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Hilário, S.; Gonçalves, M.F.M. Omics to Study Fungal Plant Pathogens. Encyclopedia. Available online: https://encyclopedia.pub/entry/42580 (accessed on 24 December 2024).
Hilário S, Gonçalves MFM. Omics to Study Fungal Plant Pathogens. Encyclopedia. Available at: https://encyclopedia.pub/entry/42580. Accessed December 24, 2024.
Hilário, Sandra, Micael F. M. Gonçalves. "Omics to Study Fungal Plant Pathogens" Encyclopedia, https://encyclopedia.pub/entry/42580 (accessed December 24, 2024).
Hilário, S., & Gonçalves, M.F.M. (2023, March 28). Omics to Study Fungal Plant Pathogens. In Encyclopedia. https://encyclopedia.pub/entry/42580
Hilário, Sandra and Micael F. M. Gonçalves. "Omics to Study Fungal Plant Pathogens." Encyclopedia. Web. 28 March, 2023.
Omics to Study Fungal Plant Pathogens
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This entry is adapted from the peer-reviewed paper 10.3390/horticulturae9040423

In plant pathology, multi-omics (genomics, transcriptomics, proteomics, and metabolomics) can help mainly in the prevention and management of diseases. The omics have been applied to elucidate the function of genes and the structure of the genome to provide insights into gene and protein expression and to understand the metabolic profiling of both the host and the pathogen during an infection process. The application of omics in the genus Diaporthe is still poorly explored, although metabolomics has been widely applied to explore endophytic Diaporthe natural products for their potential applications in pharmacology. Although the genus Diaporthe comprises important plant pathogens and endophytes, these species also have the ability to switch lifestyles.

fungal-plant interactions genomics metabolomics pathogenicity proteomics

1. Genomics

Since the sequencing of the first fungal genome, the yeast Saccharomyces cerevisiae in 1996 [1], advances in next-generation sequencing (NGS) technology have led to an increase in genomes [2], specifically from fungal pathogens that affect agriculture and forestry [3]. NGS is a rapid and high-throughput approach, and it is represented by different sequencing platforms such as AB SOLiD, Illumina HiSeq System, PacBio RS, and Oxford Nanopore Technology PromethION [4][5]. In 2011, the 1000 Fungal Genomes Project started with plans to sequence at least two reference genomes from each fungal family (http://1000.fungalgenomes.org; acceded on 5 February 2023). A search at the NCBI database (https://www.ncbi.nlm.nih.gov/; accessed on 10 February 2023) and the Genome Portal (https://genome.jgi.doe.gov/portal/; accessed on 10 February 2023) retrieved more than 12,200 and 2600 fungal genomes, respectively. Among these, over 11,550 genomes belong to the phylum Ascomycota that comprises the highest number of sequenced fungal genomes [6]. Despite the increasing number of fungal genomes over the last years, there are only a few genomes available in the genus Diaporthe. Table 1 sums up all species of Diaporthe with sequenced genomes deposited in NCBI and JGI databases.
Table 1. Synopsis of all Diaporthe strains with genomes sequenced. (Note: NA stands for ‘not applicable’ meaning that the genome is available at the JGI Portal but has no Project ID).
Species Strain Host JGI
Project		 GenBank
Accession Number		 Sequencing Platform References
Diaporthe ampelina DA912 Vitis vinifera NA LCUC00000000 Illumina HiSeq [7]
S3MP Commiphora wightii - LWAD00000000 Illumina HiSeq [8]
Diaporthe amygdali CAA958 Vaccinium
corymbosum		 - JAJATV000000000 Illumina HiSeq [9]
DUCC20226 Malus sp. - JAJJOG000000000 PacBio Sequel and Illumina -
Diaporthe aspalathi MS-SSC91 Glycine max - LJJS00000000 Illumina HiSeq [10]
Diaporthe batatas CRI 302-4 Ipomoea batatas - JAHWGW000000000 Oxford
Nanopore and PromethION		 [5]
Diaporthe capsici GY-Z16 Juglans regia - WNXA00000000 PacBio RSII [11]
Diaporthe caulivora D57 G. max - JAMPTR000000000 PacBio Sequel [12]
Diaporthe cf. heveae LGMF1633 - 1251927 - - -
Diaporthe destruens CRI305-2 Ipomoea batatas - JACAAM010000000 Oxford Nanopore and
PromethION		 [13]
Diaporthe citrisiana ZJUD30 Citrus unshiu - JADAZS000000000 Illumina HiSeq [14]
- JADWDH000000000
Diaporthe citrichinensis ZJUD34 C. unshiu - JADAZR000000000 Illumina HiSeq [14]
Diaporthe citri NFHF-8-4 Citrus sp. - JACTAD000000000 PacBio Sequel [15]
ZJUD2 C. reticulata - JADAZQ000000000 Illumina HiSeq [14]
ZJUD14 C. reticulata - JADAZP000000000 Illumina HiSeq
Q7 C. reticulata - JADAZO000000000 Illumina HiSeq
Diaporthe eres (syn. D. phragmitis) NJD1 Actinidia deliciosa - JACDXY000000000 PacBio RS [16]
Diaporthe eres (syn. D. vaccinii) CBS 160.32 V. corymbosum - JAJATR000000000 Illumina HiSeq [9]
Diaporthe eres Phoaprs 18-02 Malus sp. - JAKJXL000000000 Illumina
NovaSeq		 [17]
Phoaprs 18-03 Malus sp. - JAKJXM000000000 PacBio Sequel
Diaporthe helianthi 7/96 Helianthus annuus NA MAVT02000001 Illumina MiSeq [18]
Diaporthe ilicicola FPH2015-502 Ilex verticillata - JALPVH000000000 Illumina and Oxford
Nanopore		 [19]
Diaporthe
inconspicua		 LGMF1612 - 1251935 - - -
Diaporthe longicolla MSPL 10–6 G. max - AYRD00000000 Illumina HiSeq [20]
TWH P74 - JUJX00000000 Illumina HiSeq [21]
Diaporthe vexans PV 4 Solanum melongena   JAJLLZ000000000 Oxford Nanopore [22]
Diaporthe vochysiae LGMF1583 Vochysia
divergens		 1251933 - Pacbio -
Diaporthe sp. DP-2020a Sequoia sempervirens - JACVEP000000000 Illumina HiSeq -
Diaporthe sp. HANT25 Hydnocarpus anthelminthicus - JACBFG000000000 Illumina HiSeq [23]
The low number of genomes and annotations available impairs researchers to unveil key genes involved in the infection process of Diaporthe, as well as mechanisms involved in the dual lifestyle (pathogen-endophyte). To bridge this, studies on the genome sequencing of Diaporthe species have focused on genomic signatures that allow them to successfully invade and colonize the host plant through the presence of:
(1)
Hydrolytic enzymes to degrade plant cell wall polysaccharides (e.g., pectins, celluloses, and lignins) to ensure a successful entry into the host [9][12][14][20].
(2)
Biosynthetic gene clusters encoding for toxic metabolites that injure plant cells and enhance disease progression. (e.g., fusicoccin A, fusarin, and ACT-toxin II) [9][20].
(3)
Cellular transporters of ions (e.g., zinc, sulfur, copper), molecules that enhance pathogenicity (e.g., peroxiredoxin, tetraspanin), and sugars from plant polysaccharides degradation (e.g., xylose, inositol, and glycerol) [9].
(4)
Pathogenicity-related genes (e.g., acid aspartate and aminopeptidase) and candidate effectors (e.g., carboxylesterases, CFEM-domain, and laccases) that facilitate the host to be infected and manipulate the host immune defense [9][12].
Moreover, genome-wide association studies could be implemented to identify the genomic regions potentially associated with aggressiveness, through the analysis of single nucleotide polymorphism (SNP) data [24]. The analysis of SNPs between pathogenic and non-pathogenic fungi/endophytes is also a promising tool for the identification of candidate effectors underlying the pathogenicity of species of Diaporthe, as well as to understand the ecological and evolutionary dynamics of plant pathogens [25].
The integration of omics approaches can also speed up the identification of putative effectors in the genus Diaporthe and the characterization of their virulence functions in their host plants. Effectors are secreted proteins by fungal pathogens that modulate and interfere with plant defense responses [26]. Recently, Mena et al. [12] defined a set of proteins considered within the secretome of six Diaporthe species through a comparative analysis of available genomes. Moreover, Hilário et al. [9] have also identified candidate effectors from two Diaporthe species, through sequencing and analysis of their genomes (Table 2). This suggests that the genomes of species of Diaporthe have a large array of candidate effectors involved in pathogenicity, and some of them are common to other Diaporthe pathogens while others are Diaporthe-specific [12]. Nevertheless, future studies should be undertaken aiming to reveal effector functions during the infection process and to understand how effectors alter plant physiology, thus underpinning Diaporthe lifestyles [27]. Overall, genomic studies on Diaporthe intend to deepen the knowledge on:
(1)
Ecological selection and adaptation of species of Diaporthe to degrade the available biomass as carbon source [9][12][20].
(2)
Gene functions related to pathogenicity [9][12].
(3)
Phylogenomic studies to offer insights into phylogenetic inference of Diaporthe [2][14].
(4)
Genetic basis for multi-omics analyses to provide a thorough overview on plant-pathogen interactions [12][28][29][30][31].
Table 2. Overview of some effector proteins identified in the genomes of species of Diaporthe.
Species Effector Candidate Effector Location References
D. amygdali glycosyl hydrolase family 61 Apoplastic [9]
aldehyde reductase 1 Apoplastic
putative cfem domain-containing protein Cytoplasmic
putative metalloprotease Apoplastic
murein transglycosylase Apoplastic
acetyl xylan esterase Apoplastic
putative cerato-ulmin Apoplastic
putative gas1-like protein Apoplastic
putative secreted aspartic proteinase precursor Apoplastic
Pectate lyase H Apoplastic
glycosyl hydrolase family 61 Apoplastic
D. capsici sterigmatocystin biosynthesis peroxidase stcC Apoplastic [12]
pectate lyase F Apoplastic
putative 1,4-beta-D-glucan cellobiohydrolase A Apoplastic
putative proline-rich antigen Apoplastic
chitin deacetylase Apoplastic
xylanase G1 Apoplastic
putative chitin binding protein Apoplastic
putative mannose binding Apoplastic
putative gas1-like protein Apoplastic
glycoside hydrolase family 11 protein Apoplastic
Cell wall glyco protein Cytoplasmic
Poly(rC)-binding protein 4 Cytoplasmic
D. caulivora putative sterigmatocystin biosynthesis peroxidase stcC Apoplastic [12]
putative proline-rich antigen Apoplastic
putative cytochrome p450 Apoplastic
xylanase G1 Apoplastic
glycoside hydrolase Apoplastic
pectate lyase Apoplastic
peptidase S41 family protein Apoplastic
chitin deacetylase Apoplastic
putative aldehyde dehydrogenase Apoplastic
pectate lyase F Apoplastic
putative 1,4-beta-D-glucan cellobiohydrolase A Apoplastic
D. citri chitin deacetylase Apoplastic [12]
Glucan endo-1,3-beta-glucosidase Apoplastic
putative 1,4-beta-D-glucan cellobiohydrolase A Apoplastic
putative sterigmatocystin biosynthesis peroxidase stcC Apoplastic
cholera enterotoxin subunit A2 Apoplastic
pectate lyase Apoplastic
polysaccharide lyase family 3 protein Apoplastic
Chitin binding protein Apoplastic
Acetylxylan esterase-like protein Apoplastic
pectate lyase F Apoplastic
xylanase G1 Apoplastic
putative riboflavin-aldehyde forming enzyme protein Apoplastic
D. destruens pectate lyase Apoplastic [12]
NPP1 domain-containing protein Apoplastic
xylanase G1 Apoplastic
cellulose binding CEL1 Apoplastic
putative pectate lyase F Apoplastic
Poly(rC)-binding protein 4 Apoplastic
chitin deacetylase Apoplastic
ribosomal protein s17 Cytoplasmic
Protein CAP22 Apoplastic
fungal cellulose binding domain-containing protein Apoplastic
D. eres (syn. D. phragmitis) pectate lyase Apoplastic [12]
Acetylxylan esterase 2 Apoplastic
putative glutamine-serine-proline rich Apoplastic
putative rhamnogalacturonan acetylesterase Apoplastic
xylanase G1 Apoplastic
Protein CAP22 Apoplastic
lytic polysaccharide monooxygenase Apoplastic
pectate lyase F Apoplastic
putative proline-rich antigen Apoplastic
chitin deacetylase Apoplastic
D. eres (syn. D. vaccinii) putative metalloprotease Apoplastic [9]
carbohydrate-binding module family 50 protein Apoplastic
putative glycoside hydrolase family 61 protein Apoplastic
acetylxylan esterase Apoplastic
putative ricin b lectin Apoplastic
putative pectate lyase b Apoplastic
aldehyde reductase 1 Apoplastic
putative npp1 domain Cytoplasmic
putative pectinesterase Cytoplasmic
putative pectate lyase Apoplastic
disulfide-isomerase erp38 Cytoplasmic
D. longicolla polysaccharide lyase family 3 protein Apoplastic [12]
putative carbohydrate-binding module family 1 protein Apoplastic
carbohydrate esterase family 5 protein Apoplastic
starch binding domain-containing protein Apoplastic
putative pectate lyase F Apoplastic
Acetylxylan esterase 2 Apoplastic
pectate lyase Apoplastic
cell wall protein PhiA Apoplastic
xylanase G1 Apoplastic
cellulose binding CEL1 Apoplastic
fungal cellulose binding domain-containing protein Apoplastic
Protein CAP22 Apoplastic

2. Transcriptomics

The RNA-Seq technique has revolutionized the way in which transcriptomes are analyzed [32]. It promotes the understanding of gene expression under different conditions and allows for the discovery of new genes and transcription patterns, which helps to understand cell function and metabolic mechanisms [33]. As a result, it has been considered one of the most important applications of NGS technology, and one of the most important tools in plant pathology [32] since it allows to investigate the transcriptomic profiles of plant pathogens during infection [34][35]. As the interaction between plants and their pathogens is a dynamic process, these interactions should be analyzed as a dual process [34]. Hence, dual RNA sequencing allows to study host and pathogen transcriptomes simultaneously, detecting pathogen-specific transcripts as well as provides a more complete insight into the host defense mechanisms [36]. This approach has already been applied in studies of plant-pathogen interactions in crops such as grapevines [31][35]; peach [37] and potato [38]; medicinal plants [39]; and forest trees such as Eucalyptus sp. [40] and Pinus sp. [41].
However, the utilization of transcriptomics data is often hampered by the lack of annotations and genomes available, which is reflected in the scarce transcriptome studies, for example, in the genus Diaporthe. The few studies regarding the transcripts characterization in this genus are mainly based on quantitative PCR (qPCR). For example, Książkiewicz et al. [42] have used this technique to target genes on Lupinus angustifolius that confer resistance to D. toxica, the causal agent of lupinosis. Moreover, Elverson et al. [43] developed two qPCR assays to detect and quantify D. helianthi and D. gulyae on sunflower, the causing agents of Phomopsis stem canker. Hosseini et al. [44] have also established a multiplex qPCR to distinguish D. longicolla, D. caulivora, D. eres, and D. novem on soybean, which are responsible for seed decay, pod, and stem canker on this host. In another study, Fujiwara et al. [45] demonstrated that the qPCR assay they developed is useful to diagnose and quantify D. batatas and D. destruens in sweet potato, as they are the main causal agents of foot rot disease.
To the knowledge, Mena et al. [12] applied for the first time the dual RNA-Seq approach to the genus Diaporthe to evaluate how D. caulivora may affect soybean plants. The authors stated that the infected soybean with D. caulivora induces the reinforcement of cell walls, evidenced by the incorporation of phenolic compounds. Moreover, several defense genes were also upregulated, including those encoding a pathogenesis-related (PR) protein-1 (PR-1), a PR-10, a β-1,3-glucanase, two chitinases, two lipoxygenases, a phenylalanine-ammonia lyase, and a chalcone synthase [12][46]. Given the cosmopolitan behavior of species of Diaporthe, their ability to infect a wide range of hosts and their different lifestyles (e.g., endophytes and pathogens), transcriptome analyses of both the host and the pathogen, and the validation of the differentially expressed genes (DEGs) should be considered to understand the regulatory networks and mechanisms involved in infection processes. Such an approach would thus contribute to unravelling host-pathogen interactions to provide helpful information for the development of disease control strategies [39][40][41][47].

3. Proteomics

Profiling the protein expression can unravel the functions of different proteins by assessing the plant responses to environmental stresses, such as pathogen attack [48]. After the plant is stimulated by external stresses, their defensive response is rapidly generated, followed by changes in some physiological and biochemical characters (e.g., decrease in chlorophyl A and photosynthesis) [49]. For example, studies have demonstrated that Arabidopsis infected by Fusarium [50], rice infected by Magnaporthe oryzae [51], and strawberry leaves inoculated with Colletotrichum [52] showed an overexpression of peroxidase levels after pathogens infection, to scavenged reactive oxygen species (ROS). Additionally, PR proteins such as chitinases and plant β-1,3-glucanases are considered important components of plant defense mechanisms under a pathogen attack [53]. For instance, the above-mentioned proteins were upregulated in Triticum aestivum inoculated with F. equiseti [54] and in Populus trichocarpa after infection with Botryosphaeria dothidea [55].
When the fungus infects host plants, a series of effector proteins (e.g., cell wall degrading enzymes) are secreted into the host tissue to destroy intracellular components, interfering with their defense response [50][53]. The analysis of the proteome has been successfully made for some fungal plant pathogens. For instance, some studies have shown that cell wall degrading enzymes such as pectin, esterases, xylanases, pectate lyases, or galacturonases are upregulated in L. theobromae [52], M. oryzae [51], and F. graminearum [56], suggesting their pathogenicity on grapevines, rice, and barley, respectively. Moreover, the hydrolase glucan-β-glucosidase was found to be involved in the virulence of C. higginsianum [57] and Alternaria alternata [58]. Some effector proteins secreted by fungal pathogens, such as avirulence proteins (Avr), are delivered into the host plant, which have the potential to suppress pathogen-associated molecular patterns (PAMPs)-triggered immunity [59]. However, these pathogen-derived avirulence proteins are recognized by plant receptor proteins encoded by R genes, resulting in effector-triggered immunity that leads to fast responses [60].
As mentioned above, proteomics has been applied to unveil key proteins of several plant pathogens as well as those involved in plant defense under a pathogen attack. Nevertheless, no proteomic studies have been performed with members of the genus Diaporthe nor for their interaction with plants. As the proteome profiling during infection can identify specific proteins involved in plant disease resistance and pathogenicity processes [61], in-depth studies and comparative proteomics should be undertaken to reveal molecular mechanisms of Diaporthe—plant interactions as well as the susceptibility or resistance in plants. These studies will assist in the discovery of novel proteins that might be potential candidates for the enhancement of tolerance to fungal diseases.

4. Metabolomics

Currently, mass spectrometry (MS) has become a highly sensitive tool used for the identification and quantification of metabolites. Nuclear magnetic resonance (NMR) and types of mass analyzers are commonly used for metabolomic studies, such as capillary electrophoresis mass spectrometry (CE-MS), gas chromatography mass spectrometry (GC–MS), liquid chromatography mass spectrometry (LC–MS), and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) [62].
The increasing number of sequenced genomes and the scalable metabolomics approaches have largely expanded the access to the metabolite repertoire of fungi [63]. The awareness that fungi are a source of beneficial compounds came up after Sir Alexander Fleming in 1928 discovered penicillin [64]. This first broad-spectrum antibiotic was produced by the fungus Penicillium notatum (syn. P. rubens) and was considered as the ‘wonder drug’ of World War II [65]. After this event, the study of microorganisms as sources for antibiotics gave rise to the golden era for the discovery of natural products from fungi [66]. Species of the genus Diaporthe, for instance, are well known as producers of several compounds (e.g., polyketides, indoles, and terpenes) with potential applications in pharmacology and biomedicine [67].
Besides that, metabolomics research of plant pathogenic fungi has gained attention, since it allows the identification of metabolites, their functions, and metabolic pathways involved in pathogenicity [68][69][70]. Moreover, metabolomics profiling of host plants has also been performed to elucidate plant responses to abiotic and biotic stresses and to evaluate plant adaptations to such conditions [71]. For instance, Dickinson et al. [72] applied the LC–MS method to investigate metabolite changes of Medicago tranuclata under drought stress and infection with F. oxysporum. The authors stated that under pathogen infection, an increase in flavonoids, sucrose relocation from leaves to roots, and a decrease in organic acids were observed. Also, Jones et al. [73] used a meta-analytical method based on GC–MS/MS, LC–MS/MS, and NMR to evaluate rice plants at different time points after infection by M. grisea. These authors proposed that the production of a large amount of alanine caused by fungi may lead to cell death and thereby promoting M. grisea infection, suggesting that metabolomics may help evaluating the overall effects of pathogen infection on plant hosts [74].
It has also been suggested that metabolomic profiling in fungal-plant interactions provide important information for the early diagnosis of several fungal plant pathogens [71][74][75]. Hu et al. [74] used the GC–MS method to analyze the metabolic profiling of strawberry infected with Botrytis cinerea and identified biomarkers in the early stage of disease development. Moreover, Zeiss et al. [75] analyzed the metabolic profiling of tomato plants infected with Ralstonia solanacearum using the LC–MS method and detected metabolites that may be used as biomarkers for an early infection diagnosis (e.g., phenylpropanoids, phenolic acids, and flavonoids). Additionally, plant metabolomics can also help identify and link genes associated with resistance to fungal pathogens. For instance, Kage et al. [76] have also reported an increase in the metabolite coumaroylagmatine in a tolerant wheat variety to Fusarium head blight. The analysis of these compounds and their metabolic pathways paved the way for the detection of a gene (agmatine coumaroyl transferase) that confers resistance against F. graminearum [76].
Several studies have been focused to identify a wide range of metabolites produced by species of Diaporthe with biotechnological applications [67]. Nevertheless, there is still a lack of metabolomic studies on the interaction between species of Diaporthe and their hosts. Therefore, metabolomic approaches should be performed in Diaporthe-infected plants to elucidate the metabolic pathways involved in pathogenicity, as well as secreted metabolites as potential biomarkers for early disease diagnosis [68]. Moreover, unveiling metabolic features responsible for plant survival under stress conditions (e.g., pathogen attack) could facilitate crop improvement for biotic-stress tolerance, through the application of unique metabolites in formulations [48][71].

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