Design of a diagnostic system based on molecular markers derived from the ascomycetes pan-genome analysis: The case of Fusarium dieback disease

About the Authors:

Mirna Vázquez-Rosas-Landa

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliation: Red de Estudios Moleculares Avanzados (REMAv), Instituto de Ecología A.C. (INECOL), Xalapa, Veracruz, México

Diana Sánchez-Rangel

Roles Formal analysis, Funding acquisition, Methodology, Validation

Affiliations Red de Estudios Moleculares Avanzados (REMAv), Instituto de Ecología A.C. (INECOL), Xalapa, Veracruz, México, Catedrático CONACYT en el INECOL, Xalapa, Veracruz, México

Eric E. Hernández-Domínguez

Roles Formal analysis, Methodology, Validation

Affiliations Red de Estudios Moleculares Avanzados (REMAv), Instituto de Ecología A.C. (INECOL), Xalapa, Veracruz, México, Catedrático CONACYT en el INECOL, Xalapa, Veracruz, México

Claudia-Anahí Pérez-Torres

Roles Formal analysis, Methodology, Validation

Affiliations Red de Estudios Moleculares Avanzados (REMAv), Instituto de Ecología A.C. (INECOL), Xalapa, Veracruz, México, Catedrático CONACYT en el INECOL, Xalapa, Veracruz, México

Abel López-Buenfil

Roles Formal analysis

Affiliation: Colegio de Postgraduados (COLPOS), Montecillo, Mexico

Clemente de Jesús García-Ávila

Roles Formal analysis

Affiliation: Servicio Nacional de Sanidad, Inocuidad y Calidad Agroalimentaria (SENASICA), Centro Nacional de Referencia Fitosanitaria (CNRF), Tecámac, Estado de México, México

Edgar-David Carrillo-Hernández

Roles Methodology, Validation

Affiliation: Red de Estudios Moleculares Avanzados (REMAv), Instituto de Ecología A.C. (INECOL), Xalapa, Veracruz, México

Cynthia-Coccet Castañeda-Casasola

Roles Methodology, Validation

Affiliation: Servicio Nacional de Sanidad, Inocuidad y Calidad Agroalimentaria (SENASICA), Centro Nacional de Referencia Fitosanitaria (CNRF), Tecámac, Estado de México, México

Benjamín Rodríguez-Haas

Roles Methodology

Affiliation: Red de Estudios Moleculares Avanzados (REMAv), Instituto de Ecología A.C. (INECOL), Xalapa, Veracruz, México

Josué Pérez-Lira

Roles Methodology

Affiliation: Red de Estudios Moleculares Avanzados (REMAv), Instituto de Ecología A.C. (INECOL), Xalapa, Veracruz, México

Emanuel Villafán

Roles Methodology

Affiliation: Red de Estudios Moleculares Avanzados (REMAv), Instituto de Ecología A.C. (INECOL), Xalapa, Veracruz, México

Alexandro Alonso-Sánchez

Roles Methodology

Affiliation: Red de Estudios Moleculares Avanzados (REMAv), Instituto de Ecología A.C. (INECOL), Xalapa, Veracruz, México

Enrique Ibarra-Laclette

Roles Conceptualization, Formal analysis, Investigation, Methodology, Resources, Validation, Writing – review & editing

* E-mail: [email protected]

Affiliation: Red de Estudios Moleculares Avanzados (REMAv), Instituto de Ecología A.C. (INECOL), Xalapa, Veracruz, México

ORCID logo https://orcid.org/0000-0003-1441-1938

Introduction

Phytosanitary problems are considered a primary cause behind economic crop losses around the world [1]. Therefore, accurate identification of the causal agent is a critical factor for implementing actions against a specific disease [2]. The introduction of new species or the eradication of native species within an ecosystem contributes also to phytosanitary problems [3]. One clear example is the accidental introduction to the United States of two species of ambrosia beetles (Coleoptera: Curculionidae: Scolytinae) native to Asia: the polyphagous shot hole borer (PSHB) and the Kuroshio shot hole borer (KSHB) [4]. Both plagues have negatively impacted avocado (Persea americana Mill) production [5] and many other arboreal plant species [5–7]. Specifically, the KSHB complex has devastated large areas of riparian forest in the valley of Tijuana [8], and also was first reported in Mexico in 2015 [9] and currently, in this country is considered a quarantine pest.

The ambrosia beetles establish mutualistic relationships with various species of fungi mainly for nutritional purposes. To maintain these symbioses, beetles dig galleries and tunnels into the vascular system (especially the xylem) of host plants, where they farm the fungi to guarantee an available food source throughout their life cycle [10]. In some cases, the symbiotic fungi are the causal agents of some diseases [11, 12]. That is the case of the ambrosia beetles involved in PSHB and KSBH complexes that vectored phytopathogenic fungi of Fusarium genus (F. euwallaceae and F. kuroshium, respectively) causing Fusarium dieback disease [11, 12]. Since Mexico is the most important producer of avocado [13] and possesses forest arboreal species recognized as potential hosts [7], it is a priority to develop strategies that allow us to prevent, diagnose and eradicate this disease.

Currently, micro- and macro-conidia features, the presence or absence of chlamydospores, colony appearance, and growth rates in different culture media are used to identify Fusarium species [14]. In consequence, the diagnosis is time-consuming and requires experts in the field. On the other hand, molecular diagnosis is a rapid, sensitive, specific and efficient alternative [15, 16]; including polymerase chain reaction (PCR) and sequencing techniques. In both cases, a small quantity of DNA is required during the assay. There are several PCR based methods to identify Fusarium species [17–22], including methods based on real-time PCR (qPCR) [23] moreover there are some recent approaches based on this technique for the detection of ambrosia fungi species. For example, Carrillo et al. [23] developed a multiplex qPCR assay using hydrolysis probes for the detection of the b-tubulin gene.

A critical step of PCR based methods is the selection of a target gene and the development of specific primers and probes [24]. Most of the targets used for plant pathogenic fungi identification derive from housekeeping genes; which do not reflect sufficient sequence variation for the discrimination between species [25, 26], or derive from genes related to the pathogenesis of the fungi; in that case the specificity of these types of markers depends on the evolutionary history of the gene, which could be common to other pathogens, either by horizontal gene transfer [27], common ancestry, or convergent evolution [28, 29], and therefore non-specific.

Also, techniques based on genotyping have also been employed (i.e., restriction fragment length polymorphism (RFLP) or amplified fragment length polymorphism (AFLP)). These techniques differentiate strains, races, or varieties within a given species, and have been used to generate markers that allow, for example, the identification of groups within the species F. oxysporum [30]. However, given the anonymity of the markers concerning the genome, numerous strains or isolates are necessary to validate the stability of the markers [31].

Distinct strategies based on comparative genomics are used to identify and design specific targets. Comparative genomics studies revealed a wide diversity of genetic content throughout different biological groups [32–34]. This diversity includes genes shared by all the members of a specific biological group (i.e., species, genus, family), frequently called ‘core’, as well as, genes that are not shared by all the members of the group, which are named ‘accessories’, and are unique genes, present in specific lineages or clades [35]. The term pan-genome encompasses all genes from both groups [36]. These concepts have been applied to find taxa-specific targets in order to develop tools for the control of the pathogens as the vaccines [34]. This method has been used to generate tags for F. circinatum [37], F. oxysporum [2, 38], and even strains of F. euwallaceae sp. Nov. associated with PSHB Euwallacea sp. [39]. Despite comparative genomics identifies specific targets, these markers can amplify genome non-coding areas that accumulate variation because they are subjected to less selective pressure. Therefore, this is a potential problem regarding marker specificity.

In this study, we performed a pan-genome analysis of ascomycetes associated with ambrosia beetles and other phytopathogens to find gene markers in order to design primers for the accurate diagnosis of Fusarium spp. by PCR. Unlike previous research, we focus on the use of coding regions. We probed the specificity of the primers using three different DNA sources, i) a pure culture of different phytopathogenic ascomycetes, ii) stems of Persea americana cv. Hass artificially infected by Fusarium kuroshium and iii) field samples from KSHB infested trees. We demonstrate the specificity of the markers, and therefore, our results bring to light the possibility of using this analysis method to develop an accurate diagnostic system for other types of plant pathogens.

Materials and methods

1. Bioinformatic analyses

1.1. Collection of data.

Sixty-three proteomes of phytopathogenic and non-pathogenic ascomycete fungi from public databases were used including the species of interest (F. kuroshium). Some of them also associated with ambrosia beetles (Table 1). Forty-eight proteomes from the JGI database (S1 Table) [40, 41]; contained gene models without isoforms. For the rest of the species, including the recently reported genome of F. kuroshium [42], we carried out gene models prediction. Additional information related to strain name isolation environment and their host was also included in S1 Table.

[Figure omitted. See PDF.]

Table 1. Species considered for the ascomycete pan-genome analysis.

https://doi.org/10.1371/journal.pone.0246079.t001

1.2. Prediction of gene models.

The prediction of the gene models followed these steps: first, transcriptomic datasets were assembled in order to generate transcriptional evidence; then, using the AUGUSTUS software, gene models were predicted on the genome sequence by integrating ab initio and evidence-based gene finding approaches [43, 44]. Finally, the optimization/refinement of the gene models were done with the MAKER annotation pipeline [45].

The SRA (Sequence Read Archive) database of the NCBI (National Center for Biotechnology Information) was consulted, and transcriptomic data available for 12 of the 15 species of interest were downloaded (S1 Table). Prior to the assembly, the available transcriptomic sequences of the species of interest were processed by a Python script (https://github.com/Czh3/NGSTools/blob/master/qualityControl.py) to discard low-quality reads. For the study purposes, we use the parameters -q 30 (the minimum quality value allowed from in Phred format), -p 90 (minimum percentage of bases in the sequence of -q quality), and -a 30 (Phred format estimated average quality limit throughout the sequence). When overlapping regions were detected (at least 25 overlapped bases in initial-terminal portions of paired-end reads), we used SeqPrep software (https://github.com/jstjohn/SeqPrep) to obtain a single, more extended sequence. We eliminate orphan reads (those in which one of the paired sequences failed to meet the established parameters), in order to only keep paired sequences. Finally, the sequences were assembled with Trinity software [46].

AUGUSTUS software [43, 44] was used to process the data and generate the first gene models version. Then, the MAKER annotation pipeline [45] was used to improve the gene models. We masked the repetitive DNA regions of the genomes by using RepeatMasker software (http://www.repeatmasker.org). The entries for MAKER included the masked genomes; all of the resulting contigs from the assembly of transcriptomic data, the gene models predicted by AUGUSTUS, as well as a protein database containing complete proteomes of a total of 43 ascomycete fungi (S2 Table). We used the generated gene models in subsequent analyses.

1.3. Prediction of orthologs and pan-genomic analysis.

The identification of orthologous and paralogous gene groups (orthogroups) was performed using the GET_HOMOLOGUES [47, 48] pipeline, a software that uses reciprocal/bi-directional BLAST analysis to identify sequences maintaining similarities across different taxa above a certain threshold (in this case, 75% of identity and coverage); after that, proteins with similarities were grouped by using OrthoMCL software [49]. Groups were created using normalized scores based on an algorithm that uses Markov chain models, which allowed for the identification of orthologous and paralogous putative genes. Finally, using the Perl script (parse_pangenome_matrix.pl) included with the pipeline, we identified genes present only in Fusarium genera, as well as the specific genes for each of the Fusarium species.

1.4. Primer design and in silico experiments.

We designed primers for diagnosis based on both, species and genera-specific genes. Regarding the primer design for species-specific genes, the proteins tagged as an orphan after the identification of orthologous groups were compared against a database including a total of 1,953,116 ascomycete fungi proteins available from GeneBank using the BLASTp algorithm. All proteins which showed homology with proteins of unknown function (hypothetical or predicted) were discarded from further analyses to avoid bias/errors associated with the computational tools used. Proteins showing an identity equal to or greater than 75% with proteins of other species were also discarded basically because we consider that homologs sequences with an identity greater than this defined threshold, could present regions (motifs) that at nucleotide level have high similarity and as consequence, with a greater probability of not being truly specie-specifics. The remaining proteins were considered as a list of possible targets for primer design. For that purpose, the nucleotide sequences corresponding to predicted gene models were extracted from the genome [complete gene structure, including both coding regions (exons) and non-coding regions (introns)]. Primers were designed using Primer3 software [50, 51]. Optimal size required for primer design was 20 nucleotides (at an alignment temperature close to 60°C); the expected range of the amplicon or expected product was set between 400 and 600 nucleotides. The complete structure of putative target genes was used considering that diagnosis employs mainly DNA as template and an estimate of the PCR amplicon length is required.

For the design of the genera-specific primers, the strategy consisted of analyzing the orthologous groups including only proteins of Fusarium species. First, the candidate proteins were compared against the ascomycete fungi protein database available on GeneBank and homologs identified as hypothetical or predicted proteins were discarded from further analyses. After this, using Kalign [52], we aligned the proteins contained into the remaining orthogroups and only in those cases that contained proteins, represented the proteins from all available species of the genus of interest. The protein sequences were translated into nucleotide sequences using the backtranseq application included in the EMBOSS [53] software package, to finally be realigned using ClustalW [54] based on specific codon usage. Gblocks [55] was utilized to extract highly conserved regions based on this alignment, and a consensus sequence was generated using the em_cons application included in EMBOSS [53]. Genera-specific primers were designed in these consensus regions using Primer3 [50]; optimal primer length was 20 nucleotides, and the expected product-length range was set between 200 and 1,000 nucleotides.

Finally, we performed an in-silico experiment to test specificity. Briefly, the designed primers were tested using ThermonucleotideBLAST [56], which performs a DNA fragment search in a database; however, as opposed to the BLAST [57] algorithm, ThermonucleotideBLAST uses additional alignment parameters based on biochemical variables present in PCR reactions, especially free energy and alignment temperature. A database containing all ascomycete genomes available in GenBank was used as a source of target sequences to perform in-silico amplification. We selected primers lacking mismatches regarding the species or genera we were expecting for as well as those whose estimated alignment temperatures were similar for both forward and reverse primers.

2. Experimental validation

2.1. Biological material.

Different phytopathogenic Fusarium strains were used for the experimental validation of the designed primers. The Fusarium verticillioides (MY3) strain was provided by Dr Javier Plasencia, School of Chemistry, National Autonomous University of Mexico (UNAM) [58]. The Fusarium oxysporum (CB-36) strain was provided by Dr Gloria Carrión, Biocontrol Laboratory, Institute of Ecology A.C. (INECOL). Fusarium kuroshium (strain HFEW-16IV-019) was provided by the Mycology Department of the National Reference Center (CNRF). The Fusarium sp. associated with Xylosandrus morigerus INECOL-BM-04 strain and other phytopathogenic fungi such as Botrytis cinerea and Neofusicoccum parvum (isolated from Liquidambar styraciflua), Fusarium tricinctum and Alternaria alternata (isolated from Persea schiedeana and Nectandra salicifolia, respectively) were provide by Dr. Diana Sánchez-Rangel, Phytopathology Laboratory of the Molecular Studies Network (REMAv) at INECOL. All designed primers were tested using DNA isolated from the different fungal species mentioned above as a template; these fungi were grown under controlled conditions, using papa dextrose agar (PDA) medium.

With the purpose of validating the primers designed as a diagnostic tool of Fusarium Diseases (FD), first, the primers were probed using DNA isolated from artificially infected avocado stalks (Persea americana cv Hass) with F. kuroshium HFEW-16-IV-019 strain. Briefly, stalks of approximately 30–40 cm length and 1 cm diameter were collected from one-year-old healthy avocado trees which were grown in pots and then acclimated for 4 to 6 months in a greenhouse. In order to mimic the ambrosia beetle, the stalks were cut into fragments of 3.5–4 cm length and were drilled into the center with a 1/16” Dremel®. Then, the stalk-segments were placed into humid chambers and were inoculated into the drilled injury with 40 μL of conidial suspension (1x108 conidia/mL). After 14 days (at 27°C and 16 h light/8 h dark), DNA isolation was carried out.

In addition, tissue from some branches collected from visibly symptomatic trees in the field was used. In Mexico and USA, the highest incidence of affected trees by KSHB corresponds to species such as coral trees and dwarf coral trees (Erythrina corallodendron and Erythrina humeana), California sycamore (Platanus racemosa), coast live oak tree (Quercus agrifolia), and some different species of willow trees (Salix spp.). All these species are recognized as suitable reproductive host trees of KSHB and susceptible to Fusarium dieback (https://ucanr.edu/sites/eskalenlab/?file=index.html). Therefore, with the help of Mexican phytosanitary authorities, was performed a visual examination in urban landscapes and natural, agricultural, and riparian areas in order to identify some trees of the species mentioned above with symptoms of the Fusarium dieback disease and/or visible damage caused by KSBH complex. Interestingly, after our search, only symptomatic/damaged trees from Erythrina corallodendron were found in one of the evaluated areas (S3 Table). Collected samples were moved and processed at the CNRF under biosafety conditions.

2.2. DNA extraction.

Fungal spores were preserved at -80°C in 10% glycerol. These were inoculated in PDA medium (potato, dextrose, agar) to promote germination and mycelial growth. After 15 days, a 0.7 cm2 plug from the outer zone of the colony was punched with a sterile well cutter and transferred to new PDA culture plates and incubated for one week in total darkness at 28°C using a CB 210 CO2 incubator (BINDER™). Mycelium was peeled off from the agar surface with a scalpel and DNA was extracted using a previously reported method [59]. DNA concentration was calculated using a NanoDrop 2000 (Thermo Scientific) spectrometer.

To obtain DNA from infected avocado stalks, the biological material was frozen in liquid nitrogen and then pulverized using a mortar and pestle. Approximately 10 mg of pulverized tissue was employed for DNA extraction using a Plant/Fungi DNA Isolation Kit (Norgene Biotek Corporation) following the manufacturer’s instructions. Obtained DNA was resuspended in 50 μl of deionized sterile water, and its quality was quantified and evaluated using a NanoDrop 2000 (Thermo Scientific) spectrometer. Finally, DNA was stored at -20°C.

In the case of field samples, tree branch segments were cut and lengthwise scraped to extract the inner part of the galleries drilled by the beetles and colonized by the fungi. DNA extraction was performed following a standard protocol [60]. Briefly, 200 mg of plant material was mixed with 1 mL of lysis buffer (Tris-HCl 100 mM pH 8.0, 3 M NaCl, 3% CTAB (cetyltrimethylammonium bromide), 20 mM EDTA and 50–80 mg of polyvinylpyrrolidone (PVP)). Following, a total of 500 μL of chloroform: isoamyl alcohol (24:1) was added and mixed by inversion followed by centrifuging 10 min at 3,500 rpm. The supernatant was mixed with one volume of isopropanol, incubated for 10 min at -20°C. Subsequently, the isolated genetic material was visualized in a 1.5% agarose gel electrophoresis. The molecular weight estimation was carried out using a 1 kb molecular weight marker (Thermo Scientific).

2.3. PCR amplification of ITS region and diagnostic markers.

DNA quality was confirmed amplifying the internal transcribed spacer (ITS) region, using the primers ITS1 (5’-TCCGTAGGTGAACCTGCGG-3’) and ITS4 (5’-TCCTCCGCTTATTGATATGC-3’) [61]. The amplification was performed with 10x-Mg PCR buffer, 10 μM of each primer, 50 mM of MgCl2, 10 mM of deoxyribonucleotides mix (Sigma), 50 ng of DNA, 2 U of Platinum™ Taq DNA Polymerase (Invitrogen) and sterile deionized water for a final total volume of 25 μl. PCR reactions were carried out in a thermal cycler (Applied Biosystems 9700) as follows: an initial denaturing step of 95°C for 3 min., followed by 35 cycles of continual changes beginning with denaturing for 35 seconds at 94°C followed by an alignment step of 45 seconds at 58°C and finally, elongated for 1 minute at 72°C, and an additional elongation step of 5 min. at 72°C after the 35 cycles. The amplicon was analyzed in an agarose gel at 1.5% and the PCR product was sequenced at the CNRF Molecular Biology laboratory.

Likewise, to validate the marker’s design, PCR reactions were performed with 10x-Mg PCR buffer, 10 μM of each designed primer per marker, 50 mM of MgCl2, 10 mM of deoxyribonucleotide mix (Sigma), 100 ng of DNA, 2 U of Platinum™ Taq DNA Polymerase (Invitrogen) and sterile deionized water for a final total volume of 50 μl. A thermal cycler (Eppendorf Mastercycler Nexus gradient) was used to carry out PCR reactions using the following thermal profile: an initial denaturation of 94°C for 2 min., followed by 35 cycles of continual changes beginning with a denaturation step of 35 seconds at 94°C followed by an alignment step of 35 seconds at 55°C, and an extension step of 1 minute at 72°C and an additional extension step of 5 min at 72°C after the 35 cycles. Finally, the amplicon was analyzed in agarose gel of 1.5% and it was visualized in a Gel Doc™ EZ imaging tool (BIO-RAD) and images were processed using Image Lab™ (BIO-RAD).

Results and discussions

1. A diagnosis system based on comparative genomics

1.1. Fusarium complex shows the largest number of coding genes among ascomycetes.

The number of coding genes (CDS) inferred for the ascomycete species analyzed ranges from 4,528 CDS in the case of Eremothecium sinecaudum to 20,925 in the case of F. oxysporum. Consistently with previously reported, 13,777 CDS were predicted into the F. kuroshium genome [42]. The predicted number of genes for F. kuroshium resulted comparable to genes reported for Nectria haematococca (also referred to by its asexual name F. solani), F. verticillioides, F. fujikuroi, F. graminearum, and F. pseudograminearum (15,707, 15,869, 14,813, 13,321, and 12,395, respectively) and slightly lower than F. redolens and F. oxysporum (17,051 and 20,925, respectively) (Table 1). Interestingly, the species belonging to Fusarium genus are those with the highest number of protein-coding genes and from the total of compared species on this study, their genome sizes are ranged between 36.44 Mb and 61.35 Mb [62–65]. Certain tendencies can be observed between genome size and genes content, a relevant aspect when the core genome and accessory or variable genomes of the species from Fusarium genus are defined.

It is also remarkable that the number of gene models predicted according to our analysis, was in the same range as previously reported by Vanderpool et al. [86]; especially for the species of Esteya, Ceratocystiopsis, Fragosphaeria, Grosmannia, Ambrosiella, and Raffaelea genus (Table 2).

[Figure omitted. See PDF.]

Table 2. Comparison of the number of gene models predicted in the present study and previous reports.

https://doi.org/10.1371/journal.pone.0246079.t002

1.2. The ascomycetes pan-genome analysis reveals target genes useful for the identification of Fusarium spp.

For the sixty-three proteomes analyzed (Table 1), we obtained a total of 685,096 proteins classified into 382,502 orthogroups or OrthoMCL-defined protein families. Clustering analysis of pan-genome showed that under the set parameters (see Methods section for more information), 2,631 orthogroups, contain unique proteins only from species of Fusarium genus (Fig 1). Regarding species-specific proteins, on average 3,941 orthogroups resulted like specific for each of the species included within the Fusarium complex (Fig 2). As expected, the number of orthogroups with shared proteins between some of the compared species is smaller, to the extent that the number of species included in the analysis is increased.

[Figure omitted. See PDF.]

Fig 1. Clustering analysis of ascomycete pan-genome.

Clustering based on gene content of the orthogroups inferred. The Fusarium complex is highlighted in purple among the number of exclusive genes.

https://doi.org/10.1371/journal.pone.0246079.g001

[Figure omitted. See PDF.]

Fig 2. Number of exclusive specie-specific orthologue groups for each member of the Fusarium complex.

Bar plot including the number of exclusive orthogroups for each Fusarium spp.

https://doi.org/10.1371/journal.pone.0246079.g002

Moreover, we identified a total of 65 proteins (or genes) belonging to ascomycetes core genome (considering all species used in this study). Core genes fulfill two essential conditions; all the species share them, and they are single-copy ortholog genes. We observe that the topology of the resolved phylogenetic tree (Fig 3) resembles in some clades (but not all of them) to the clustering based on the gene content of the orthogroups inferred (Fig 2). We explain this discrepancy by the differences in the analysis; on the one hand, the phylogeny of core genes shows the evolutionary relationships of species, while clustering analysis of pan-genome genes shows the similarities regarding the presence and absence of shared ortholog genes, which are inferred based on specific parameters.

[Figure omitted. See PDF.]

Fig 3. Core phylogeny of ascomycetes.

Phylogenetic reconstruction from the 65 core orthogroups.

https://doi.org/10.1371/journal.pone.0246079.g003

1.3. In-silico validation of a set of primers design for the diagnosis of Fusarium spp.

We performed primers design for both, specific species, and Fusarium genus. In the case of the species-specific dataset, we identified on average 136 (SD ± 108) target genes per species for which we performed the design of the primers. However, for each species on average, only 120 pairs of primers met the set criteria, i.e., lacked mismatches and the amplification temperatures required by both forward and reverse primers were in the same or equivalent range. Subsequently, based on a manual evaluation considering the alignment temperatures, percentages of guanine and cytosine, and a total lack of secondary structures formation such as hairpins, dimers, cross dimers or palindromes, we selected the primer sets for the experimental tests presented in Table 3.

[Figure omitted. See PDF.]

Table 3. Primers designed to be used as specific gene markers in the diagnostic system.

https://doi.org/10.1371/journal.pone.0246079.t003

2. Experimental validation of the diagnosis system

In order to probe the specificity of the primers designed, we employed DNA from three different sources: i) DNA from different ascomycetes fungi strains which were isolated from a pure culture, ii) DNA isolated from Persea americana cv Hass stalks artificially infected with F. kuroshium and iii) DNA isolated from plant tissue collected on the field from trees apparently infested by KSHB that showed FD symptoms.

2.1. The diagnosis system accurately identifies Fusarium spp.

Based on the criteria described before (see Methods for details) and after their in-silico validation, we selected three primer pairs as the main candidates to identify species of the Fusarium genus. These markers were tagged as FuSp01, FuSp02, and FuSp03 (Table 3). We tested the specificity of those primers with DNA obtained from strains of F. kuroshium, F. graminearum, F. verticillioides, F. oxysporum, F. tricinctum, F. solani, Alternaria alternata, Botrytis cinerea, and Neofusicoccum parvum. Prior to validating the use of the primers designed for the fusariosis diagnostic, we amplified the ITS region with universal primers in order to guarantee the quality of the DNA samples used as a template (Fig 4A). As expected, the primers designed for the marker FuSp02, amplified a single band of around 590 bp on all tested Fusarium species but not so in the other ascomycete fungi species included in this study (Fig 4B). As a positive control, we included a previously reported marker that successfully identifies Fusarium species, labelled in the present study as PC01 (from Positive Control 01, Table 3 and Fig 4B) [87]. Based on the obtained results (Fig 4), we conclude that FuSp02 marker shows a high level of Fusarium specificity comparable to PC01. With only one exception (the F. graminearum species), the primers designed for FuSp01 marker shown only the unique expected product for the species of Fusarium genus which were tested. Meanwhile, the results obtained with FuSp03 marker were inconsistent, obtaining the desired product in only few cases. We concluded that FuSp02 seems to be a suitable marker for diagnosis of fusariosis because amplified efficiently using DNA from only Fusarium species but not from DNA of other ascomycete fungi (Fig 4).

[Figure omitted. See PDF.]

Fig 4. PCR-based assays using FuSp02 and PC01 [87] as diagnostic markers of fusariosis.

Agarose (0.8%) gels electrophoresis which shows: (A) The amplified ITS region to validate the quality of the template DNA used and isolated from different species of ascomycetes fungi (F. kuroshium [Fk], F. graminearum [Fg], F. verticillioides [Fv], F. oxysporum [Fo], F. tricinctum [Ft], F. solani [Fs], A. alternata [Aa], B. cinereal [Bc], and Neofusicoccum parvum [Np]; lines 1–9, respectively), and (B) The PCR fragments (amplicons) obtained for FuSp02 (odd lanes) and PC01 (even lanes) markers, both of them highly-specific to identify species from Fusarium genus. In (B), lanes from F. tricinctum were not shown only for lack of space reasons in the electrophoresis gel.

https://doi.org/10.1371/journal.pone.0246079.g004

2.2. The primers designed to identify Fusarium kuroshium are highly efficient for the diagnosis of Fusarium dieback disease.

Fusarium genus comprises a wide range of phytopathogen species [90]; and, in this study, our main goal was to design specific primers that can be used to identify the F. kuroshium specie and to generate an efficient diagnosis system for Fusarium dieback disease. We obtained three sets of primers that we named as FuKu01, FuKu02, and FuKu03, and which can generate 600, 599, and 466 bp amplicons, respectively. These primers were tested using genomic DNA of six different Fusarium species (including the species of interest, F. kuroshium) and other three species of phytopathogenic ascomycete, A. alternata, B. cinerea, and N. parvum. As shown in Fig 5A, we obtained amplicons of the expected sizes only when F. kuroshium genomic DNA was used as a template (lanes 1, 2, and 3). Markers FuKu01 and FuKu02 generate amplicons of the expected size and are species-specific. In the case of FuKu01, this genetic marker generated a noticeably lower amount of product with respect to FuKu02. As expected, these two markers fail to generate any product when using genomic DNA from other Fusarium species even when they are closely related to the species of interest. For example, Fusarium sp. associated with X. morigerus (lanes 16–18), a strain which on this study was also isolated from an ambrosia beetle species. Regarding FuKu03, this genetic marker also amplified only in the case of F. graminearum besides the species of interest (F. kuroshium). We consider that even when the amplicon obtained for F. graminearum is slightly longest with respect to the amplicon obtained for F. kusoshium, this marker (FuKu03) is not useful for the diagnosis because this difference could be related either to presence/absence of one or several introns, or maybe related to differences in the size of some of them. In principle, we do not attribute this difference to the obtaining of a non-specific product. It is important to emphasize the total absence of these three amplicons (gene markers) when template DNA belongs from other phytopathogenic ascomycetes (Fig 5B).

[Figure omitted. See PDF.]

Fig 5. PCR analysis of the markers designed for Fusarium kuroshium diagnosis.

From left to right, every three lanes correspond to each of the different markers (FuKu01, FuKu02, and FuKu03, respectively). These markers were tested using genomic DNA from different species of the Fusarium genus (A) and another phytopathogenic ascomycete fungi (B). Tested species were: On (A), F. kuroshium ([Fk]; lanes 1–3), F. graminearum ([Fg]; lanes 4–6), F. verticillioides ([Fv]; lanes 7–9), F. oxysporum ([Fo]; lanes 10–12), F. tricinctum ([Ft]; lanes 13–15), and F. solani ([Fs]; lanes 16–18); while on (B), Fusarium kuroshium ([Fk]; lanes 1–3), A. alternata ([Aa]; lanes 4–6), B. cinerea ([Bc]; lanes 7–9), and N. parvum ([Np]; lanes 10–12).

https://doi.org/10.1371/journal.pone.0246079.g005

Next, using DNA isolated from Persea americana cv. Hass stalks artificially infected with F. kuroshium (see S1 Fig and Methods for more details), we tested the reliability of the primers designed to diagnose both, fusariosis (genus-specific primers) and the Fusarium dieback disease (primers designed specifically for F. kuroshium). Consistent with the results presented above, FuSp02 genetic marker was successfully used to amplify the expected fragment, confirming the presence of genetic material in the DNA sample which belongs to one (or several) Fusarium species. To define the species, FuKu01 and Fuku02 genetic markers were successfully used (Fig 6).

[Figure omitted. See PDF.]

Fig 6. Evaluation of the diagnosis system using as template DNA obtained from a plant tissue artificially infected tissue with F. kuroshium.

This test included the FuSp02 genus-specific marker and FuKu01 and FuKu02 species-specific markers. Lanes 1, 2 and 3 which showed a total absence of any amplicons correspond to negative controls, that is, DNA used as template was isolated from non-infected plant tissue. For the rest of the lines, DNA used as template comes from plant tissue artificially infected tissue with F. kuroshium. Lane 4 represents FuSp02, while lanes 5 and 6 show the expected products for markers FuKu01 and FuKu02. As additional controls, markers designed for F. graminearum were also tested. Two of them designed in the present study (FuGr01 and FuGr02; lanes 7 and 8), and the other one (PC02; lane 9), previously reported [88]. In the same way, the FuOx01 and FuOx02 markers designed on this study for F. oxysporum, were also tested (lanes 10 and 11). Additional results regarding markers designed on this study to F. graminearum and F. oxysporum are shown in sections downstream described.

https://doi.org/10.1371/journal.pone.0246079.g006

We also test the effectiveness of the primers designed for fusariosis diagnostic on field samples. In our search of symptomatic trees, only three trees of the Erythrina corallodendron species were identified in a unique and small location (S2 Fig and S3 Table, see Methods for more details). First, to confirm the presence of genetic material from fungi on the DNA isolated from infected plant tissue, we amplify the ITS region (Fig 7, lanes 1–3). Then, using a genus-specific marker (FuSp02) we confirm that this fungus (or fungi) belongs to the Fusarium genus (Fig 7, lanes 4–6). Finally, FuKu01 and FuKu02 successfully identified the pathogen F. kuroshium (Fig 7, lanes 7–12). Equal that in vitro assays, primers designed to amplify specifically other species belonging to Fusarium genus (F. graminearun and F. oxysporum), were also used as negative controls and as expected, with none of them we obtained PCR products (data not shown).

[Figure omitted. See PDF.]

Fig 7. Evaluation of the diagnosis system using as template the DNA isolated from plant tissue of E. corallodendron trees, which were collected in the field and it was infested by KSHB and infected with F. kuroshium.

Lanes 1–3 show the amplicons corresponding to ITS, one lane per independently collected tree. Lanes 4–6, FuSp02 marker (genus-specific). Lanes 7–9, and 10–12, species-specific markers designed for F. kuroshium (FuKu01 and Fuku02).

https://doi.org/10.1371/journal.pone.0246079.g007

2.3. The strategy of pan-genome analysis allows the identification of specific markers for the different species which involved in fusariosis.

In order to provide additional evidence of the suitability of the application of pan-genome analysis for select markers and design primers to be used in a PCR-based diagnosis system, we evaluate the primers designed for the detection of F. graminearum and F. oxysporum. The primers designed for these species showed specificity, because only amplified DNA of the species for which they were designed. As a result of our analyses, we proposed two markers as diagnostic tools for F. graminearum (FuGr01 and FuGr02, Table 3); these allowed for the generation of 549 and 509 bp amplicons, respectively. We used as a positive control the primers previously reported to detect the presence of strains of the F. graminearum species ([88], see Table 3, PC02). After the analysis of band patterns obtained by electrophoresis, it was evident that primers designed on this study as well as positive control were specific for F. graminearum; these primers produced no unspecific products, neither with other species of the genera or with other ascomycetes (Fig 8).

[Figure omitted. See PDF.]

Fig 8. PCR analysis of primers designed for Fusarium graminearum.

From left to right, every third lane corresponds to the markers (FuGr01, FuGr02, and PC02) tested using genomic DNA from different species of the Fusarium genus and other phytopathogenic ascomycete fungi. (A) Lanes 1–3 correspond to Fusarium kuroshium [Fk], 4–6 to F. graminearum [Fg], 7–9 to F. verticillioides [Fv], 10–12 to F. oxysporum [Fo], 13–15 to F. tricinctum [Ft], and 16–18 to F. solani [Fs]; while on (B), lanes 1–3 correspond to F. graminearum [Fg], 4–6 to Alternaria alternata [Aa], 7–9 to Botrytis cinereal [Bc], and 10–12 to N. parvum [Np].

https://doi.org/10.1371/journal.pone.0246079.g008

For the diagnosis of F. oxysporum we identified two possible markers candidates, FuOx01 and FuOx02. Primers designed to amplify these markers produced amplicons with 554 and 556 bp sizes, respectively. The experimental validation using genomic DNA from the strain reveals high specificity (Fig 9). We use a previously reported set of primers as a positive control ([89], Table 3, PC03); however, in contrast with the high degree of specificity of the markers proposed in this study, the positive control of F. oxysporum (PC03) amplified PCR products of 280 pb when DNA used as template was isolated from Fusarium kuroshium, F. solani, F. graminearum, F. verticillioides, and N. parvum (Fig 10, lanes 1, 2, 3, 6 and 9, respectively). Which indicates that the primers previously reported are not useful to identify a species-specific marker that can be used to diagnose fusariosis caused by strains of F. oxysporum species.

[Figure omitted. See PDF.]

Fig 9. Evaluation of primers designed for the diagnosis of F. oxysporum.

From left to right, every two lanes correspond to the markers FuOx01, FuOx02. In (A), lanes 1 and 2 correspond to Fusarium kuroshium [Fk], 3 and 4 to F. graminearum [Fg], 5 and 6 to F. verticillioides [Fv], 7 and 8 to F. oxysporum [Fo], 9 and 10 to F. tricinctum [Ft], and 11 and 12 to F. solani [Fs]. In (B), lanes 1 and 2 showed the PCR products of F. oxysporum [Fo], 3 and 4 of Alternaria alternata [Aa], 5 and 6 from Botrytis cinereal [Bc], and 7 and 8 of N. parvum [Np].

https://doi.org/10.1371/journal.pone.0246079.g009

[Figure omitted. See PDF.]

Fig 10. PCR analysis of the PC03 marker previously reported for the diagnosis of F. oxysporum.

From left to right are shown the PCR products obtained for Fusarium kuroshium [Fk], F. graminearum [Fg], F. verticillioides [Fv], F. oxysporum [Fo], F. tricinctum [Ft], F. solani [Fs], A. alternata [Aa], B. cinereal [Bc], and N.parvum [Np], lanes 1 to 9, respectively.

https://doi.org/10.1371/journal.pone.0246079.g010

Conclusions

In this study, we identify highly divergent coding regions, and we prove that they can be used to design specific primers that allow the identification of genus- or specie- in specific manner (in this study we mainly focused our searches on species from Fusarium genus). Based on presented results we propose that this method could be employed to design primers or probes for other pathogenic species or strains. We confirmed by validating species-specific and genera specific genes of at least three different species of the Fusarium spp., that the proposed method is capable to identify reliable marker. We also prove the technique is stable enough to prevent erroneous diagnoses even when dealing with closely related species (e.g., F. solani and F. kuroshium).

A really low percent of the designed primers under proposed methodology can generate inconsistent results, we consider that this problem can be associated with automatic gene prediction algorithms often make errors and can jeopardize subsequent analyses. This issue obviously could be more frequent in low quality draft genomes with complex gene structures and assembled with low coverages. These low-quality genomes could be no included, however, it should be considered that increasing the number of genomes in pangenome analysis, it undoubtedly increases the reliability with which specific genus or species markers are identified. Based on the above, we suggest that gene model prediction processes need to be accompanied by the implementation of some dedicated computational methods to correct mistakes from the predictions and accompanied by some manual curation.

KSBH is considered a phytosanitary problem of national relevance in Mexico. The advantage of performing the PCR analysis directly from the DNA extraction of infected tissue in-vitro and in-vivo is a key innovation of the diagnosis system. Therefore, it is not necessary to isolate and grow the fungus. Our results inferred that the system may function at early infection stages however it is important to consider the use of and effective sampling method.

Short-term availability of the system is expected to be provided as a kit including not only the essential primers but also control DNA to be generated after cloning the amplicons generated for each of the proposed markers. Thus, users of this tool are guaranteed to have positive controls always available to bring complete certainty to their diagnosis. Finally, is important to consider that our system is susceptible to adapt to qPCR to become a more sensitive method.

Supporting information

S1 Fig. Fusarium kuroshium inoculations in avocado (Persea americana cv. Hass) stalks.

https://doi.org/10.1371/journal.pone.0246079.s001

(PDF)

S2 Fig. Collected field samples.

https://doi.org/10.1371/journal.pone.0246079.s002

(PDF)

S1 Table. Overview of proteomes used for pan-genome analysis.

This table showed the name of the strains from where the genomic information was obtained, as well as the metadata associated.

https://doi.org/10.1371/journal.pone.0246079.s003

(XLSX)

S2 Table. List of proteomes used on the database for gene model prediction.

https://doi.org/10.1371/journal.pone.0246079.s004

(XLSX)

S3 Table. Field sampled sites location of infested trees.

The first column described the native name, second the scientific name, and finally the georeferenced sites of the sampled sites.

https://doi.org/10.1371/journal.pone.0246079.s005

(XLSX)

S1 Raw images.

https://doi.org/10.1371/journal.pone.0246079.s006

(PDF)

Acknowledgments

We thank Martín Aluja Schuneman Hofer for managing funding with Mexican phytosanitary authorities. Also, we thank the staff of the mycology laboratory at CNRF for their technical assistance. We thank Jessica Paola Alcantara Chapa for valuable help in reviewing the manuscript.

Citation: Vázquez-Rosas-Landa M, Sánchez-Rangel D, Hernández-Domínguez EE, Pérez-Torres C-A, López-Buenfil A, de Jesús García-Ávila C, et al. (2021) Design of a diagnostic system based on molecular markers derived from the ascomycetes pan-genome analysis: The case of Fusarium dieback disease. PLoS ONE 16(1): e0246079. https://doi.org/10.1371/journal.pone.0246079

References

1. Chakraborty S, Newton AC. Climate change, plant diseases and food security: an overview. Plant Pathology. 2011;60(1):2–14.

2. van Dam P, de Sain M, Ter Horst A, van der Gragt M, Rep M. Use of comparative genomics-based markers for discrimination of host specificity in Fusarium oxysporum. Applied and environmental microbiology. 2018;84(1). Epub 2017/10/17. pmid:29030446.

3. Vestergård M, Rønn R, Ekelund F. Above–belowground interactions govern the course and impact of biological invasions. AoB Plants. 2015;7:plv025. pmid:25854693

4. Stouthamer R, Rugman-Jones P, Thu PQ, Eskalen A, Thibault T, Hulcr J, et al. Tracing the origin of a cryptic invader: phylogeography of the Euwallacea fornicatus (Coleoptera: Curculionidae: Scolytinae) species complex. Agricultural and Forest Entomology. 2017;19(4):366–75.

5. O’Donnell K, Libeskind-Hadas R, Hulcr J, Bateman C, Kasson MT, Ploetz RC, et al. Invasive Asian Fusarium–Euwallacea ambrosia beetle mutualists pose a serious threat to forests, urban landscapes and the avocado industry. Phytoparasitica. 2016;44(4):435–42.

6. Eskalen A, Gonzalez A, Wang DH, Twizeyimana M, Mayorquin JS, Lynch SC. First Report of a Fusarium sp. and its vector Tea Shot Hole Borer (Euwallacea fornicatus) causing Fusarium Dieback on avocado in California. Plant Disease. 2012;96(7):1070-. pmid:30727226

7. Eskalen A, Stouthamer R, Lynch SC, Rugman-Jones PF, Twizeyimana M, Gonzalez A, et al. Host range of Fusarium Dieback and its ambrosia beetle (Coleoptera: Scolytinae) Vector in Southern California. Plant Disease. 2013;97(7):938–51. pmid:30722538

8. Boland JM. The impact of an invasive ambrosia beetle on the riparian habitats of the Tijuana River Valley, California. PeerJ. 2016;4:e2141. pmid:27366644

9. García-Avila CDJ, Trujillo-Arriaga FJ, López-Buenfil JA, González-Gómez R, Carrillo D, Cruz LF, et al. First Report of Euwallacea nr. fornicatus (Coleoptera: Curculionidae) in Mexico. Florida Entomologist. 2016;99(3):555–6.

10. Biedermann P. Evolution of cooperation in ambrosia beetles. Mitteilungen der Deutschen Gesellschaft für allgemeine und angewandte Entomologie. 2014;19:191–202.

11. Freeman S, Sharon M, Maymon M, Mendel Z, Protasov A, Aoki T, et al. Fusarium euwallaceae sp. nov.—a symbiotic fungus of Euwallacea sp., an invasive ambrosia beetle in Israel and California. Mycologia. 2013;105(6):1595–606. Epub 2013/08/10. pmid:23928415.

12. Na F, Carrillo JD, Mayorquin JS, Ndinga-Muniania C, Stajich JE, Stouthamer R, et al. Two novel fungal symbionts Fusarium kuroshium sp. nov. and Graphium kuroshium sp. nov. of Kuroshio shot hole borer (Euwallacea sp. nr. fornicatus) cause Fusarium dieback on woody host species in California. Plant Disease. 2017. pmid:30673440

13. Rendon-Anaya M, Ibarra-Laclette E, Mendez-Bravo A, Lan T, Zheng C, Carretero-Paulet L, et al. The avocado genome informs deep angiosperm phylogeny, highlights introgressive hybridization, and reveals pathogen-influenced gene space adaptation. Proc Natl Acad Sci U S A. 2019;116(34):17081–9. Epub 2019/08/08. pmid:31387975.

14. Summerell BA, Salleh B, Leslie JF. A Utilitarian approach to Fusarium identification. Plant Disease. 2003;87(2):117–28. pmid:30812915

15. Atkins SD, Clark IM. Fungal molecular diagnostics: a mini review. J Appl Genet. 2004;45(1):3–15. Epub 2004/02/13. pmid:14960763.

16. McCartney HA, Foster SJ, Fraaije BA, Ward E. Molecular diagnostics for fungal plant pathogens. Pest management science. 2003;59(2):129–42. Epub 2003/02/18. pmid:12587866.

17. Li Y, Garibaldi A, Gullino ML. Molecular detection of Fusarium oxysporum f. sp. Chrysanthemi on three host plants: Gerbera jamesonii, Osteospermum sp. and Argyranthemum frutescens. Journal of Plant Pathology. 2010;92(2):525–30.

18. Faria CB, Abe CA, da Silva CN, Tessmann DJ, Barbosa-Tessmann IP. New PCR assays for the identification of Fusarium verticillioides, Fusarium subglutinans, and other species of the Gibberella fujikuroi complex. International journal of molecular sciences. 2012;13(1):115–32. Epub 2012/02/09. pmid:22312242.

19. Faria CB, Almeida-Ferreira GC, Gagliardi KB, Alves TCA, Tessmann DJ, Machinski Junior M, et al. Use of the polymerase chain reaction for detection of Fusarium graminearum in bulgur wheat. Food Science and Technology. 2012;32:201–8.

20. Visentin I, Tamietti G, Valentino D, Portis E, Karlovsky P, Moretti A, et al. The ITS region as a taxonomic discriminator between Fusarium verticillioides and Fusarium proliferatum. Mycological research. 2009;113(Pt 10):1137–45. Epub 2009/07/28. pmid:19631741.

21. Baird R, Abbas HK, Windham G, Williams P, Baird S, Ma P, et al. Identification of select fumonisin forming Fusarium species using PCR applications of the polyketide synthase gene and its relationship to fumonisin production in vitro. International journal of molecular sciences. 2008;9(4):554–70. Epub 2009/03/28. pmid:19325769.

22. Lievens B, Thomma BP. Recent developments in pathogen detection arrays: implications for fungal plant pathogens and use in practice. Phytopathology. 2005;95(12):1374–80. Epub 2008/10/24. pmid:18943547.

23. Carrillo JD, Mayorquin JS, Stajich JE, Eskalen A. Probe-based multiplex real-time PCR as a diagnostic tool to distinguish distinct fungal symbionts associated with Euwallacea kuroshio and E. whitfordiodendrus in California. Plant Disease. 2019. pmid:31647694

24. Hanna SE, Connor CJ, Wang HH. Real-time polymerase chain reaction for the food microbiologist: Technologies, applications, and limitations. Journal of Food Science. 2005;70(3):R49–R53.

25. Waweru B, Turoop L, Kahangi E, Coyne D, Dubois T. Non-pathogenic Fusarium oxysporum endophytes provide field control of nematodes, improving yield of banana (Musa sp.). Biological Control. 2014;74:82–8. https://doi.org/10.1016/j.biocontrol.2014.04.002.

26. Kavroulakis N, Ntougias S, Zervakis GI, Ehaliotis C, Haralampidis K, Papadopoulou KK. Role of ethylene in the protection of tomato plants against soil-borne fungal pathogens conferred by an endophytic Fusarium solani strain. Journal of experimental botany. 2007;58(14):3853–64. Epub 2007/12/01. pmid:18048373.

27. Qiu H, Cai G, Luo J, Bhattacharya D, Zhang N. Extensive horizontal gene transfers between plant pathogenic fungi. BMC Biology. 2016;14(1):41. pmid:27215567

28. Andersson JO. Convergent Evolution: Gene Sharing by Eukaryotic Plant Pathogens. Current Biology. 2006;16(18):R804–R6. pmid:16979549

29. Shang Y, Xiao G, Zheng P, Cen K, Zhan S, Wang C. Divergent and convergent evolution of fungal pathogenicity. Genome Biol Evol. 2016;8(5):1374–87. Epub 2016/04/14. pmid:27071652.

30. Lievens B, Claes L, Vakalounakis DJ, Vanachter AC, Thomma BP. A robust identification and detection assay to discriminate the cucumber pathogens Fusarium oxysporum f. sp. cucumerinum and f. sp. radicis-cucumerinum. Environ Microbiol. 2007;9(9):2145–61. Epub 2007/08/10. pmid:17686014.

31. Lievens B, Rep M, Thomma BP. Recent developments in the molecular discrimination of formae speciales of Fusarium oxysporum. Pest management science. 2008;64(8):781–8. Epub 2008/03/13. pmid:18335459.

32. Li R, Li Y, Zheng H, Luo R, Zhu H, Li Q, et al. Building the sequence map of the human pan-genome. Nature Biotechnology. 2009;28:57. pmid:19997067

33. Golicz AA, Batley J, Edwards D. Towards plant pangenomics. Plant Biotechnol J. 2016;14(4):1099–105. Epub 2015/11/26. pmid:26593040.

34. Moriel DG, Tan L, Goh KGK, Phan M-D, Ipe DS, Lo AW, et al. A novel protective vaccine antigen from the core Escherichia coli genome. mSphere. 2016;1(6):e00326–16. pmid:27904885

35. McInerney JO, McNally A. Why prokaryotes have pangenomes. 2017;2:17040. pmid:28350002.

36. Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: Implications for the microbial “pan-genome”. Proceedings of the National Academy of Sciences of the United States of America. 2005;102(39):13950–5. pmid:16172379

37. Maphosa MN, Steenkamp ET, Wingfield BD. Genome-based selection and characterization of Fusarium circinatum-specific sequences. G3 (Bethesda, Md). 2016;6(3):631–9. Epub 2016/02/19. pmid:26888868.

38. Ling J, Zhang J-x, Zeng F, Cao Y-x, Xie B-y, Yang Y-h. Comparative genomics provide a rapid detection of Fusarium oxysporum f. sp. conglutinans. Journal of Integrative Agriculture. 2016;15(4):822–31. https://doi.org/10.1016/S2095-3119(15)61237-0.

39. Short DPG, O’Donnell K, Stajich JE, Hulcr J, Kijimoto T, Berger MC, et al. PCR multiplexes discriminate Fusarium symbionts of invasive Euwallacea ambrosia beetles that inflict damage on numerous tree species throughout the United States. Plant Disease. 2016;101(1):233–40. pmid:30682305

40. Grigoriev IV, Nordberg H, Shabalov I, Aerts A, Cantor M, Goodstein D, et al. The genome portal of the Department of Energy Joint Genome Institute. Nucleic acids research. 2012;40 (Database issue):D26–D32. Epub 2011/11/22. pmid:22110030.

41. Nordberg H, Cantor M, Dusheyko S, Hua S, Poliakov A, Shabalov I, et al. The genome portal of the Department of Energy Joint Genome Institute: 2014 updates. Nucleic Acids Res. 2014;42 (Database issue):D26–31. Epub 2013/11/15. pmid:24225321.

42. Ibarra-Laclette E, Sánchez-Rangel D, Hernández-Domínguez E, Pérez-Torres CA, Ortiz-Castro R, Villafán E, et al. Draft genome sequence of the phytopathogenic fungus Fusarium euwallaceae, the causal agent of Fusarium Dieback. Genome announcements. 2017;5(999):e00881–17. pmid:28860245

43. Stanke M, Tzvetkova A, Morgenstern B. AUGUSTUS at EGASP: using EST, protein and genomic alignments for improved gene prediction in the human genome. Genome biology. 2006;7. pmid:16925833

44. Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics (Oxford, England). 2008;24(5):637–44. Epub 2008/01/26. pmid:18218656.

45. 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. pmid:18025269

46. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol. 2011;29(7):644–52. Epub 2011/05/17. pmid:21572440.

47. Contreras-Moreira B, Vinuesa P. GET_HOMOLOGUES, a versatile software package for scalable and robust microbial pangenome analysis. Applied and environmental microbiology. 2013;79(24):7696–701. Epub 2013/10/08. pmid:24096415.

48. Contreras-Moreira B, Cantalapiedra CP, Garcia-Pereira MJ, Gordon SP, Vogel JP, Igartua E, et al. Analysis of plant pan-genomes and transcriptomes with GET_HOMOLOGUES-EST, a clustering solution for sequences of the same species. Frontiers in plant science. 2017;8:184. Epub 2017/03/07. pmid:28261241.

49. Li L, Stoeckert CJ Jr., Roos DS. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 2003;13(9):2178–89. Epub 2003/09/04. pmid:12952885.

50. Untergasser A, Nijveen H, Rao X, Bisseling T, Geurts R, Leunissen JA. Primer3Plus, an enhanced web interface to Primer3. Nucleic Acids Res. 2007;35(Web Server issue):W71–4. Epub 2007/05/09. pmid:17485472.

51. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 2012;40(15):e115. Epub 2012/06/26. pmid:22730293.

52. Lassmann T, Sonnhammer EL. Kalign—an accurate and fast multiple sequence alignment algorithm. BMC Bioinformatics. 2005;6:298. Epub 2005/12/14. pmid:16343337.

53. Rice P, Longden I, Bleasby A. EMBOSS: the European Molecular Biology Open Software Suite. Trends in genetics: TIG. 2000;16(6):276–7. Epub 2000/05/29. pmid:10827456.

54. Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994;22(22):4673–80. Epub 1994/11/11. pmid:7984417.

55. Castresana J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Molecular biology and evolution. 2000;17(4):540–52. Epub 2000/03/31. pmid:10742046.

56. Gans JD, Wolinsky M. Improved assay-dependent searching of nucleic acid sequence databases. Nucleic Acids Res. 2008;36(12):e74. Epub 2008/06/03. pmid:18515842.

57. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. Journal of molecular biology. 1990;215(3):403–10. pmid:2231712

58. Sánchez-Rangel D, SanJuan-Badillo A, Plasencia J. Fumonisin Production by Fusarium verticillioides Strains Isolated from Maize in Mexico and Development of a Polymerase Chain Reaction to Detect Potential Toxigenic Strains in Grains. Journal of Agricultural and Food Chemistry. 2005;53(22):8565–71. pmid:16248554

59. Tapia-Tussell R, Lappe P, Ulloa M, Quijano-Ramayo A, Caceres-Farfan M, Larque-Saavedra A, et al. A rapid and simple method for DNA extraction from yeasts and fungi isolated from Agave fourcroydes. Mol Biotechnol. 2006;33(1):67–70. Epub 2006/05/13. pmid:16691008.

60. Inglis PW, Pappas MCR, Resende LV, Grattapaglia D. Fast and inexpensive protocols for consistent extraction of high quality DNA and RNA from challenging plant and fungal samples for high-throughput SNP genotyping and sequencing applications. PloS one. 2018;13(10):e0206085. Epub 2018/10/20. pmid:30335843.

61. Innis MA, Gelfand DH, Sninsky JJ, White TJ. PCR Protocols: A guide to methods and applications: Elsevier Science; 1990. 482 p.

62. Wiemann P, Sieber CM, von Bargen KW, Studt L, Niehaus EM, Espino JJ, et al. Deciphering the cryptic genome: genome-wide analyses of the rice pathogen Fusarium fujikuroi reveal complex regulation of secondary metabolism and novel metabolites. PLoS Pathog. 2013;9(6):e1003475. Epub 2013/07/05. pmid:23825955.

63. Cuomo CA, Guldener U, Xu JR, Trail F, Turgeon BG, Di Pietro A, et al. The Fusarium graminearum genome reveals a link between localized polymorphism and pathogen specialization. Science (New York, NY). 2007;317(5843):1400–2. Epub 2007/09/08. pmid:17823352.

64. Ma LJ, van der Does HC, Borkovich KA, Coleman JJ, Daboussi MJ, Di Pietro A, et al. Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium. Nature. 2010;464(7287):367–73. Epub 2010/03/20. pmid:20237561.

65. Coleman JJ, Rounsley SD, Rodriguez-Carres M, Kuo A, Wasmann CC, Grimwood J, et al. The genome of Nectria haematococca: contribution of supernumerary chromosomes to gene expansion. PLoS genetics. 2009;5(8):e1000618. Epub 2009/08/29. pmid:19714214.

66. Zeiner CA, Purvine SO, Zink EM, Paša-Tolić L, Chaput DL, Haridas S, et al. Comparative analysis of secretome profiles of Manganese(II)-Oxidizing ascomycete fungi. PloS one. 2016;11(7):e0157844. Epub 2016/07/21. pmid:27434633.

67. Wicker T, Oberhaensli S, Parlange F, Buchmann JP, Shatalina M, Roffler S, et al. The wheat powdery mildew genome shows the unique evolution of an obligate biotroph. Nature genetics. 2013;45(9):1092–6. Epub 2013/07/16. pmid:23852167.

68. Frantzeskakis L, Kracher B, Kusch S, Yoshikawa-Maekawa M, Bauer S, Pedersen C, et al. Signatures of host specialization and a recent transposable element burst in the dynamic one-speed genome of the fungal barley powdery mildew pathogen. BMC genomics. 2018;19(1):381. Epub 2018/05/24. pmid:29788921.

69. Marsberg A, Kemler M, Jami F, Nagel JH, Postma-Smidt A, Naidoo S, et al. Botryosphaeria dothidea: a latent pathogen of global importance to woody plant health. Molecular plant pathology. 2017;18(4):477–88. Epub 2016/09/30. pmid:27682468.

70. Amselem J, Cuomo CA, van Kan JA, Viaud M, Benito EP, Couloux A, et al. Genomic analysis of the necrotrophic fungal pathogens Sclerotinia sclerotiorum and Botrytis cinerea. PLoS genetics. 2011;7(8):e1002230. Epub 2011/08/31. pmid:21876677.

71. Condon BJ, Leng Y, Wu D, Bushley KE, Ohm RA, Otillar R, et al. Comparative genome structure, secondary metabolite, and effector coding capacity across Cochliobolus pathogens. PLoS genetics. 2013;9(1):e1003233. Epub 2013/01/30. pmid:23357949.

72. Ohm RA, Feau N, Henrissat B, Schoch CL, Horwitz BA, Barry KW, et al. Diverse lifestyles and strategies of plant pathogenesis encoded in the genomes of eighteen Dothideomycetes fungi. PLoS Pathog. 2012;8(12):e1003037. Epub 2012/12/14. pmid:23236275.

73. Haridas S, Albert R, Binder M, Bloem J, LaButti K, Salamov A, et al. 101 Dothideomycetes genomes: A test case for predicting lifestyles and emergence of pathogens. Studies in mycology. 2020;96:141–53. Epub 2020/03/25. pmid:32206138.

74. Dietrich FS, Voegeli S, Brachat S, Lerch A, Gates K, Steiner S, et al. The Ashbya gossypii genome as a tool for mapping the ancient Saccharomyces cerevisiae genome. Science (New York, NY). 2004;304(5668):304–7. Epub 2004/03/06. pmid:15001715.

75. Jones L, Riaz S, Morales-Cruz A, Amrine KC, McGuire B, Gubler WD, et al. Adaptive genomic structural variation in the grape powdery mildew pathogen, Erysiphe necator. BMC genomics. 2014;15(1):1081. Epub 2014/12/10. pmid:25487071.

76. Gardiner DM, McDonald MC, Covarelli L, Solomon PS, Rusu AG, Marshall M, et al. Comparative pathogenomics reveals horizontally acquired novel virulence genes in fungi infecting cereal hosts. PLoS Pathog. 2012;8(9):e1002952. Epub 2012/10/03. pmid:23028337.

77. Okagaki LH, Nunes CC, Sailsbery J, Clay B, Brown D, John T, et al. Genome sequences of three phytopathogenic species of the Magnaporthaceae family of fungi. G3 (Bethesda, Md). 2015;5(12):2539–45. Epub 2015/09/30. pmid:26416668.

78. Wu W, Davis RW, Tran-Gyamfi MB, Kuo A, LaButti K, Mihaltcheva S, et al. Characterization of four endophytic fungi as potential consolidated bioprocessing hosts for conversion of lignocellulose into advanced biofuels. Applied microbiology and biotechnology. 2017;101(6):2603–18. Epub 2017/01/13. pmid:28078400.

79. Rouxel T, Grandaubert J, Hane JK, Hoede C, van de Wouw AP, Couloux A, et al. Effector diversification within compartments of the Leptosphaeria maculans genome affected by Repeat-Induced Point mutations. Nat Commun. 2011;2:202. Epub 2011/02/18. pmid:21326234.

80. Forgetta V, Leveque G, Dias J, Grove D, Lyons R Jr., Genik S, et al. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX Titanium System in a comparison of multiple genomics core facilities. J Biomol Tech. 2013;24(1):39–49. pmid:23542132.

81. Mondo SJ, Dannebaum RO, Kuo RC, Louie KB, Bewick AJ, LaButti K, et al. Widespread adenine N6-methylation of active genes in fungi. Nature genetics. 2017;49(6):964–8. Epub 2017/05/10. pmid:28481340.

82. Ellwood SR, Liu Z, Syme RA, Lai Z, Hane JK, Keiper F, et al. A first genome assembly of the barley fungal pathogen Pyrenophora teres f. teres. Genome biology. 2010;11(11):R109. Epub 2010/11/12. pmid:21067574.

83. Manning VA, Pandelova I, Dhillon B, Wilhelm LJ, Goodwin SB, Berlin AM, et al. Comparative genomics of a plant-pathogenic fungus, Pyrenophora tritici-repentis, reveals transduplication and the impact of repeat elements on pathogenicity and population divergence. G3 (Bethesda, Md). 2013;3(1):41–63. Epub 2013/01/15. pmid:23316438.

84. Cissé OH, Almeida JMGCF, Fonseca Á, Kumar AA, Salojärvi J, Overmyer K, et al. Genome sequencing of the plant pathogen Taphrina deformans, the causal agent of peach leaf curl. mBio. 2013;4(3):e00055–13. pmid:23631913

85. Klosterman SJ, Subbarao KV, Kang S, Veronese P, Gold SE, Thomma BP, et al. Comparative genomics yields insights into niche adaptation of plant vascular wilt pathogens. PLoS Pathog. 2011;7(7):e1002137. Epub 2011/08/11. pmid:21829347.

86. Vanderpool D, Bracewell RR, McCutcheon JP. Know your farmer: Ancient origins and multiple independent domestications of ambrosia beetle fungal cultivars. Mol Ecol. 2017. Epub 2017/11/01. pmid:29087025.

87. Karlsson I, Edel-Hermann V, Gautheron N, Durling MB, Kolseth A-K, Steinberg C, et al. Genus-specific primers for study of Fusarium communities in field samples. Applied and environmental microbiology. 2016;82(2):491–501. pmid:26519387

88. de Biazio GR, Leite GGS, Tessmann DJ, Barbosa-Tessmann IP. A new PCR approach for the identification of Fusarium graminearum. Brazilian Journal of Microbiology. 2008;39(3):554–60. pmid:24031265

89. Mishra RK, Pandey BK, Singh V, Mathew AJ, Pathak N, Zeeshan M. Molecular detection and genotyping of Fusarium oxysporum f. sp. psidii isolates from different agro-ecological regions of India. J Microbiol. 2013;51(4):405–12. Epub 2013/08/31. pmid:23990290.

90. Moussa TAA, Al-Zahrani HS, Kadasa NMS, Ahmed SA, de Hoog GS, Al-Hatmi AMS. Two new species of the Fusarium fujikuroi species complex isolated from the natural environment. Antonie Van Leeuwenhoek. 2017;110(6):819–32. Epub 2017/03/16. pmid:28303400.