong to the core secretome and represent relevant secreted proteins in the Diaporthe genus. Diaporthe-specific and D. caulivora-specific secreted proteins were all uncharacterized proteins whose roles in pathogenesis needs further investigation.

Fig. 3
figure 3

Shared and D. cualivora-specific secreted proteins and candidate effectors. A Venn diagram showing the overlap the core secretome, D. caulivora-specific secreted proteins, D. caulivora secreted CAZymes and PHI-base. B Conservation patterns of secreted proteins and effector candidates from D. caulivora indicating the number of conserved homologs in other Diaporthe species and fungi

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We looked into more detail to CAZYmes present in the D. caulivora secretome, and identified a total of 460 genes encoding CAZymes (Fig. 4; Additional file 8). The high proportion of CAZymes in the D. caulivora secretome (30.6%), is consistent with previous reports in other plant pathogens and Diaporthe species and emphasizes the importance to degrade efficiently polysaccharide materials present in the plant cell walls to facilitate infection and/or gain nutrition [26, 29, 47]. The other Diaporthe species have a similarly proportion of their secretome represented by CAZymes; 488 in D. capsici (30.7%), 425 in D. citri (30.7%), 424 in D. destruens (32.7%), 482 in D. longicolla (31.4%), and 485 in D. phragmitis (31.5%). The diversity of sub-categories of CAZymes, including Glycoside Hydrolase (GHs), Glycosyltransferase (GTs), Polysaccharide Lyase (PLs), Carbohydrate Esterase (CEs), were similar among the six Diaporthe species (Fig. 4A). The number of shared secreted CAZymes between D. caulivora and the other Diaporthe species varied between 331 and 382 (Fig. 4B), and 156 CAZymes were present in all species (Additional file 8). These findings suggest that secreted CAZymes play similar roles among Diaporthe species in terms of carbon utilization capabilities.

Fig. 4
figure 4

Comparative distribution of secreted CAZymes in different Diaporthe species. A Distribution according to classes of CAZyme module. PL, polysaccharide lyases; GT, glycosyl transferases; GH, glycoside hydrolases; CE, carbohydrate esterases; CBM, carbohydrate-binding module and AA, auxiliary activity. B Number of CAZymes shared between D. caulivora and other Diaporthe species

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We further search in the Pathogen–Host Interaction (PHI)-base (Fig. 3A, Additional file 9), which catalogs experimentally verified pathogenicity, virulence and effector genes from different plant pathogens [48]. In total, 556 secreted proteins of D. caulivora (37%), were identified in the PHI-base and among them, 287 were related to reduced virulence or loss of pathogenicity mutant phenotypes, 18 to increased virulence and 35 to effectors (Additional file 9). From them, 191 belongs to the core secretome and half of them correspond to CAZymes reinforcing their important role in Diaporthe pathogenicity (Fig. 3A). Interestingly, several D. caulivora secreted proteins that are shared with other pathogenic fungi but were absent in available genomes of Diaporthe species include CAZymes, a kievitone hydratase involved in phytoalexin detoxification [49], several FAD-binding domain-containing proteins, a putative versicolorin B synthase involved in mycotoxin production [50], among others (Additional file 7). Five of these D. caulivora secreted proteins, represent homologs of fungal genes that were assigned as effectors or result in reduced virulence in knockout or mutant experiments, including two LysM domain-containing proteins, an endo-beta-1,6-glucanase, a pectin lyase-like protein, and a minor extracellular protease vpr (Additional file 9). Moreover, several genes encoding important virulence proteins in other fungi, such as necrosis- and ethylene-inducing proteins (NEP), transporters, oxidoreductases, proteases, hypersensitive response-inducing proteins, CFEM domain-containing protein were also present in the secretomes of several of the analyzed Diaporthe species, and most of them have PHI-base accessions hits.

Effectors are proteins or small molecules secreted by pathogens, which manipulate host cells, facilitating infection and interfering with host immunity [23, 51]. We identified 133 secreted D. caulivora effector candidates (Fig. 2A), including pectate and other polysaccharide lyases, glycoside hydrolases, a pathogenesis-related protein, a hypersensitive-inducing protein, peptidases, carbohydrate esterase and several hypothetical proteins (Additional file 10). The number of predicted effectors in the other five Diaporthe species ranged from 85 to 123 (Fig. 2A; Additional file 10). Diaporthe caulivora shared between 63 to 98 effectors candidates with the other five Diaporthe species (Fig. 2C). Nine effector candidates were considered core effectors since they were present in all Diaporthe species, including four CAZymes (pectate lyase, polysaccharide lyase, 1,4-beta-D-glucan cellobiohydrolase and xylanase), a protein CAP22 and four hypothetical proteins. Most of the D. caulivora effector candidates were also found in other Diaporthe species and other fungi, 11 were Diaporthe-specific and four were D. caulivora-specific (Fig. 3B, Additional file 7). All Diaporthe-specific and D. caulivora-specific effector candidates are hypothetical proteins, which lack a conserved domain. Moreover, of the total secreted D. caulivora effector candidates, only 17 were identified in the PHI-base; 8 were related to reduced virulence mutant phenotypes and four were identified as effectors (Additional file 9). These virulence factors include several CAZymes, comprising four CAZymes of the core effectors, and a sterigmatocystin biosynthesis peroxidase involved in toxin production [52]. Taken together, our results revealed that the genome of D. caulivora has a large array of pathogenicity-related genes, most of which are in common with other Diaporthe and fungal pathogens, while others are Diaporthe-specific or D. caulivora-specific. Further studies are needed to reveal the function of these genus- and species-specific effector candidates.

Diaporthe caulivora genes encoding virulence factors and effector candidates are induced during soybean infection

In order to identify Diaporthe genes involved in pathogenicity, we performed transcriptional profiling of two early stages of D. caulivora infection of soybean plants (8 and 48 hpi) and included D. caulivora mycelium grown on PDA medium (Additional file 11). A total of 69,940,418 reads mapped to the D. caulivora genome and were considered for further analyses. Biological variability within replicates was analyzed by principal component analysis (PCA). As shown in Fig. 5A, the first principal component (PC1) accounted for 79.1% of the total variation and separates the two time points (8 and 48 hpi), and the control D. caulivora samples. In total, 306 D. caulivora genes were differentially expressed in plant tissues (48 vs 8 hpi), 295 genes were upregulated and 11 downregulated (Fig. 5B; Additional file 12). In order to obtain more information on the infection process, we also compared differential expression between samples at 8 hpi and 48 hpi with mycelium samples grown on PDA. We identified 2635 additional D. caulivora differentially expressed genes (DEGs); 77 and 593 were upregulated, and 595 and 1561 were downregulated at 8 and 48 hpi, respectively (Fig. 5B-C; Additional file 12). We further focused on the total upregulated DEGs of the three comparisons (806 genes), since they could encode pathogenicity-related proteins involved in D. caulivora infection strategies. The functions of these DEGs were significantly enriched in several Molecular Function enriched GO terms, including oxidoreductase activity, hydrolase activity, lyase activity, ion binding and transporter activity (Fig. 6A; Additional file 12).

Fig. 5
figure 5

Differentially expressed D. caulivora genes during soybean infection. A Principal component analysis (PCA) of the transcriptomic data from RNA-seq. Colored dots denote each biological replicate. B Number of differentially expressed genes (DEGs). C Venn diagrams from D. caulivora comparisons at 8 hpi vs control, 48 hpi vs control and 48 vs. 8 hpi. Log2 FC ≥ 2.0 or ≤ − 2.0 and false discovery rate (FDR) ≤ 0.05 were considered for DEGs identification. In Venn diagrams, the overlap of expressed fungal genes can be observed

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Fig. 6
figure 6

A Enriched Gene Ontology (GO) terms distribution for molecular function (MF) of upregulated differentially expressed genes (DEGs) in D. caulivora during soybean infection. B Venn diagram showing the overlap of upregulated DEGs and different gene categories relevant to D. caulivora pathogenicity

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We identified a high proportion of DEGs showing homology to genes previously reported to be involved in fungal infection processes. During D. caulivora infection, genes encoding CAZymes, proteins involved in detoxification and transport of toxic compounds, proteases and effectors were upregulated (Additional file 12). A search against the PHI-base, predicted 119 upregulated D. caulivora genes that may be involved in pathogenicity (Fig. 6B, Additional file 13). Among them, 46 were related to reduced virulence or loss of pathogenicity mutant phenotypes and 15 to effectors, including CAZymes such as lectins, xyloglucan-specific endo-beta-1,4-glucanase, pectin and pectate lyases, as well as proteases, peptidases, among others (Additional file 13). Of the total upregulated DEGs, 246 encode proteins that were present in our predicted secretome (Fig. 6B). Interestingly, 106 (43%) of these upregulated secreted proteins belong to the core secretome, indicating their contribution to Diaporthe pathogenesis (Additional file 13). Moreover, of the 104 upregulated D. caulivora genes encoding secreted CAZymes (Fig. 6B), 47 were present in the core secretome (Additional file 12), including polygalacturonases, endoglucanases, exoglucanases, pectate lyases, pectin lyases, glycoside hydrolases, xyloglucan-specific endo-beta-1,4-glucanase, mannan endo-1,4-beta-mannosidase, rhamnogalacturonan acetylesterases, among others. The number of upregulated DEGs and expression levels of CAZymes-encoding genes, including PCWDEs, increased at 48 hpi compared to 8 hpi (Additional file 12), highlighting the important role played by these enzymes in soybean tissue breakdown and host penetration. In addition, seven genes encoding secreted proteins present only in D. caulivora and not in other Diaporthe species were upregulated, including a kievitone hydratase and six hypothetical proteins, three of which are D. caulivora-specific with no hit in public databases. Interestingly, kievitone hydratases are involved in detoxification processes of the phenylpropanoid kievitone, which play an important role in legumes defense against pathogen attack [53]. Of the total number of D. caulivora effectors, 23.3% (31 genes) were upregulated, including those encoding several CAZymes, a cell wall glycoprotein, several small secreted proteins, and hypothetical proteins (Additional files 12 and 13). Interestingly, five of the nine core effectors were upregulated during infection, including a xylanase, a polysaccharide lyase, CAP22, a putative 1,4-beta-D-glucan cellobiohydrolase and a hypothetical protein, indicating that they represent common effector candidates in different Diaporthe plant pathogens. Most of these common genes encode proteins involved in cell wall degradation and modification, and CAP22 is expressed in other pathogenic fungi in infection structures such as appresoria [54]. Moreover, one Diaporthe-specific (gene_10233.t1) and one D. caulivora-specific (gene_06736.t1) effector candidates were upregulated during infection.

Further inspection in transcriptomic data revealed possible strategies used by D. caulivora, and probably the other Diaporthe species, leading to host infection and tissues colonization. In addition to CAZymes-encoding genes, other upregulated genes belonging to the core secretome include proteins involved in pathogenesis such as peroxidases, SnodProt1, necrosis- and ethylene-inducing peptide 1 (Nep1)-like protein, saponin hydrolase, pathogenesis related protein, CAP22, peptidases, small secreted proteins and lipases. Upon entry into the host, fungal pathogens must resist toxic compounds produced by the plant [55]. Upregulation of a high number of genes encoding proteins involved in oxidoreductase processes during D. caulivora infection, like dehydrogenases, oxidases and reductases, is consistent with their important role played during fungal colonization and maintenance of redox status in both organisms [56] (Additional files 12 and 13). Several upregulated D. caulivora peroxidase-encoding genes belonging to the core secretome showed homology with genes included in the PHI-base. Like in other plant-pathogen interactions, D. caulivora and other Diaporthe peroxidases could be involved in lignin breakdown and detoxification of ROS produced by the host. In addition, expression of aldehyde dehydrogenases-encoding genes increased upon D. caulivora infection, which could be involved in pathogenicity through scavenging reactive aldehydes, fatty acid radicals, and other alcohol derivatives, as occur in other plant fungal pathogens [57] (Additional file 13). Moreover, 24 cytochrome P450 encoding genes were upregulated during D. caulivora infection, several of which exhibited functionally characterized homologs in PHI-base (Additional file 13). Since some cytochrome P450 are capable of detoxifying phytoalexins [58], and P450 monooxygenases are involved in the synthesis of some fungal toxins [59], they probably are also needed for D. caulivora pathogenesis. Interestingly, P450 families expanded significantly in several plant pathogens, including D. ampelina indicating their important roles during fungal pathogenesis [30]. Enzymes with oxidative-reductive properties also play relevant functions in degrading antimicrobial compounds such as phytoalexins and polyamines of plant origin [60]. Consistently, D. caulivora genes encoding enzymes involved in detoxification of plant defense molecules show high expression levels during early infection stages. Among them, we found several genes encoding putative pisatin demethylases, most of which were related to reduced virulence phenotypes in other pathosystems according to PHI-base (Additional file 13). In Fusarium oxysporum f. sp. pisi, a pisatin demethylase is responsible for detoxifying the pea phytoalexin pisatin [61]. Pisatin demethylases could encode glyceollin demethylases and detoxify soybean phytoalexin glyceollin [59]. Furthermore, two genes encoding dienelactone hydrolase (gene_04049.t1, gene_11950), involved in the β-ketoadipate pathway [62], were highly expressed, suggesting that they could be involved in detoxification mechanisms of plant defensive aromatic compounds. Interestingly, a saponin hydrolase present in the core secretome was upregulated during D. caulivora infection. These enzymes hydrolyze plant saponins that have antifungal activity and serve as potential chemical barriers against pathogens [63]. Thus, saponin hydrolases could be part of Diaporthe strategies to achieve successful host infection. On the other hand, a kievitone hydratase encoding gene (gene_03751.t1), which was only present in D. caulivora and other fungi such as Fusarium oxysporum and Colletotrichum fructicola, and not in other Diaporthe species, was upregulated during D. caulivora colonization. Kievitone hydratase is an important virulence factor in Fusarium solani that catalyzes the conversion of bean kievitone to a less toxic metabolite [49]. Furthermore, a gene encoding the phytotoxin SnodProt1, present in the core secretome, displays increased expression throughout the D. caulivora infection process. SnodProt1 proteins are involved in plant tissue colonization by several pathogenic fungi and produces ROS and necrosis of the plant tissues [64]. We have observed that D. caulivora infection induces ROS production in soybean stems (Mena, unpublished observations), and D. aspalathi elicitors activate the formation of nitric oxide (NO) in plant tissues that triggers the biosynthesis of antimicrobial flavonoids in soybean [65]. These findings, suggest that ROS and NO production play a role in plant-Diaporthe interactions.

Fungal toxins and necrosis inducing factors are produced to kill plant cells enabling nutrient uptake and mycelium growth. During D. caulivora infection, genes encoding secreted NEP (gene_04209.t1, gene_05426.t1 and gene_11576.t1) and a hypersensitive response-inducing protein (gene_18357.t1) were upregulated. Two of these NEP proteins belong to the core secretome, indicating their contribution to Diaporthe pathogenesis. Expression levels of genes involved in mycotoxin biosynthesis (gene_13234.t1, gene_10937.t1, gene_00256.t1, gene_05355.t1 and gene_15459.t1), and 11 genes encoding polyketide synthases (PKS) involved in toxin production in other fungi, increased during soybean infection (Additional file 12). Upregulation of this high number of PKS-encoding genes underline their involvement in D. caulivora pathogenesis, which is consistent with PHI-base hits with reduced virulence mutant phenotypes for PKS (gene_13009.t1, gene_01287.t1, gene_16446.t1) (Additional file 13), and previously reported reduced virulence of D. helianthi PKS1 mutant [27]. During sunflower infection, D. helianthi produces the polyketidic phytotoxin phomozin and related toxins, and purified phomozin causes disease symptoms [27]. Interestingly, the possible involvement of a phytotoxin produced by D. caulivora during SCC development has been previously reported [66].

Enzymes such as subtilases and alkaline proteinases have been shown to degrade plant defense proteins [67]. Increased expression of genes encoding three subtilisin-like proteinases (gene_11104.t1, gene_05325, gene_14820), an aspartic proteinase (gene_08152), and several other proteases in D. caulivora-infected tissues suggests their possible involvement in degradation of plant defense proteins. Consistently, disruption of genes with significant homology to gene_11104.t1 and gene_08152.t1, as well as other genes encoding proteases (gene_15501.t1, gene_15174.t1 and gene_06260.t1) showed reduced virulence in other plant pathogens (Additional file 13). The role of proteases and peptidases in Diaporthe pathogenesis is further supported by their presence in the core secretome.

Other upregulated genes encoding virulence factors include several transporters such as major facilitator superfamily (MSF) and ABC transporters, that in plant pathogenic fungi are responsible not only for export of compounds involved in pathogenesis, but also for excretion of plant-derived antimicrobial compounds [68]. In total, 6 MSF and four ABC transporters were upregulated during D. caulivora infection and showed significant homology to other transporters involved in virulence mechanisms in other plant pathogens (Additional files 12 and 13).

Taken together, our results suggest that early infection processes of soybean stems by D. caulivora depends on plant cell wall degradation and modification, detoxification of toxic compounds, transporter activities and toxin production. In addition, increased expression of genes encoding putative effector proteins during host colonization, some of which are species-specific, indicate that D. caulivora infection strategy also relies on plant defense evasion. Interestingly, several of these upregulated genes encoded proteins are part of the core secretome and represent common virulence components among Diaporthe species.

Conclusions

Our findings give novel and relevant insights into the molecular players involved in pathogenicity of D. caulivora towards soybean plants, several of which are in common with other Diaporthe pathogens with different host specificity, while others are species-specific. Future studies towards understanding the role of effector candidates during the infection process of important pathogens among Diaporthe genus could reveal novel effector functions and plant targets that underpin Diaporthe pathogenic lifestyle. Our findings improve our understanding of D. caulivora pathogenicity mechanism involved in SCC development and allow genomic and transcriptomic comparisons among the most important pathogens belonging to the Diaporthe genus. Finally, the knowledge generated in this study provides a foundation for developing effective disease management strategies for SSC.

Methods

All methods were performed in accordance with the relevant guidelines and regulations.

Diaporthe caulivora inoculation

The Diaporthe virulent strain D57 was previously isolated from a stem canker lesion of a soybean plant grown in Uruguay and confirmed as D. caulivora by a phylogenetic tree generated from the analysis of the internal transcribed spacer (ITS) and translation elongation factor 1-alpha gene (TEF1α) [13]. Diaporthe caulivora D57 was growth in potato dextrose agar (PDA; Difco, Detroit, USA) at 24 °C in 12 h light/12 h darkness photoperiod. Plugs of a 5-day-old culture were used for plant inoculation. SSC-susceptible soybean (Glycine max) plants cultivar Williams (PI548631) were used for all assays. Seeds were obtained from USDA ARS Soybean germplasm collection (seed source 13 U-9280), and planted in 12-cm × 12-cm pots filled with a mix of soil and vermiculite at a rate of 3:1. Plants were grown at 24 °C under a 16 h light/8 h dark lighting regime. Soybean inoculation was performed on 3-week-old (V2 stage) plants, using the stem wounding method described by Mena et al. [13]. Briefly, wounding was performed by making a thin slice along the stem with a sterile scalpel (approx. 7 mm), 1 cm above the cotyledon, and single agar plugs bearing mycelium were carefully placed on the wound that was subsequently sealed with vaseline. PDA plugs without pathogen were placed on wounds as a control.

Genomic DNA extraction, sequencing and de novo D. caulivora genome assembly

Agar plugs (5 mm in diameter) from the growing edge of 8-day-old cultures grown on PDA were transferred to new culture media and incubated at 24 °C under 12 h light and dark photoperiod. After 7 days, when the fungal growth completed the Petri dish, the mycelia were collected from three Petri dishes and frozen with liquid nitrogen. Approximately 150 mg of mycelium was ground with liquid nitrogen. Total genomic DNA was extracted using the DNeasy Plant Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions and quantified with Nanodrop 2000c (Thermo Scientific, Wilmington, USA). PacBio library preparation and sequencing were done in the Integrative Genomics Core Service, Beckman Research Institute, City of Hope (Monrovia, CA, USA). Briefly, a library for single-molecule real-time (SMRT) sequencing was constructed with an insert size of 15 kb using the SMRTbell™ Template Prep kit (Pacific Biosciences of CA, USA). The size of inserts was determined using the BluePippin device (Sage Science, MA, USA). Finally, the whole genome of D. caulivora was sequenced using the PacBio Sequel platform. Subreads were obtained using the SMRT Analysis RS. Flye v.2.7 [69] was used to assemble the data applying standard parameters and an estimated genome size of 50 Mb. The polishing and correction steps are included in the assembly pipelines. The genome graph structure was visualized with Bandage [70] to survey contiguity and ambiguities. Assembly statistics were obtained with QUAST v5.0.2 [71].

Gene prediction and functional annotation

Gene prediction was performed using the FunGap pipeline [72]. Briefly, gene prediction was performed using three publicly available programs, Augustus [73], Braker 1 [74] and Maker [75] housing into the pipeline. Transcriptomic data of D. caulivora strain D57 obtained with the RNASeq protocol was processed with Trimmomatic v0.39 [76] and used for gene model prediction. The parameters using to run FunGAP were: –sister_proteome: Fusarium, − augustus_species fusarium_graminearum and transcript reads as –trans_read: paired-end. Fusarium graminearum was used as the reference species due to the relatively close phylogenetic relationship to Diaporthe among the genome sequences available in GenBank [26]. Single-copy fungal orthologs (fungi_odb10.2019-11-20 gene set) from Benchmarking Universal Single-Copy Orthologs (BUSCO v4) [77] were used to assess the completeness of the genome annotation.

Functional annotation was completed with Blast2GO [45], trough Omicbox software v. 2.0.36 (https://www.biobam.com/omicsbox). Gene models were compared with several databases (NCBI nonredundant protein database, Gene Ontology (GO) Consortium (http://geneontology.org), and InterpoScan) with BlastP finding single hit at an e-value threshold of 1E-20 using taxIds for fungi [78]. InterproScan analysis was used to identify domains in the D. caulivora genome [79]. Classification into GO categories was performed with Blast2GO software using Fusarium graminearum as a reference since protein domain information for D. caulivora was not available [80].

Comparative genome analyses and phylogenetic analysis

The genomes of D. capsici strain GY-Z16 (Gene Bank accession number: WNXA00000000); D. citri strain NFHF_8_4 (Gene Bank accession number: JACTAD000000000); D. destruens strain CRI305_2 (Gene Bank accession number: JACAAM000000000); D. longicolla strain TWHP_74 (Gene Bank accession number: JUJX00000000); D. phragmitis strain NJD1 (Gene Bank accession number: JACDXY000000000) were downloaded from GenBank database of the NCBI website ((https://www.ncbi.nlm.nih.gov/nuccore).

ITS and TEF1α sequences were retrieved from the six Diaporthe genomes and aligned with MUSCLE (v3.8.31) [81], and configured for highest accuracy (MUSCLE with default settings). Iqtree software [82] was used to build a phylogenetic tree, with substitution model TIM2 + F + G4 estimated with Model finder [83] and ultraboostrap 1000.

Gene prediction for each Diaporthe assemblies downloaded was performed using AUGUSTUS web Server (http://bioinf.uni-greifswald.de/webaugustus/), with D. caulivora gene models from this work as a training set with default parameters (UTR prediction: false; report genes on: both strands; alternative transcripts: medium; allowed gene structure: predict any number of (possibly partial) genes.). The average nucleotide identity (ANI) of all Diaporthe species was calculated using PYANI v0.3. 0-alpha with MUMer to align the input sequences (ANIm) (https://github.com/widdowquinn/pyani). The orthologous analysis was conducted using OrthoFinder v2.5.4 [84] with all-versus-all BLAST strategy to define the orthogroups among the six Diaporthe species. Phylogenetic species rooted tree was inferred by multiple sequence alignment and maximum likelihood (options: “-S blast –M msa”) using all orthogroups from the six Diaporthe species based on Species Tree Inference from All Genes method (STAG).

Multiple software tools were jointly used to predict secreted proteins of the six Diaporthe species. SignalP 5.0 (http://www.cbs.dtu.dk/services/SignalP/) [85], WoLF PSORT for Fungi (http://www.genscript.com/psort/wolf_psort.html) [86], and Phobius (http://phobius.sbc.su.se/) [87], were employed to identify signal peptide signatures. Default parameters were used for all programs. For WoLF PSORT, proteins were considered if their extracellular score was ≥17 [88]. Predicted proteins to be signal-peptide positive by all three programs were selected and two additional filtering steps were applied to this set of sequences. First, putative membrane proteins were identified with TMHMM v2.0 (http://www.cbs.dtu.dk/services/TMHMM/) [89] and proteins with a number of expected transmembrane amino acids ≥18 were removed. Then, putative endoplasmic reticulum (ER) targeting proteins, predicted by using PS-Scan (http://prosite.expasy.org/scanprosite/) [90] with Prosite accession PS00014 were also removed. The remaining proteins were considered as secreted proteins and used in subsequent analyses. Additionally, EffectorP V3.0 was used to predict fungal effectors of all Diaporthe species [91,92,93].

The carbohydrate-active enzymes (CAZymes) were identified with dbCAN 5.0, which searches CAZy family-specific HMMs with HMMER3, and NCBI’s conserved domain database CDD [94]. Putative polyketide synthases (PKS) genes were identified using InterProScan and identification of conserved domain as indicated in [27]. To identify proteins involved in pathogenicity, the predicted secretome was used as a query for BlastP (e-value 1E-05) search against the pathogen-host interaction database (PHI-base v4.10) that catalogues experimentally verified pathogenicity, virulence and effector genes from fungal, oomycete and bacterial pathogens [48].

RNA extraction, RNA sequencing and data processing

For transcriptomic analysis, samples were taken at 8 h post inoculation (hpi) and 48 hpi in soybean plants and D. caulivora mycelium grown on PDA plates for 7 days was used as a control. Each treatment consisted of three pots with three plants each at each infection time point, and three plates of mycelium grown on PDA plates. Soybean tissues (stem section of 1.5 cm including the wounded area) and D. caulivora mycelium were harvested for RNA extraction, immediately frozen in liquid nitrogen, and stored at − 80 °C. Soybean stem tissue samples (n = 3) were ground in liquid nitrogen. Total RNA from 100 mg of tissue was extracted and purified with TRIzol reagent (Invitrogen, USA), and using the Invitrogen PureLink RNA Extraction Mini kit (Invitrogen, USA), followed by a DNase I treatment (RNase-Free DNase I). The extraction was performed according to the manufacturer’s instructions. Quality of the isolated RNA was checked by running samples on 1.2% formaldehyde agarose gel. RNA concentration was measured using a NanoDrop 2000c (Thermo Scientific, Wilmington, USA). RNA quality control, library preparation, and sequencing were performed at Macrogen Inc. (Seoul, Korea). Three biological replicates are included by treatment. Libraries for each biological replicate were prepared for paired-end sequencing by TruSeq Stranded Total RNA LT Sample Prep Kit (Plant) with 1 μg input RNA, following the TruSeq Stranded Total RNA Sample Prep Guide, Part # 15,031,048 Rev. E. Sequencing was performed on Illumina platform (Illumina, CA, USA) by Macrogen Inc. (Seoul, Korea) to generate paired-end 101 bp reads, obtaining 41.6 to 65.2 M reads per sample with Q20 > 98% and Q30 > 95%. RNA-seq processing steps were done through Galaxy platform (https://usegalaxy.org/). Raw reads quality was subjected to a quality control check using FastQC software ver. 0.11.2 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Sequences were trimmed, and the adapters removed using Trimmomatic Version 0.38.0 software [76]. Additionally, to the default options, the following parameters were adjusted: adapter sequence TruSeq3 (paired-ended (PE), for MiSeq and HiSeq), always keep both reads of PE, and SLIDINGWINDOW: 4:15 HEADCROP: 13 MINLEN:50.Trimmed reads were mapped to the assembly of the D. caulivora genome obtained before using Hisat2 software [95]. The BAM files were obtained with Samtools View software v. 1.9 and then sorted by name with Samtools Sort software v. 2.0.3 [96], for further analysis.

Reads were counted using FeatureCounts software v. 1.6.4 [97]. Additionally to default options, parameters were adjusted for: count fragments instead of reads, allow read to map to multiple features, and use reference sequence file obtained before for D. caulivora annotation. Cluster analysis of replicates from each time point and control samples were performed by Principal Component Analysis (PCA) using pcaExplorer 2.16.0 software [98]. Counts were normalized to counts per million (cpm) using the TMM method and low expressed genes were filtered out applying a cpm value ≥3 in all samples. Differential expression analyses were performed using EdgeR software ver. 3.24.1 [99]. A false discovery rate (FDR) ≤ 0.05 was used to determine significant differentially expressed genes (DEGs) between D. caulivora grown on soybean stem and D. caulivora grown on PDA (control), and minimum log2 Fold Change 2 expression values were considered. Venn diagram drawing was performed with Venny [100].

Availability of data and materials

The nuclear assembly data of D. caulivora (D57) was deposited into NCBI’s Genome database (https://www.ncbi.nlm.nih.gov/genome/) under the BioProject ID: PRJNA717308. All raw RNA-Seq read data were deposited at NCBI Short Read Archive (http://www.ncbi.nlm.nih.gov/sra/) under the BioProject ID PRJNA717275. The data are under embargo until publication.

Abbreviations

CAZymes:

Carbohydrate-active enzymes

DEG:

Differentially expressed gene

GO:

Gene Ontology

PCA:

Principal Component Analysis

PDA:

Potato dextrose agar

PCWDEs:

Plant cell wall degrading enzymes SSC: soybean stem canker

PHI-base:

Pathogen–Host Interaction base

ROS:

Reactive Oxygen Species

References

  1. Liu JK, Hyde KD, Jones EBG, Ariyawansa HA, Bhat DJ, Boonmee S, et al. Fungal diversity notes 1-110: taxonomic and phylogenetic contributions to fungal species. Fungal Divers. 2015;72:1–197.

    Article  CAS  Google Scholar 

  2. Hyde KD, Hongsanan S, Jeewon R, Bhat DJ, McKenzie EHC, Jones EBG, et al. Fungal diversity notes 367-491: taxonomic and phylogenetic contributions to fungal taxa. Fungal Divers. 2016;80:1–270.

    Article  Google Scholar 

  3. Marin-Felix Y, Hernández-Restrepo M, Wingfield MJ, Akulov A, Carnegie AJ, Cheewangkoon R, et al. Genera of phytopathogenic fungi: GOPHY 2. Stud Mycol. 2019;92:47–133.

    Article  CAS  PubMed  Google Scholar 

  4. Yang Q, Fan XL, Guarnaccia V, Tian CM. High diversity of Diaporthe species associated with twelve new species described. MycoKeys. 2018;39:97–149.

    Article  Google Scholar 

  5. Udayanga D, Castlebury LA, Rossman AY, Hyde KD. Species limits in Diaporthe: molecular re-assessment of D citri D cytosporella D foeniculina and D rudis. Persoonia. 2014;32:83–101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Fang X, Qin K, Li S, Han S, Zhu T, Fang X, et al. Whole genome sequence of Diaporthe capsici a new pathogen of walnut blight. Genomics. 2020;112:3751–61.

    Article  CAS  PubMed  Google Scholar 

  7. Thompson SM, Tan YP, Young AJ, Neate SM, Aitken EAB, Shivas RG. Stem cankers on sunflower (Helianthus annuus) in Australia reveal a complex of pathogenic Diaporthe (Phomopsis) species. Persoonia. 2011;27:80–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Udayanga D, Castlebury LA, Rossman AY, Chukeatirote E, Hyde KD. The Diaporthe sojae species complex: phylogenetic re-assessment of pathogens associated with soybean cucurbits and other field crops. Fungal Biol. 2015;119:383–407.

    Article  PubMed  Google Scholar 

  9. Fernández FA, Philips DV, Russin JS, Rupe JC. Diaporthe-Phomopsis complex. In: Compendium of soybean diseases Hartman GLJB Sinclair and JC Rupe (4th ed). St Paul Minnesota: APS Press; 1999. p. 33–5.

    Google Scholar 

  10. Pioli RN, Morandi EN, Martínez MC, Lucca F, Tozzini A, Bisaro V, et al. Morphologic molecular and pathogenic characterization of Diaporthe phaseolorum variability in the core soybean-producing area of Argentina. Phytopathology. 2003;93:136–46.

    Article  PubMed  Google Scholar 

  11. Santos JM, Vrandečić K, Cosić J, Duvnjak T, Phillips AJ. Resolving the Diaporthe species occurring on soybean in Croatia. Persoonia. 2011;27:9–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ghimire K, Petrović K, Kontz BJ, Bradley CA, Chilvers MI, Mueller DS, et al. Inoculation method impacts symptom development associated with Diaporthe aspalathi D caulivora and D longicolla on soybean (Glycine max). Plant Dis. 2019;103(4):PDIS06181078RE.

  13. Mena E, Stewart S, Montesano M, Ponce de León I. Soybean stem canker caused by Diaporthe caulivora; pathogen diversity colonization process and plant defense activation. Front Plant Sci. 2020;10:1733.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Backman PA, Weaver DB, Morgan-Jones G. Soybean stem canker: an emerging disease problem. Plant Dis. 1985;69:641–7.

    Article  Google Scholar 

  15. Grijalba PE, Guillin E. Occurrence of soybean stem canker caused by Diaporthe phaseolorum var caulivora in the southern part of Buenos Aires province Argentina. Australas Plant Dis Notes. 2007;2:65–6.

    Article  Google Scholar 

  16. Wrather JA, Koenning SR. Effects of diseases on soybean yields in the United States 1996 to 2007. Plant Health Prog. 2009;10(1).

  17. Maldonado dos Santos JV, Ferreira EGC, Passianotto AL, Brumer BB, Santos ABD, Soares RM, et al. Association mapping of a locus that confers southern stem canker resistance in soybean and SNP marker development. BMC Genomics. 2019;20:798.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Bandara AY, Weerasooriya DK, Bradley CA, Allen TW, Esker PD. Dissecting the economic impact of soybean diseases in the United States over two decades. PLoS One. 2020;15:e0231141.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Roth MG, Webster RW, Mueller DS, Chilvers MI, Faske TR, Mathew FM, et al. Integrated management of important soybean pathogens of the United States in changing climate. J Integr Pest Manag. 2020;11:1–28.

    Article  Google Scholar 

  20. Chiesa MA, Pioli RN, Morandia EN. Specific resistance to soybean stem canker conferred by the Rdm4 locus. Plant Pathol. 2009;58:1032–8.

    Article  CAS  Google Scholar 

  21. Peruzzo AM, Hernández FE, Pratta GR, Ploper LD, Pioli RN. Identification and inheritance of an Rdc gene resistance to soybean stem canker (Diaporthe phaseolorum var caulivora). Eur J Plant Pathol. 2019;154:1179–84.

    Article  CAS  Google Scholar 

  22. Grijalba P, Ridao AC. Survival of Diaporthe phaseolorum var caulivora (causal agent of soybean stem canker) artificially inoculated in different crop residues. Trop Plant Pathol. 2012;37:271–4.

    Article  Google Scholar 

  23. Selin C, de Kievit TR, Belmonte MF, Fernando WGD. Elucidating the role of effectors in plant-fungal interactions: Progress and challenges. Front Microbiol. 2016;7:600.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Klosterman SJ, Rollins JR, Sudarshana MR, Vinatzer BA. Disease management in the genomics era - summaries of focus issue papers. Phytopathology. 2016;106:1068–70.

    Article  CAS  PubMed  Google Scholar 

  25. Chang HX, Yendrek CR, Caetano-Anolles G, Hartman GL. Genomic characterization of plant cell wall degrading enzymes and in silico analysis of xylanses and polygalacturonases of Fusarium virguliforme. BMC Microbiol. 2016;16:147.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Li S, Darwish O, Alkharouf NW, Musungu B, Matthews BF. Analysis of the genome sequence of Phomopsis longicolla: a fungal pathogen causing Phomopsis seed decay in soybean. BMC Genomics. 2017;18:688.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Ruocco M, Baroncelli R, Cacciola SO, Pane C, Monti MM, Firrao G, et al. Polyketide synthases of Diaporthe helianthi and involvement of DhPKS1 in virulence on sunflower. BMC Genomics. 2018;19:27–39.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Li S, Song Q, Pingsheng J, Cregan P. Draft genome sequence of Phomopsis longicolla type strain TWH P74 a fungus causing phomopsis seed decay in soybean. Genome Announc. 2015;3(1):e00010–5.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Li S, Song Q, Martins AM, Cregan P. Draft genome sequence of Diaporthe aspalathi isolate MS-SSC91 a fungus causing stem canker in soybean. Genomics data. 2016;7:262–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Morales-Cruz A, Amrine KC, Blanco-Ulate B, Lawrence DP, Travadon R, Rolshausen PE, et al. Distinctive expansion of gene families associated with plant cell wall degradation secondary metabolism and nutrient uptake in the genomes of grapevine trunk pathogens. BMC Genomics. 2015;16:469.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Baroncelli R, Scala F, Vergara M, Thon M, Ruocco M. Draft whole-genome sequence of the Diaporthe helianthi 7/96 strain causal agent of sunflower stem canker. Genom Data. 2016;10. https://doi.org/10.1016/jgdata20161100.

  32. Savitha J, Bhargavi SD, Praveen VK. Complete genome sequence of the endophytic fungus Diaporthe (Phomopsis) ampelina. Genome Announc. 2016;4:e00477–16.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Tulsook K, Isarangkul D, Sriubolmas N, Kittakopp P, Wiyakrutta S. Genome sequence of Diaporthe sp strain HANT25 an endophytic fungus producing mycoepoxydiene. Microbiol Resour Announc. 2020;9:e00805–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Liu X, Chaisiri C, Lin Y, Yin W, Luo C. Whole-genome sequence of Diaporthe citri isolate NFHF-8-4 the causal agent of citrus melanose. Mol Plant-Microbe Interact. 2021;34(7):845–7.

    Article  PubMed  Google Scholar 

  35. Gai Y, Xiong T, Xiao X, Li P, Zeng Y, Li L, et al. The genome sequence of the Citrus melanose pathogen Diaporthe citri and two citrus-related Diaporthe species. Phytopathology. 2021;111(5):779–83.

    Article  PubMed  Google Scholar 

  36. Huang L, Zhang X, Yang Y, Zou H, Fang B, Liu W. High-quality genome resource of Diaporthe destruens causing foot rot disease of sweet potato. Plant Dis. 2021;105(10):3279–81.

    Article  PubMed  Google Scholar 

  37. Wang X, Dong H, Lan J, Liu Y, Liang K, Lu Q, et al. High-quality genome resource of the pathogen of Diaporthe (Phomopsis) phragmitis causing kiwifruit soft rot. Mol Plant-Microbe Interact. 2021;34:218–21.

    Article  CAS  PubMed  Google Scholar 

  38. Hosseini B, El-Hassan A, Immanuel H, Voegele R. Analysis of the species spectrum of the Diaporthe/Phomopsis complex in European soybean seeds. Mycol Prog. 2020;19(5):455–69.

    Article  Google Scholar 

  39. Zambelli A, Mancebo MF, Bazzalo ME, Reid RJ, Sanchez MC, Kontz BJ, et al. Six species of Diaporthe associated with Phomopsis stem canker of sunflower in southern pampean region of Argentina. Plant Health Prog. 2021;22(2):136–42.

    Article  Google Scholar 

  40. Nozaki M, Camargo M, Barreto M. Caracterização de Diaporthe citri em diferentes meios de cultura condições de temperatura e luminosidade. Fitopatol Bras. 2004;29(4):429–32.

  41. Mathew FM, Rashid KY, Gulya TJ, Markell SG. First report of Phomopsis stem canker of sunflower (Helianthus annuus) caused by Diaporthe gulyae in Canada. Plant Dis. 2015;99(1):160.

    Article  CAS  PubMed  Google Scholar 

  42. Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34(18):3094–100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Tang H, Lyons E, Pedersen B, Schnable JC, Paterson AH, Freeling M. Screening synteny blocks in pairwise genome comparisons through integer programming. BMC Bioinformatics. 2011;102:1–11.

  44. Kurtz S, Phillippy A, Delcher AL, Smoot M, Shumway M, Antonescu C, et al. Versatile and open software for comparing large genomes. Genome Biol. 2004;5:R12.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Conesa A, Götz S. Blast2gGO: A comprehensive suite for functional analysis in plant genomics. Int J Plant Genom. 2008;2008:619832.

  46. McCotter SW, Horianopoulos LC, Kronstad JW. Regulation of the fungal secretome. Curr Genet. 2016;62:533–45.

    Article  CAS  PubMed  Google Scholar 

  47. Zhao Z, Liu H, Wang C, Xu JR. Comparative analysis of fungal genomes reveals different plant cell wall degrading capacity in fungi. BMC Genomics. 2013;14:274.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Winnenburg R, Urban M, Beacham A, Baldwin TK, Holland S, Lindeberg M, et al. PHI-base update: additions to the pathogen host interaction database. Nucleic Acids Res. 2008;36:572–6.

    Article  Google Scholar 

  49. Li D, Chung KR, Smith DA, Schardl CL. The Fusarium solani gene encoding kievitone hydratase a secreted enzyme that catalyzes detoxification of a bean phytoalexin. Mol Plant-Microbe Interact. 1995;8(3):388–97.

    Article  CAS  PubMed  Google Scholar 

  50. Silva JC, Townsend CA. Heterologous expression isolation and characterization of versicolorin B synthase from Aspergillus parasiticus a key enzyme in the aflatoxin B1 biosynthetic pathway. J Biol Chem. 1997;272(2):804–13.

    Article  CAS  PubMed  Google Scholar 

  51. Toruño TY, Stergiopoulos I, Coaker G. Plant-pathogen effectors: cellular probes interfering with plant defenses in spatial and temporal manners. Annu Rev Phytopathol. 2016;54:419–41.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Caceres I, Al Khoury A, El Khoury R, Lorber S, Oswald IP, El Khoury A, et al. Aflatoxin biosynthesis and genetic regulation: A Review. Toxins. 2020;12:150.

    Article  CAS  PubMed Central  Google Scholar 

  53. Westrick NM, Smith DL, Kabbage M. Disarming the host: detoxification of plant defense compounds during fungal necrotrophy. Front Plant Sci. 2021;12:651716.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Hwang CS, Kolattukudy PE. Isolation and characterization of genes expressed uniquely during appressorium formation by Colletotrichum gloeosporioides conidia induced by the host surface wax. Mol Gen Genet. 1995;247(3):282–94.

    Article  CAS  PubMed  Google Scholar 

  55. Morrissey JP, Osbourn AE. Fungal resistance to plant antibiotics as a mechanism of pathogenesis. Microbiol Mol Biol Rev. 1999;63:708–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Segal LM, Wilson RA. Reactive oxygen species metabolism and plant-fungal interactions. Fungal Genet Biol. 2018;110:1–9.

    Article  CAS  PubMed  Google Scholar 

  57. Abdul W, Aliyu SR, Lin L, Sekete M, Chen X, Otieno FJ, et al. Family-four aldehyde dehydrogenases play an indispensable role in the pathogenesis of Magnaporthe oryzae. Front Plant Sci. 2018;9:980.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Maloney A, VanEtten H. A gene from the fungal plant pathogen Nectria haematococca that encodes the phytoalexin-detoxifying enzyme pisatin demethylase defines a new cytochrome P450 family. Mol Gen Genet. 1994;243:506–14.

    Article  CAS  PubMed  Google Scholar 

  59. Tokai T, Koshino H, Takahashi-Ando N, Sato M, Fujimura M, Kimura M. Fusarium Tri4 encodes a key multifunctional cytochrome P450 monooxygenase for four consecutive oxygenation steps in trichothecene biosynthesis. Biochem Biophys Res Commun. 2007;353:412–7.

    Article  CAS  PubMed  Google Scholar 

  60. Sahu BB, Baumbach JL, Singh P, Srivastava SK, Yi X, Bhattacharyya MK. Investigation of the Fusarium virguliforme transcriptomes induced during infection of soybean roots suggests that enzymes with hydrolytic activities could play a major role in root necrosis. PLoS One. 2017;12:e0169963.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Coleman JJ, Wasmann CC, Usami T, White GJ, Temporini ED, McCluskey K, et al. Characterization of the gene encoding pisatin demethylase (FoPDA1) in Fusarium oxysporum. Mol Plant-Microbe Interact. 2011;24:1482–91.

    Article  CAS  PubMed  Google Scholar 

  62. Cheah E, Ashley GW, Gary J, Oilis D. Catalysis by dienelactone hydrolase: a variation on the protease mechanism. Proteins. 1993;16:64–78.

    Article  CAS  PubMed  Google Scholar 

  63. Bowyer PBR, Clarke P, Lunness MJ, Osbourn AE. Host range of a plant pathogenic fungus determined by a saponin detoxifying enzyme. Science. 1995;267:371–4.

    Article  CAS  PubMed  Google Scholar 

  64. Zhang Y, Gao Y, Liang Y, Dong Y, Yang X, Yuan J, et al. The Verticillium dahliae SnodProt1-like protein VdCP1 contributes to virulence and triggers the plant immune system. Front Plant Sci. 2017;8:1880.

    Article  PubMed  PubMed Central  Google Scholar 

  65. Modolo LV, Cunha FQ, Braga MR, Salgado I. Nitric oxide synthase-mediated phytoalexin accumulation in soybean cotyledons in response to the Diaporthe phaseolorum f sp meridionalis elicitor. Plant Physiol. 2002;130:1288–97.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Lalitha B, Snow JP, Berggren GT. Phytotoxin production by Diaporthe phaseolorum var caulivora the causal organism of stem canker of soybean. Phytopathology. 1989;79:499–504.

    Article  CAS  Google Scholar 

  67. Pietro AD, Huertas-González MD, Gutierrez-Corona JF, Martínez-Cadena G, Méglecz E, Roncero MIG. Molecular characterization of a subtilase from the vascular wilt fungus Fusarium oxysporum. Mol Plant-Microbe Interact. 2001;14:653–62.

    Article  PubMed  Google Scholar 

  68. Coleman JJ, Mylonakis E. Efflux in fungi: La pièce de résistance. PLoS Pathog. 2009;5:e1000489.

    Article  Google Scholar 

  69. Kolmogorov M, Yuan J, Lin Y, Pevzner PA. Assembly of long error-prone reads using repeat graphs. Nat Biotechnol. 2019;37(5):540–6.

    Article  CAS  PubMed  Google Scholar 

  70. Wick RR, Schultz MB, Zobel J, Holt KE. Bandage: interactive visualization of de novo genome assemblies. Bioinformatics. 2015;31:3350–2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Min B, Grigoriev IV, Choi IG. FunGAP: fungal genome annotation pipeline using evidence-based gene model evaluation. Bioinformatics. 2017;33:2936–7.

    Article  CAS  PubMed  Google Scholar 

  73. Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B. AUGUSTUS: Ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006;34:435–9.

    Article  Google Scholar 

  74. Hoff KJ, Lange S, Lomsadze A, Borodovsky M, Stanke M. BRAKER1: unsupervised RNA-Seq-based genome annotation with GeneMark-ET and AUGUSTUS. Bioinformatics. 2016;32:767–9.

    Article  CAS  PubMed  Google Scholar 

  75. Cantarel BL, Korf I, Robb SMC, Parra G, Ross E, Moore B, et al. MAKER: an easy-to-use annotation pipeline designed for emerging model organism genomes. Genome Res. 2008;18:188–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Simão FA, Waterhouse RM, Ioannidis P, Kriventseva EV, Zdobnov EM. BUSCO: assessing genome assembly and annotation completetness with single-copy orthologs. Bioinformatics. 2015;31:3210–2.

    Article  PubMed  Google Scholar 

  78. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10.

    Article  CAS  PubMed  Google Scholar 

  79. Zdobnov EM, Apweiler R. InterProScan--an integration platform for the signature-recognition methods in InterPro. Bioinformatics. 2001;17(9):847–8.

    Article  CAS  PubMed  Google Scholar 

  80. Güldener U, Mannhaupt G, Münsterkötter M, Haase D, Oesterheld M, Stümpflen V, et al. FGDB: a comprehensive fungal genome resource on the plant pathogen Fusarium graminearum. Nucleic Acids Res. 2006;1(34):D456–8.

    Article  Google Scholar 

  81. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32(1):268–74.

    Article  CAS  PubMed  Google Scholar 

  83. Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14(6):587–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Emms DM, Kelly S. OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy. Genome Biol. 2015;16:157.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Petersen TN, Brunak S, Von Heijne G, Nielsen H. SignalP 40: discriminating signal peptides from transmembrane regions. Nat Methods. 2011;8:785–6.

    Article  CAS  PubMed  Google Scholar 

  86. Horton P, Park KJ, Obayashi T, Fujita N, Harada H, et al. WoLF PSORT: protein localization predictor. Nucleic Acids Res. 2007;35:585–7.

    Article  Google Scholar 

  87. Kall L, Krogh A, Sonnhammer ELL. Advantages of combined transmembrane topology and signal peptide prediction - the Phobius web server. Nucleic Acids Res. 2007;35:429–32.

    Article  Google Scholar 

  88. do Amaral AM, Antoniw J, Rudd JJ, Hammond-Kosack KE. Defining the predicted protein secretome of the fungal wheat leaf pathogen Mycosphaerella graminicola. PLoS One. 2012;7:e49904.

    Article  Google Scholar 

  89. Kroghs A, Larsson B, von Heijne G, Sonnhammer ELL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol. 2001;305:567–80.

    Article  Google Scholar 

  90. de Castro E, Sigrist CJA, Gattiker A, Bulliard V, Langendijk-Genevaux PS, Gasteiger E, et al. ScanProsite: detection of PROSITE signature matches and ProRule associated functional and structural residues in proteins. Nucleic Acids Res. 2006;34:362–5.

    Article  Google Scholar 

  91. Sperschneider J, Gardiner DM, Dodds PN, Tini F, Covarelli L, Singh KB, et al. EffectorP: predicting fungal effector proteins from Secretomes using machine learning. New Phytol. 2016;210:743–61.

    Article  CAS  PubMed  Google Scholar 

  92. Sperschneider J, Dodds PN, Gardiner DM, Singh KB, Taylor JM. Improved prediction of fungal effector proteins from secretomes with EffectorP 20. Mol Plant Pathol. 2018;19:2094–110.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Sperschneider J, Dodds PN, Singh KB, Taylor JM. ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning. New Phytol. 2017;217:1764–78.

    Article  PubMed  Google Scholar 

  94. Yin Y, Mao X, Yang J, Chen X, Mao F, Xu Y. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2012;40:445–51.

    Article  Google Scholar 

  95. Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12:357–60.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. 1000 genome project data processing subgroup the sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.

    Article  PubMed  PubMed Central  Google Scholar 

  97. Liao Y, Smyth GK, Shi W. FeatureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2013;30:923–30.

    Article  PubMed  Google Scholar 

  98. Martini F, Binder H. pcaExplorer: an R/bioconductor package for interacting with RNA-seq principal components. BMC Bioinform. 2019;20:1–8.

    Google Scholar 

  99. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2009;26:139–40.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Oliveros JC. Venny: an interactive tool for comparing lists with Venn's diagrams. 2007-2015.https://bioinfogp.cnb.csic.es/tools/venny/index2.0.2.html.

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Acknowledgments

The authors would like to acknowledge Dr. Luisa Berná for her support with the D. caulivora genomic analysis and Ricardo Larraya for technical assistance. This work was funded by “Agencia Nacional de Investigación e Innovación (ANII) (grant RTS-1-2014, and graduate fellowship)” Uruguay, “Programa de Desarrollo de las Ciencias Básicas (PEDECIBA)” Uruguay, and “Programa para Grupo de I + D Comisión Sectorial de Investigación Científica, Universidad de la República”, Uruguay.

Funding

This work was funded by “Agencia Nacional de Investigación e Innovación (ANII) (grant RTS-1-2014, and graduate fellowship)” Uruguay, “Programa de Desarrollo de las Ciencias Básicas (PEDECIBA)” Uruguay, and “Programa para Grupo de I + D Comisión Sectorial de Investigación Científica, Universidad de la República”, Uruguay.

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Author notes

  1. Eilyn Mena and Silvia Garaycochea contributed equally to this work.

Authors and Affiliations

  1. Departamento de Biología Molecular, Instituto de Investigaciones Biológicas Clemente Estable, Avenida Italia 3318, CP 11600, Montevideo, Uruguay

    Eilyn Mena, Marcos Montesano & Inés Ponce De León

  2. Instituto Nacional de Investigación Agropecuaria (INIA), Estación Experimental INIA Las Brujas, Ruta 48 Km 10, Canelones, Uruguay

    Silvia Garaycochea

  3. Instituto Nacional de Investigación Agropecuaria (INIA), Programa Cultivos de Secano, Estación Experimental La Estanzuela, Ruta 50 km 11, 70000, Colonia, Uruguay

    Silvina Stewart

  4. Laboratorio de Fisiología Vegetal, Centro de Investigaciones Nucleares, Facultad de Ciencias, Universidad de la República, Mataojo 2055, CP 11400, Montevideo, Uruguay

    Marcos Montesano

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  1. Eilyn Mena
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Contributions

EM performed the DNA and RNA extraction, aligned and mapped the reads obtained by RNAseq to the D. caulivora genome, analyzed the data and helped to write the manuscript. SG assembled and annotated the D. caulivora genome, performed gene identification and comparative genomic analysis, analyzed the data and helped to write the manuscript. SS interpreted the data and contributed to discussions. MM interpreted the data, contributed to discussions and helped to write the manuscript. IPDL supervised the study, interpreted the data, and wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Inés Ponce De León.

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Supplementary Information

Additional file 1. 

General features of Diaporthe genomes.

Additional file 2. 

Characteristics of selected Diaporthe species.

Additional file 3. 

Phylogenetic tree generated from the analysis of internal transcribed spacer (ITS) and translation elongation factor 1-alpha gene (TEF1α) regions of the six Diaporthe species used in this study. The number at the branch nodes indicates bootstrap values (%) built on 1000 replications. The Diaporthe species used in this study are indicated in red and the ex-types strains in black. Fusarium graminearum was used as outgroup. ITS and TEF1α sequences of D. destruens were only obtained from the genome of strain CRI305_2 since sequences of other D. destruens strains were not available at NCBI.

Additional file 4. 

Synteny analysis between D. caulivora and other Diaporthe species. The synteny circle plots show the large synthenic blocks between D. caulivora and the other five Diaporthe species obtained using SyMAP.

Additional file 5. 

Gene prediction and annotation of D. caulivora.

Additional file 6. 

Diaporthe genes encoding predicted secreted proteins shared with one or more Diaporthe species or present only in one species.

Additional file 7. 

Predicted D. caulivora secreted proteins and effector candidates absent in the other five Diaporthe genomes. A NCBI search was performed showing hit with other Diaporthe and fungal species.

Additional file 8. 

Predicted CAZymes in the different Diaporthe species. CAZymes shared with one or more Diaporthe species or present only in one species are indicated.

Additional file 9. 

Summary of D. caulivora genes encoding predicted secreted proteins and effector candidates with functionally characterized homologs in Pathogen-Host Interaction database (PHI-base).

Additional file 10. 

Effectors candidates in Diaporthe secretomes shared with one or more Diaporthe species or present only in one species.

Additional file 11. 

Summary of mapped reads of D. caulivora in the RNA-Seq libraries. 1–3 indicate the three biological replicates during soybean infection at the indicated time points and control PDA grown mycelium.

Additional file 12. 

List of D. caulivora differentially expressed genes (DEGs) during soybean infection at 8 and 48 hpi.

Additional file 13. 

List of pathogenicity related D. caulivora differentially expressed genes (DEGs) present in different categories: secretome, CAZymes, effectors, and their functionally characterized homologs in PHI-base.

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Mena, E., Garaycochea, S., Stewart, S. et al. Comparative genomics of plant pathogenic Diaporthe species and transcriptomics of Diaporthe caulivora during host infection reveal insights into pathogenic strategies of the genus. BMC Genomics 23, 175 (2022). https://doi.org/10.1186/s12864-022-08413-y

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Keywords

BMC Genomics

ISSN: 1471-2164

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