Introduction

Ceratocystis canker of almond, also known as the mallet wound canker, is caused by the ascomycete fungal pathogen Ceratocystis destructans. The disease mainly develops on areas of the trunk or branches that have been damaged by tractors, hedgers, harvesting equipment, and at pruning wounds. Ceratocystis canker shorten the life span and productivity of affected trees, overall reducing the orchard’s profitability. Symptoms of Ceratocystis canker include water-soaked lesions with amber-colored gum balls developing at the canker margin (Fig. 1). With time, cankers may girdle and kill infected branches or trunks. C. destructans is spread by several species of sap-feeding insects, including fruit flies and beetles, which either ingest fungal spores and later excrete them or transport them on their bodies to new locations1,2. Wounds on almond trees are susceptible to Ceratocystis for about 8 to 14 days, thereafter the chances of infection are greatly reduced3. Once spores are spread to fresh almond wounds and the new canker establishes, there is no cure for the infected tree. The most effective way to manage Ceratocystis cankers is to avoid injuries to trunks and scaffold branches. Tree surgery can be used eventually in an attempt to remove the infected area of cankers. However, this technique is tedious and rarely successful, in which case the fungus continues to grow. Recently, Holland et al.4 reported a widespread occurrence of Ceratocystis canker, which appears ubiquitous in almond-producing counties of California. The same authors concluded that C. destructans is likely endemic to California, occurring naturally on the native vegetation surrounding orchards5.

Figure 1
figure 1

Symptoms and impacts of Ceratocystis canker on almonds. Ceratocystis canker developing near large pruning wounds (a). A closer look at an active Ceratocystis canker extending from a pruning wound site, with amber-colored gum balls developing at the canker margin (b). Dieback of an entire almond tree resulting from Ceratocystis canker which girdled the trunk (c). A pile of almond logs removed from Ceratocystis canker infested trees from an orchard (d).

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To colonize woody plants, trunk pathogens rely on carbohydrate active enzyme (CAZymes), as they are responsible for the breakdown of plant cell wall components (cellulose, hemicellulose, xylan, xyloglucan, mannan and pectin)6,7. Owing to their involvement in plant cell wall degradation during the colonization of plants by phytopathogens, CAZymes are an important virulence factor of phytopathogens8,9. Besides CAZymes, several secondary metabolites and their toxins, membrane transporters, and cytochrome P450 monooxygenases have been shown to be critical virulence factors in most canker pathosystems10,11,12,13. Cytochrome P450s facilitate fungal adaptations to various ecological niches by buffering harmful chemical and biological cues13,14. They also play a critical role in the post-synthesis modification of diverse metabolites14. Transporters such as the major facilitator superfamily (MFS) are single-polypeptide secondary carriers capable of transporting small solutes in response to chemiosmotic ion gradients. These can play a crucial role in antifungal resistance and nutrient transport15,16, which is crucial for pathogenesis in response to reactive oxygen species. Such a wealth of knowledge about virulence factors of phytopathogenic fungi can be exploited to infer hypothetical proteins involved in the pathogenesis and or virulence of new species. And this can be built-upon for further studies, such as pathogen-host interactions, contributing to advanced understanding of emerging/new phytopathogens.

Advances in whole genome sequencing along with many bioinformatics computational tools offer an unprecedented capability to generate and mine genomic sequences of plant pathogens17. The fungal secretome is the set of proteins that contain a signal peptide and are processed via the endoplasmic reticulum and golgi apparatus, among which effectors help circumvent host defenses by either masking the presence of the pathogen or killing the host cell directly18,19. Effectors and the other established virulence factors of canker pathogens can be mined from genome sequences. Several bioinformatics tools which can be integrated to predict fungal virulence factors from genomic sequences are readily available. These include but are not limited to SECRETOOL20, CAZy database21, antiSMASH22, Fungal cytochrome P450 database (FCPD)23, SignalP24, and effectorP25. The wealth of information obtained from these and other tools provides a baseline for experimental work, such as understanding how pathogens colonize the host, leading to a better solution for the control and management of plant pathogens.

In the present study, we present high-quality genome assemblies of five C. destructans isolates (KARE490, KARE979, KARE1428, KARE1622, and KARE1624) obtained from canker-infested almond (P. dulcis) trees in California. The main differences among the isolates were the geographical location from which they were isolated, albeit all of them were from the San Joaquin Valley, California. For precise location details, refer to5. Currently, there are no publicly available C. destructans genomes, and this report presents the first comprehensive genomic resource of an important almond canker pathogen. The study also presents descriptions of the potential roles of some of the predicted virulence factors. To benchmark the analysis of our genome sequences, we included the genomes of C. fimbriata and C. albifundus, two well-established Ceratocystis species with available genomic information. C. fimbriata, the type species of the genus Ceratocystis, causes black rot of sweet potato as well as wilt or cankers in rubber trees, coffee, and Eucalyptus spp.26. C. albifundus is an aggressive tree pathogen native to Africa, mainly known as the causal agent of Ceratocystis wilt in Acacia mearnsii27. The genome of C. albifundus has been sequenced several times, and it is well annotated and less fragmented, making it good for benchmarking our analysis.

Results and discussion

Genome sequencing, assembly, and protein prediction

Genomes of the Ceratocystis destructans isolates were sequenced with Illumina HiSeq2500 and assembled to a genome size of 27.2 ± 0.9 Mbp organized into 663.2 ± 34.1 scaffolds having an N50 of 179.96 ± 18.9 Kbp (Table 1). Table 1 provides detailed assembly and quality metrics for each of the five sequenced isolates (KARE979, KARE1622, KARE1624, KARE1428, and KARE490). The relatively small genome size of C. destructans isolates is in congruence with the genome sizes of other published Ceratocystis genomes28,29,30,31,32, Table 1. Assessment of genome integrity by using BUSCO analyses33 showed that most of the conserved core fungi genes were predicted (> 97%; Table 1), which indicates that our genomes were almost complete. The total predicted protein-coding genes for the C. destructans isolates, and those for C. albifundus and C. fimbriata are shown in Table 2. The number of the total predicted protein-coding genes in C. destructans did not greatly differ from those of C. albifundus and C. fimbriata, another indication of good quality for our newly sequenced genomes (Table 2). The relatively smaller size of the C. destructans genomes compared to most canker pathogens like Eutypa lata, Neofusicoccum spp., and Diplodia spp. correlated with their small number of predicted protein-coding genes, which is consistent with previous reports on the genomes of other Ceratocystis species28,29,30,31,32. Though we combined two algorithms, Augustus, and GeneMark-ES for gene predictions, integrating C. destructans transcripts in the analyses will likely improve the protein-coding gene prediction. Integration of RNA-seq data has been shown to significantly improve protein-coding gene prediction in eukaryotes12,34,35. Thus, predictions from this study could be further refined in the future by integrating RNA-seq data, using C. destructans transcripts obtained under a range of experimental conditions. Moreover, C. destructans transcripts in planta would be a very useful resource and might lead to the identification of candidate pathogenicity genes that are only expressed when the pathogen is growing within host tissues, as was recently observed for an Ophiostoma novo-ulmi ortholog of gene AOX1 encoding an alcohol oxidase36. Analysis of C. destructans transcripts at different time points during the colonization of almond tissue could further enhance our knowledge of C. destructans genes expressed when the pathogen is growing within host tissue.

Table 1 Assembly and quality metrics of the Ceratocystis destructans isolates and the related Ceratocystis genomes.
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Table 2 Protein-coding genes predicted as secreted CAZymes, or part of a gene cluster encoding secondary metabolites.
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Gene content comparison with a focus on pathogenesis-related features

The C. destructans isolates shared 6,403 orthologous genes (Fig. 2A). 64. 2% (4, 110) of the shared orthologous genes had a GO annotation, of which only 33 gene clusters had a “pathogenesis” GO annotation. These included 1-phosphatidylinositol phosphodiesterase (14), major facilitator superfamily multidrug transporter NAG4, sucrose non-fermenting protein kinase 1, dipeptidase sirJ, probable aspartic-type endopeptidase, D-arabinitol dehydrogenase 1, transcription activator AMTR1, acyl-CoA ligase sidI, calcium channel YVC1, ferric/cupric reductase transmembrane component 1, C2H2 finger domain transcription factor (mtfA and dvrA), beta-1,2-xylosyltransferase 1, bZIP transcription factor hapX, bZIP-type transcription factor MBZ1, phospho-2-dehydro-3-deoxyheptonate aldolase AMT16, initiation-specific alpha-1,6-mannosyltransferase, and carboxypeptidase S1 homolog. Pathogenesis GO annotations were supplemented by querying the pathogen-host interaction database (PHI-base). All the proteins with a pathogenesis GO annotation had a hit in the PHI-base except for beta-1,2-xylosytransferase 1 (Supplementary data 1). Supplementary data 1 also provides the phenotypes of the top hits in the PHI-base, and the phenotypes of the other hits.

Figure 2
figure 2

Venn diagrams showing the distribution of shared orthologous clusters among the Ceratocystis genomes. Shared orthologous clusters among C. destructans isolates (a). Shared orthologous clusters among C. fimbriata, C. albifundus and the C. destructans isolates (b).

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A very high copy number of 1-phosphatidylinositol phosphodiesterase orthologous group have also been reported in other Ceratocystis genome studies. The evolution of this gene family is surmised to be guided by positive selection31,32, suggesting that this family is critical for the Ceratocystis genus. Phosphatidylinositol-specific phospholipases are responsible for cleaving glycerophospholipids containing the polar head, and they have been associated with functions such as pathogenicity and release of glycosyl-PI (GPI) anchored surface proteins from target membranes37,38,39. GPI-anchored proteins play an important role in the virulence of fungal pathogens by serving as adhesins and variable epitopes to evade the immune system40. In Magnaporthe oryzae, deletion of the fungal phospholipase C gene suppressed calcium flux resulting in defective appressorium formation and pathogenicity41. With the evolution of the phosphatidylinositol phosphodiesterase surmised to be guided by positive selection, it will be interesting to conduct in planta transcriptomics and functional deletion studies that might shed more information into the role of C. destructans 1-phosphatidylinositol phosphodiesterase family. Recently, a few protocols have been developed for successfully genetic manipulation of Ceratocystis spp. that opens the door for possibilities of producing knockout mutants in C. destructans. These include agrobacterium-based transformation42,43 and the CRISPR based method44.

When phytopathogenic fungi invade plants, the production of reactive oxygen species at the infection site is the earliest response of pathogen-associated molecular pattern triggered immunity (PTI). This triggers a cascade of effects such as cell wall strengthening, induction of antimicrobial activity, synthesis of pathogenesis-related proteins and phytoalexins, and programmed cell death45. However, fungal pathogens have developed a broad arsenal of effector proteins to antagonize plant response to infection. Through D-arabinitol dehydrogenase 1, they can produce D-arabinitol which is capable of quenching reactive oxygen species involved in host plant defense reactions, thus protecting the fungus during the pathogenic interaction46. Moreover, transporters such as the major facilitator superfamily (MFS), which are single-polypeptide secondary carriers capable of transporting small solutes in response to chemiosmotic ion gradients, can play a crucial role in antifungal resistance and nutrient transport15,16. This is crucial for pathogenesis in response to reactive oxygen species. Null mutations in Candida albicans major facilitator superfamily multidrug transporter NAG4 (CaNAG4) resulted in increased susceptibility to cycloheximide and attenuated virulence in mice16. The calcium channel YVC1 is another important element that plays a critical role in response to stress and contributes to virulence in fungi. In C. albicans, the yvc1Δ/Δ mutant displayed the decreased ability of stress response, morphogenesis, and attenuated virulence, the Yvc1 was shown to have a role in hyphal polarized growth and re-orientation to host signals47. Calcium signaling plays a critical role in fungi’s response to environmental stimuli which includes calcium depletion, alkaline pH, and antifungal compounds. Such environmental stimuli activate plasma membrane-localized high-affinity calcium uptake system resulting in the activation of calcineurin which then triggers the activation of calcium-dependent responses such as cell wall integrity genes48,49,50. The presence of such strong mechanisms to go pound for pound in response to the host antagonizing colonization by phytopathogenic fungus shows how the pathogens are continuously evolving in the arms race.

Orthologues for D-arabinitol dehydrogenase 1, major facilitator superfamily multidrug transporter NAG4, and the calcium channel YVC1 were detected in the genome sequences of C. destructans. This provides a brief insight into how C. destructans can successfully colonize almond. Also detected was sucrose non-fermenting protein kinase 1, a gene that activates the expression of galactose oxidase, as well as that of several cell-wall degrading enzymes51. Plant cell walls provide constitutive barriers that plant pathogens must overcome before accessing cellular components52,53, thus activation of fungal cell wall degrading enzymes is crucial for pathogenesis. In Fusarium virgulifome the causal organism of sudden death syndrome (SDS) in soybean, disruption of the sucrose non-fermenting protein kinase 1 gene (FvSNF1) resulted in severely impaired ability to cause SDS51.

BZIP-type transcription factor MBZ1, a gene that plays a crucial role in cell wall integrity, adherence, and virulence, was also present in the orthologous genes shared by genome sequences of C. destructans. In Metarhizium robertsii, a deletion mutant of the MBZ1 resulted in defects in cell wall integrity, adhesion to hydrophobic surfaces, and infection of the host54. Another gene that plays a key role in cell wall integrity and host colonization which was present in the C. destructans genomes is ferric/cupric reductase transmembrane component 1. In C. albicans, disruption of CFL1 resulted in hypersensitivity to chemical and cell wall stresses moreover, the mutant displayed a decreased ability of adhesion and invasion of host epithelial cells, clearly indicating its role in pathogenesis55. The C2H2 finger domain transcription factor mtfA is also a well-described virulence factor. Its deletion in Aspegillus fumigatus resulted in decreased virulence, a decrease in the secondary metabolite gliotoxin, and attenuated protease activity56, a clear indication of its role in pathogenesis. Another transcription factor in the genome sequences of C. destructans, bZIP transcription factor hapX, has been shown to play a role in the expression of genes involved in reductive iron assimilation and siderophore-mediated iron uptake, a process that was shown to be essential for maximum virulence in A. fumigatus57.

When C. albifundus and C. fimbriata were added to the analysis, there was a total of 5,809 shared orthologous genes (Fig. 2B). Of the shared orthologous genes among C. albifundus, C. fimbriata, and C. destructans isolates, 30 had a “pathogenesis” GO annotation, and they were the same as those described for C. destructans. C. albifundus had 30 singleton/unique proteins (Fig. 2B) of which none of them had a “pathogenesis” GO annotation. In contrast, C. fimbriata had 102 singletons/unique proteins (Fig. 2B), and only two had a “pathogenesis” GO annotation, both were 1-phosphatidylinositol phosphodiesterase. 495 proteins were unique to at least 2 of the C. destructans isolates. Annotations of the proteins identified proteins 1-phosphatidylinositol phosphodiesterase, and patatin group M-2 as proteins that might play a crucial role in the adaptation of C. destructans to almond. Querying the PHI-base produced top hits with reduced virulence phenotypes, indicating their possible role in the infection of almond. These protein sequences are provided, in Supplementary Data 1, and their expression profiles during the colonization of almond tissues may shed more light on their roles. Though the numbers of genes with GO annotation that included “pathogenesis” did not drastically differ among Ceratocystis species, it is likely that the unique association of C. destructans with almond is the result of a genetic adaptation. Even in species with high genome similarity, host specificity might be related to variations in a single locus or clusters of closely linked loci58. They were a few more variations observed when C. destructans genomes were compared to C. albifundus and C. fimbriata, a possible explanation for C. destructans adaptation to almonds. Most notably, 15 predicted putative effector proteins from C. destructans formed monophyletic clusters indicating they were distinct from those in C. albifundus and C. fimbriata (Supplementary Fig. 2). All of them had a hit in the PHI-database (Supplementary data 1) and could be linked with the specific adaptation of C. destructans to almond. Using BLASTP on the NCBI database, they were annotated as hypothetical proteins (12), no hits (2), and putative alpha-1,2-galactosyltransferase (Supplementary data 1). Moreover, CAZymes glycoside hydrolases (GH32, GH5) and an unknown secondary metabolite gene cluster showed some variations among C. fimbriata, C. albifundus, and C. destructans isolates. (Fig. 4, Table 2). In addition, the overall nucleotide identity was also distinct among the Ceratocystis species used in this study. The C. destructans isolates shared a minimum of 99% average nucleotide identity. In contrast, they shared 89–92% average nucleotide identity with C. fimbriata and C. albifundus (Supplementary Fig. 1). Moreover, slight variations could also be noticed in the total number of proteomes predicted as putative effectors, cytochrome P450s, and transporters (Table 3). Cytochrome P450s have been shown to be pivotal in genotypic and phenotypic evolution, understanding the ecological specialization of fungal species59,60.

Table 3 Number of protein-coding genes annotated in the highlighted functional categories.
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The distribution of the functional categories of the predicted clusters of orthologous genes is shown in Fig. 3 and Supplementary Data 1. In the cellular process and signaling category, the most common assignments were post-translational modification, protein turnover, and chaperones (6.674%), signal transduction mechanisms (5.52%), and cell cycle control, cell division, chromosome partitioning (2.91%) (Fig. 3 and Supplementary Data 1). In the information storage and processing category, largest classes were translation, ribosomal structure, and biogenesis (10.27%), replication recombination and repair (5.49%), and transcription (2.42%) (Fig. 3 and Supplementary Data 1). In the metabolism category, amino acid transport and metabolism (7%), carbohydrate transport and metabolism (6.92%), energy production and conversion (5.8%), and lipid transport and metabolism (5%) were some of the most common categories (Fig. 3 and Supplementary Data 1).

Figure 3
figure 3

Distribution of the characterized functional categories of the clusters of orthologous genes (COG). The figure shows isolate C. destructans (KARE490) and represents the general distribution of the functional categories in the other isolates. Specific information for the individual isolates is provided in Supplementary Data 1. The X-axis represent the % contribution to the total predicted proteome of the genome.

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Carbohydrate active enzymes

The C. destructans genomes had 83.4 ± 1.8 secreted CAZymes, in contrast, C. albifundus and C. fimbriata had 74 and 96, respectively (Table 2). The total number of secreted CAZymes in C. destructans was comparable to those of C. manginecans and C. fimbriata reported by Fourie et al.32. In that study, C. fimbriata was predicted to have 83 secreted CAZymes while in our current study, a total of 96 secreted CAZymes was predicted. Such slight variation can be attributed to several factors such as slight differences in bioinformatics analysis, as well as advancement in databases. But overall, this indicates that our analysis is comparable to other studies. In the Ceratocystis genomes, the glycoside hydrolases (GHs) superfamily contributed to more than 50% of the secreted CAZymes (Supplementary Data 2). The top two GHs of all the analyzed Ceratocystis genomes consisted of GH16, and GH18 (Supplementary Fig. 3), which are involved in the degradation of cellulose and hemicellulose61. Figure 4 shows the distribution of the secreted CAZyme families and how they correlated among the genomes. Among the most abundant CAZymes, the Auxillary Activities (AA) family was of interest due to P. dulcis being a woody plant, thus its cell wall includes lignin; and AAs decompose lignin. The most abundant were AA1, AA7, and AA9 (Supplementary Fig. 3). The AA1s are multicopper oxidases, that catalyze the one-electron oxidation of phenolics, aromatic amines, and other electron-rich substrates via the reduction of oxygen to water62. They are involved in a diverse range of functions which includes lignin degradation, and offensive and defensive fungal interaction strategies63,64. AA7s consist of gluco-oligosacharide oxidases that generate hydrogen peroxide which is used by peroxidases during the decomposition of lignin65. The AA9 family consists of lytic polysaccharide monooxygenases, which degrade cellulose thus rendering it accessible to other CAZymes66. The distribution of the other CAZymes is shown in Fig. 4 and Supplementary Data 2. All these works synergistically to optimize the fitness of the phytopathogens.

Figure 4
figure 4

Distribution of secreted CAZymes families among C. destructans, C. fimbriata, and C. albifundus.

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The plant cell wall is composed of carbohydrates such as pectin, cellulose, and hemicellulose, structural proteins, as well as aromatic compounds67,68. The structures that form the plant cell wall are part of a constitutive barrier that plant pathogens must overcome before accessing cellular components52,53. Carbohydrate-active enzymes (CAZymes) consist of proteins with the capability to degrade, modify, or create glycosidic bonds61. They are divided into four main classes: glycoside hydrolases (GH), polysaccharide lyases (PLs), Carbohydrate esterases (CEs), and glycotransferases61. Owing to their involvement in plant cell wall degradation during the colonization of plants by phytopathogens, CAZymes are an important virulence factor of phytopathogens8,9. Since fungal effector proteins are generally secreted, CAZymes annotations are always combined with signal peptide prediction to ascertain their probable roles as virulence factors. Not all members of the CAZymes are secreted proteins, as a result, studies aiming to identify effector proteins have always combined CAZymes annotations with the prediction of secreted proteins12,69. The Ceratocystis genomes used in this study had the secreted CAZymes which covered all the classes (Fig. 4, Supplementary Data 2), an indication they are equipped to bridge the constitutive barrier presented by the host plant.

Secondary metabolite gene cluster

Fungal secondary metabolites play a key role in mediating ecological interactions, communication, nutrient acquisition, stress protection, and chemical warfare70. In phytopathogens like Stemphylium lycopersici, the release of some secondary metabolites resulted in necrosis of plant tissue suggesting their probable role as determinants of pathogenicity and virulence19,71,72. Genes encoding secondary metabolites are usually clustered together on chromosomes. This observation led to the emergence of genome mining as a technology to identify biosynthetic gene clusters (BGCs) which encode secondary metabolites73. In the current study, we took advantage of the advances in both DNA sequencing and secondary metabolites genome mining tools, including antiSMASH22 and the Minimum Information about the Biosynthetic Gene Cluster (MIBiG)74 to explore the secondary metabolites encoded by the genomes of C. destructans. Analysis with antiSMASH revealed the presence of seven secondary metabolites gene clusters in C. destructans isolates KARE490, and KARE1428, and eight secondary metabolites gene clusters in isolates KARE1624 and KARE1622 (Table 2). Isolate KARE979 had nine secondary metabolite gene clusters whereas C. albifundus and C. fimbriata had a total of ten secondary metabolite gene clusters (Table 2). The categories of the biosynthesis gene clusters included type I polyketide synthase (T1PKS), type III polyketide synthase (T3PKS), non-ribosomal peptide synthase (NRPS), non-ribosomal peptide synthase-like (NRPS-like), and ribosomally synthesized and post-translationally modified peptides (RiPPs) (Table 2).

Table 2 provides information on the likely compound linked to each BGC based on the MIBiG. Compounds linked to the C. destructans isolates BGCs were similar except that the isolates with fewer BGCs lacked one or two compounds in comparison to isolates with more BGCs (Table 2). The slight variations in the BGCs in the C. destructans isolates could be due to variations in the quality of the assemblies, which is indicated by the different assembly quality metrics (Table 1). Misassembles, particularly from short-read data, can lead to skipping or duplication of NRPS or PKS. Moreover, it can lead to swaps that obscure the true order of genes or protein domains affecting the analysis of BGCs70. There were some predicted compounds linked to BGCs unique to C. fimbriata, and C. albifundus (Table 2). This is probably because these are completely different species from C. destructans, with different genomic makeup (Fig. 5), as well as different niches. Based on the MIBiG, all the Ceratocystis genomes had an NRPS BGCs which encode the compound dimethylcoprogen (Table 2). Dimethylcoprogen is an extracellular siderophore encoded by NRPS which is a conserved virulence determinant of plant pathogenic ascomycetes69. Deletion of NPS6, which encodes NRPS responsible for dimethylcoprogen resulted in reduced virulence and hypersensitivity to hydrogen peroxide of the phytopathogens Cochlibolus miyabeanus, F. graminearium, and Alternaria brassicola75.

Figure 5
figure 5

Dot plot graph showing syntenic blocks between Ceratocystis genomes. Syntenic blocks between C. destructans (KARE1622) and C. fimbriata (a). Syntenic blocks between C. destructans isolates KARE1622 and KARE1624 (b).

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Cytochrome P450s, transporters, and putative effectors

The C. destructans isolates had between 37 and 46 detected cytochrome P450s in their genomes (Table 3). The cytochrome P450s consisted of the P450 class names: E-class P450, CYP2D; P450, CYP52; and E-class P450, group IV which were identified to be 1, 6, and 1, respectively in all the C. destructans isolates (Supplementary Data 1). In addition, the classes cytochrome P450, P450, group I, and pisatin demethylase-like were also identified but with different numbers among the isolates (Supplementary Data 1). Pisatin demethylase, a cytochrome P450, is involved in demethylation of pisatin (phytoalexin) into a non-toxic product76. This demethylation of phytoalexin by fungi has been established to be important in pathogenicity, with some exceptions there is a correlation between phytoalexins tolerance and host range77. C. destructans, C. fimbriata, and C. albifundus have different hosts, suggesting that their pisatin demethylase might differ. Indeed, cytochrome P450 families (CYP5078A4b and CYP620F1b) were unique to C. destructans isolates. In contrast C. albifundus, and C. fimbriata had CYP5150A2, and CYP5037B3, respectively, as unique families belonging to pisatin demethylase class (Supplementary Data 1). Supplementary Data 1 also provides details on all the cytochromes which includes putative names, and best hit in the Nelson’s database.

Proteome annotated with the NCBI database identified 241 and 247 transporters for C. albifundus and C fimbriata, respectively (Table 3). These were verified by querying the transporter classification database (TCDB) using default settings. In contrast, C. destructans isolates had between 248 and 254 transporters, respectively. The identified transporters included proteins which had a pathogenesis GO annotations (Supplementary Data1), of which we already described some of their roles. We also managed to identify 57.4 ± 2.1 putative effector proteins in the C. destructans isolates (Table 3). These include some of the pathogenesis features we already describe such as 1-phosphatidylinositol phosphodiesterase, as well as a number of CAZymes. Supplementary Data 1 provides the sequences for proteins predicted to be effectors, including their localization. Total predicted effectors for C. albifundus (62) were not significantly higher than those of C. destructans (Table 3). In contrast, C. fimbriata had almost 40% more predicted effectors (Table 3). The results of our predicted effectors were comparable to that of Fourie et al.32. Moreover, we identified 15 putative effectors unique to C. destructans (based on phylogenetic analysis; Supplementary Fig. 2). Their annotation and hits in the PHI-database are provided in Supplementary data 1. These effectors might be crucial for the adaptation of C. destructans to almonds.

Conclusion

In this work, we presented the first high quality draft genome sequences of five C. destructans isolates isolated from Ceratocystis canker-infested almond trees. We incorporated a number of bioinformatics tools and predicted protein-coding genes of the assembled genomes. From the predicted proteomes, we annotated for CAZymes, cytochrome P450S, transporters, and secondary metabolite gene clusters (BGCs), all of which are important virulence factors for canker pathogens. Moreover, we identified 57.4 ± 2.1 putative effector proteins; effector proteins are known to play critical roles like evasion of the host immune system, facilitating the success of phytopathogens. We also performed Gene Ontology (GO) annotations and provided detailed descriptions of the genes with a pathogenesis annotation. GO leverages domain-specific ontologies to give comprehensive functional information on gene products78. The primary gene predictions and annotations from the current study will serve as a valuable resource for in planta expression profiling, molecular information required for targeted knock-out mutations, as well as genetic diversity studies. There is still a need to experimentally validate the predicted genes and transcriptomic experiments could add depth to the resource we presented. In addition to validating the existence of predicted genes, transcriptomics can also help improve annotations and the identification of the intro-exon structures of predicted genes. With C. destructans being likely endemic to California, further sequencing of other C. destructans isolates could help to estimate the pan-genome and genomic heterogeneity/homogeneity of this species. Such information will be critical to establish structural variations in the population of this pathogen and determine how they might correlate with host adaptations. Though the presented genomes are of good quality (based on BUSCO, genome size, and total predicted proteins), combining long reads and short read sequencing technologies will likely reduce the number of contigs, which is crucial for better annotations. More work still needs to be done; however, the present study provides the foundation for future investigations on this important almond canker pathogen.

Methods

Biological material, genomic DNA extraction, and sequencing

Ceratocystis destructans isolates KARE979, KARE1622, KARE1624, KARE1428, and KARE490 were isolated from almond trees exhibiting typical Ceratocystis canker symptoms4,5. A detailed description of the isolation procedure, orchard location, and year of isolation was provided by Holland et al.5. For whole genome sequencing of the isolates, genomic DNA was isolated from hyphal tip purified isolates grown in 250-ml Erlenmeyer flasks containing 100 ml of potato dextrose broth (PDB; Difco Laboratories) and incubated for 7 days at 25 °C and 150 rpm. Approximately 100 mg of mycelia were vacuum filtered from the PDB cultures and collected into 2 ml micro-centrifuge tubes and frozen in liquid nitrogen; mycelia were ground while still frozen. DNA extraction was done with a modification of the Cetyltrimethylammonium Bromide (CTAB) protocol according to Morales-Cruz et al.12. The quality and the integrity of the isolated DNA were measured using the NanoDrop spectrophotometry (ND-100, NanoDrop Technologies, Inc, Wilmington, DE, USA), and agarose gel electrophoresis, respectively. In preparation for sequencing, DNA was fragmented by sonication using a Bioruptor (NGS; Diogenade, Liège, Belgium). DNA libraries were prepared using the KAPA library preparation kit (catalog number KK8201, KAPA Biosystem, Woburn, MA, USA), using 1 µg of genomic DNA as input. Insert size selection was carried out using the Life E-gel SizeSelect (Life Technologies, Carlsbad, CA, USA), and the final library quality and quantity were analyzed using a Bioanalyzer 2100 (Agilent Technologies, CA, USA). Sequencing was carried out at the DNA Technologies core of the University of California, Davis, using a HiSeq2500 machine, generating paired end reads of 150 bp.

Assembly, and gene prediction

Quality trimming (Q > 20) and adapter removal were carried out using Trimmomatic v. 0.36 with the following parameters: ILLUMINACLIP:TruSeq3-PA.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:10:20 MINLEN:9079. Assembly of the filtered reads was performed using SPAdes80 with variable k-mer size. K-mer size of 23 produced the best assembly contiguity (N50) and completeness (BUSCO) and was used for all assemblies. Quality assessments of the assembled genomes were performed using Quality Assessment Tool for Assembled Genomes (QUAST)81. The Benchmarking Universal Single-Copy Orthologs (BUSCO) software33 was used to assess the completeness of the assembled genomes, benchmarking with fungi_odb10. Before gene prediction, repeat masking was performed using RepeatMasker 4.1.1(RepeatMasker Home Page). Genes were predicted using a combination of Augustus82, and GeneMark-ES trained on the genome of Fusarium graminearium, following the GenSAS eukaryotic annotation pipeline83. For consensus gene predictions, EvidenceModeler (EVM)84 was used to compile a consensus gene set from the predictions of Augustus and Genemark-ES.

Gene content comparison, and annotations

For comparison of the predicted proteins across isolates, the Orthovenn2 server for whole-genome comparison and annotation of orthologous clusters85 was used. The Orthovenn2 output also includes Gene Ontology (GO) annotations. From the GO annotations, we searched for the key word “pathogenesis” GO term/annotation. The GO (Gene Ontology Resource) is a comprehensive source of functional information on gene products that leverages domain-specific ontologies76. Proteins with a pathogenesis GO annotation were queried against the PHI-database86. The genome-based distance matrix calculator (http://enve-omics.ce.gatech.edu/g-matrix/index) was used to calculate average nucleotide identities. For comparative purposes, assembled genomes of Ceratocystis albifundus (GCA_002742255.2)29, and Ceratocystis fimbriata (GCA_000389695)28, were included in the analysis. The downloaded assembled genomes of C. albifundus, and C. fimbriata were subjected to similar pipelines with the same parameters as C. destructans isolates to standardize the comparisons. For synteny, D-GENIES87 was used to visualize the dot plots using Minimap288 with the default settings, the final plots were filtered for strong precision.

Secondary metabolites gene clusters were predicted using the antiSMASH fungal version22. Carbohydrate active enzymes (CAZymes) were predicted using the dbCAN2 a meta-server HMMer-based classification system89 using default settings. The fungal cytochrome P450 database (FCPD)23 was used to annotate cytochrome P450s from the proteome using the Phylum Ascomycota P450 sequences database (BLASTP e-value ≤ 10; ≥ 44% amino acid identity). Proteins were also annotated using BLAST + 90 and the NCBI non-redundant fungi protein database. From the annotated proteins, keyword search “transporter” was used to identify proteins annotated as transporters. To identify putative effectors from the proteome, SECRETOOL’s classical secretion pipeline was used to identify the total secretome20.The classical secretion pipeline includes processing the data with TargetP, SignalP, PredGPI, and WoLFSPORT. From the secreted protein pool, EffectorP25 was used for the identification of putative effector proteins. For the identified effector proteins, all the protein sequences are provided in Supplementary Data 1.