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Omics to Study Fungal Plant Pathogens
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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
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Subjects: Microbiology
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Table of Contents

    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).
    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.

    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].

    References

    1. Goffeau, A.; Barrell, B.G.; Bussey, H.; Davis, R.W.; Dujon, B.; Feldmann, H.; Galibert, F.; Hoheisel, J.D.; Jacq, C.; Johnston, M.; et al. Life with 6000 genes. Science 1996, 274, 546–567.
    2. Zhang, N.; Luo, J.; Bhattacharya, D. Advances in fungal phylogenomics and their impact on fungal systematics. Adv. Genet. 2017, 100, 309–328.
    3. Aylward, J.; Steenkamp, E.T.; Dreyer, L.L.; Roets, F.; Wingfield, B.D.; Wingfield, M.J. A plant pathology perspective of fungal genome sequencing. IMA Fungus 2017, 8, 1–15.
    4. Goodwin, S.; McPherson, J.D.; McCombie, W.R. Coming of age: Ten years of next-generation sequencing technologies. Nat. Rev. Genet. 2016, 17, 333–351.
    5. Yang, Y.; Yao, X.; Xhang, X.; Zou, H.; Chen, J.; Fang, B.; Huang, L. Draft genome sequence of Diaporthe batatatis causing dry rot disease in sweet potato. Plant Dis. 2022, 106, 737–740.
    6. Hill, R.; Leitch, I.J.; Gaya, E. Targeting Ascomycota genomes: What and how big? Fungal Biol. Rev. 2021, 36, 52–59.
    7. Morales-Cruz, A.; Amrine, K.C.; Blanco-Ulate, B.; Lawrence, D.P.; Travadon, R.; Rolshausen, P.E.; Baumgartner, K.; Cantu, D. Distinctive expansion of gene families associated with plant cell wall degradation, secondary metabolism, and nutrient uptake in the genomes of grapevine trunk pathogens. BMC Genom. 2015, 16, 469.
    8. Savitha, J.; Bhargavi, S.D.; Praveen, V.K. Complete genome sequence of the endophytic fungus Diaporthe (Phomopsis) ampelina. Genome Announc. 2016, 4, e00477.
    9. Hilário, S.; Gonçalves, M.F.M.; Fidalgo, C.; Tacão, M.; Alves, A. Genome Analyses of Two Blueberry Pathogens: Diaporthe amygdali CAA958 and Diaporthe eres CBS 160.32. J. Fungi 2022, 8, 804.
    10. Li, S.; Song, Q.; Martins, A.M.; Cregan, P. Draft genome sequence of Diaporthe aspalathi isolate MS-SSC91, a fungus causing stem canker in soybean. Genom. Data 2016, 7, 262–263.
    11. Fang, X.; Qin, K.; Li, S.; Han, S.; Zhu, T. Whole genome sequence of Diaporthe capsici, a new pathogen of walnut blight. Genomics 2020, 112, 3751–3761.
    12. Mena, E.; Garaycochea, S.; Stewart, S.; Montesano, M.; Ponce De León, I. Comparative genomics of plant pathogenic Diaporthe species and transcriptomics of Diaporthe caulivora during host infection reveal insights into pathogenic strategies of the genus. BMC Genom. 2022, 23, 175.
    13. Huang, L.; Zhang, X.; Yang, Y.; Zou, H.; Fang, B.; Liu, W. High-Quality genome resource of Diaporthe destruens causing foot rot disease of sweet potato. Plant Dis. 2021, 105, 3279–3281.
    14. Gai, Y.; Xiong, T.; Xiao, X.; Li, P.; Zeng, Y.; Li, L.; Riely, B.K.; Li, H. The Genome Sequence of the Citrus Melanose Pathogen Diaporthe citri and Two Citrus-Related Diaporthe Species. Phytopathology 2021, 111, 779–783.
    15. Liu, X.Y.; Chaisiri, C.; Lin, Y.; Yin, W.X.; Luo, C.X. Whole-Genome sequence of Diaporthe citri Isolate NFHF-8-4, the causal Agent of Citrus melanose. Mol. Plant-Microbe Interact. 2021, 34, 845–847.
    16. Wang, X.; Dong, H.; Lan, J.; Liu, Y.; Liang, K.; Lu, Q.; Fang, Z.; Liu, P. High-quality genome resource of the pathogen of Diaporthe (Phomopsis) phragmitis causing kiwifruit soft rot. Mol. Plant-Microbe Interact. 2021, 34, 218–221.
    17. Ali, S.; Renderos, W.; Bevis, E.; Hebb, J.; Abbasi, P.A. Diaporthe eres causes stem cankers and death of young apple rootstocks in Canada. Can. J. Plant Pathol. 2020, 42, 218–227.
    18. Baroncelli, R.; Scala, F.; Vergara, M.; Thon, M.R.; Ruocco, M. Draft whole-genome sequence of the Diaporthe helianthi 7/96 strain, causal agent of sunflower stem canker. Genom. Data 2016, 10, 151–152.
    19. Emanuel, I.B.; Konkel, Z.M.; Scott, K.L.; Valero David, G.E.; Slot, J.C.; Peduto Hand, F. Whole-Genome Sequence Data for the Holotype Strain of Diaporthe ilicicola, a Fungus Associated with Latent Fruit Rot in Deciduous Holly. Microbiol. Resour. Announc. 2022, 11, e00631-22.
    20. Li, S.; Darwish, O.; Alkharouf, N.W.; Musungu, B.; Matthews, B.F. Analysis of the genome sequence of Phomopsis longicolla: A fungal pathogen causing Phomopsis seed decay in soybean. BMC Genom. 2017, 18, 688.
    21. Li, S.; Song, Q.; Ji, P.; Cregan, P. Draft genome sequence of Phomopsis longicolla type strain TWH P74, a fungus causing Phomopsis seed decay in soybean. Genome Announc. 2015, 3, e00010-15.
    22. Heng, Z.; You, Q.; Li, Z.; Sun, B.; Xu, X.; Li, Y.; Li, Z.; Wang, H.; Gong, C.; Xu, X.; et al. The first genome sequence of Phomopsis vexans: A fungal pathogen causing Phomopsis blight in eggplant. Biologia 2023, 78, 543–548.
    23. Tulsook, K.; Isarangkul, D.; Sriubolmas, N.; Kittakoop, P.; Wiyakrutta, S. Draft genome sequence of Diaporthe sp. strain HANT25, an endophytic fungus producing mycoepoxydiene. Microbiol. Resour. Announc. 2020, 9, e00805-20.
    24. Vieira, A.; Silva, D.N.; Várzea, V.; Paulo, O.S.; Batista, D. Genome-wide signatures of selection in Colletotrichum kahawae reveal candidate genes potentially involved in pathogenicity and aggressiveness. Front. Microbiol. 2019, 10, 1374.
    25. Hartmann, F.E. Using structural variants to understand the ecological and evolutionary dynamics of fungal plant pathogens. New Phytol. 2022, 234, 43–49.
    26. Boufleur, T.R.; Massola Júnior, N.S.; Tikami, Í.; Sukno, S.A.; Thon, M.R.; Baroncelli, R. Identification and comparison of Colletotrichum secreted effector candidates reveal two independent lineages pathogenic to soybean. Pathogens 2021, 10, 1520.
    27. Selin, C.; De Kievit, T.R.; Belmonte, M.F.; Fernando, W.D. Elucidating the role of effectors in plant-fungal interactions: Progress and challenges. Front. Microbiol. 2016, 7, 600.
    28. Wang, Y.; Wu, J.; Yan, J.; Guo, M.; Xu, L.; Hou, L.; Zou, Q. Comparative genome analysis of plant ascomycete fungal pathogens with different lifestyles reveals distinctive virulence strategies. BMC Genom. 2022, 23, 34.
    29. Ball, B.; Langille, M.; Geddes-McAlister, J. Fun(gi)omics: Advanced and diverse technologies to explore emerging fungal pathogens and define mechanisms of antifungal resistance. MBio 2020, 11, e01020-20.
    30. Félix, C.; Meneses, R.; Gonçalves, M.F.M.; Tilleman, L.; Duarte, A.S.; Jorrín-Novo, J.V.; Van de Peer, Y.; Deforce, D.; Van Nieuwerburgh, F.; Esteves, A.C.; et al. A multi-omics analysis of the grapevine pathogen Lasiodiplodia theobromae reveals that temperature affects the expression of virulence-and pathogenicity-related genes. Sci. Rep. 2019, 9, 13144.
    31. Gonçalves, M.F.M.; Nunes, R.B.; Tilleman, L.; Van de Peer, Y.; Deforce, D.; Van Nieuwerburgh, F.; Esteves, A.C.; Alves, A. Dual RNA sequencing of Vitis vinifera during Lasiodiplodia theobromae infection unveils host-pathogen interactions. Int. J. Mol. Sci. 2019, 20, 6083.
    32. Lowe, R.; Shirley, N.; Bleackley, M.; Dolan, S.; Shafee, T. Transcriptomics technologies. PLoS Comput. Biol. 2017, 13, e1005457.
    33. Wang, Z.; Gerstein, M.; Snyder, M. RNA-Seq: A revolutionary tool for transcriptomics. Nat. Rev. Genet. 2009, 10, 57–63.
    34. Naidoo, S.; Visser, E.A.; Zwart, L.; Du Toit, Y.; Bhadauria, V.; Shuey, L.S. Dual RNA-seq to elucidate the plant-pathogen duel. Curr. Issues Mol. Biol. 2017, 27, 127–142.
    35. Zhang, W.; Yan, J.; Li, X.; Xing, Q.; Chethana, K.T.; Zhao, W. Transcriptional response of grapevine to infection with the fungal pathogen Lasiodiplodia theobromae. Sci. Rep. 2019, 9, 5387.
    36. Westermann, A.J.; Gorski, S.A.; Vogel, J. Dual RNA-seq of pathogen and host. Nat. Rev. Microbiol. 2012, 10, 618.
    37. Gao, L.; Wang, Y.; Li, Z.; Zhang, H.; Ye, J.; Li, G. Gene expression changes during the gummosis development of peach shoots in response to Lasiodiplodia theobromae infection using RNA-Seq. Front. Physiol. 2016, 7, 170.
    38. Li, H.; Hu, R.; Fan, Z.; Chen, Q.; Jiang, Y.; Huang, W.; Tao, X. Dual RNA-Seq reveals the genome-wide expression profiles during the compatible and incompatible interactions between Solanum tuberosum and Phytophthora infestans. Front. Plant Sci. 2022, 13, 817199.
    39. Guan, Y.; Chen, M.; Ma, Y.; Du, Z.; Yuan, N.; Li, Y.; Xiao, J.; Zhang, Y. Whole-genome and time-course dual RNA-Seq analyses reveal chronic pathogenicity-related gene dynamics in the ginseng rusty root rot pathogen Ilyonectria robusta. Sci. Rep. 2020, 10, 1586.
    40. Meyer, E.; Shuey, L.S.; Naidoo, S.; Mamni, T.; Berger, D.K.; Myburg, A.A.; van den Berg, N.; Naidoo, S. Dual RNA-sequencing of Eucalyptus nitens during Phytophthora cinnamomi challenge reveals pathogen and host factors influencing compatibility. Front. Plant Sci. 2016, 7, 191.
    41. Zamora-Ballesteros, C.; Pinto, G.; Amaral, J.; Valledor, L.; Alves, A.; Diez, J.J.; Martín-García, J. Dual RNA-sequencing analysis of resistant (Pinus pinea) and susceptible (Pinus radiata) hosts during Fusarium circinatum challenge. Int. J. Mol. Sci. 2021, 22, 5231.
    42. Książkiewicz, M.; Rychel-Bielska, S.; Plewiński, P.; Nuc, M.; Irzykowski, W.; Jędryczka, M.; Krajewski, P. The resistance of narrow-leafed lupin to Diaporthe toxica is based on the rapid activation of defense response genes. Int. J. Mol. Sci. 2021, 22, 574.
    43. Elverson, T.R.; Kontz, B.J.; Markell, S.G.; Harveson, R.M.; Mathew, F.M. Quantitative PCR assays developed for Diaporthe helianthi and Diaporthe gulyae for phomopsis stem canker diagnosis and germplasm screening in sunflower (Helianthus annuus). Plant Dis. 2020, 104, 793–800.
    44. Hosseini, B.; Voegele, R.T.; Link, T.I. Establishment of a quadruplex real-time PCR assay to distinguish the fungal pathogens Diaporthe longicolla, D. caulivora, D. eres, and D. novem on soybean. PLoS ONE 2020, 16, e0257225.
    45. Fujiwara, K.; Kobayashi, Y.O.; Usui, M.; Nishioka, K.; Nakamura, M.; Kawano, S.; Okada, Y.; Kobayashi, A.; Miyasaka, A.; Hirayae, K.; et al. Real-Time PCR assay for the diagnosis and quantification of co-infections by Diaporthe batatas and Diaporthe destruens in sweet potato. Front. Plant Sci. 2020, 12, 694053.
    46. Mena, E.; Stewart, S.; Montesano, M.; Ponce de León, I. Soybean stem canker caused by Diaporthe caulivora; pathogen diversity, colonization process, and plant defense activation. Front. Plant Sci. 2020, 10, 1733.
    47. Qi, H.; Jiang, Z.; Zhang, K.; Yang, S.; He, F.; Zhang, Z. PlaD: A transcriptomics database for plant defense responses to pathogens, providing new insights into plant immune system. Genom. Proteom. Bioinform. 2018, 16, 283–293.
    48. Rani, M.; Mangat, H.K.; Pathak, R.K.; Yadav, I.S. Harnessing the potential of omics for prevention and management of the complex crop plant’s diseases. J. Proteins Proteom. 2021, 12, 227–245.
    49. Yang, H.; Luo, P. Changes in photosynthesis could provide important insight into the interaction between wheat and fungal pathogens. Int. J. Mol. Sci. 2021, 22, 8865.
    50. Chisholm, S.T.; Coaker, G.; Day, B.; Staskawicz, B.J. Host-microbe interactions: Shaping the evolution of the plant immune response. Cell 2006, 124, 803–814.
    51. Kim, S.T.; Kim, S.G.; Hwang, D.H.; Kang, S.Y.; Kim, H.J.; Lee, B.H.; Lee, J.J.; Kang, K.Y. Proteomic analysis of pathogen-responsive proteins from rice leaves induced by rice blast fungus, Magnaporthe grisea. Proteomics 2004, 4, 3569–3578.
    52. Fang, X.; Chen, W.; Xin, Y.; Zhang, H.; Yan, C.; Yu, H.; Liu, H.; Xiao, W.; Wang, S.; Zheng, G.; et al. Proteomic analysis of strawberry leaves infected with Colletotrichum fragariae. J. Proteom. 2012, 75, 4074–4090.
    53. Van Loon, L.C.; Rep, M.; Pieterse, C.M. Significance of inducible defense-related proteins in infected plants. Annu. Rev. Phytopathol. 2006, 44, 135–162.
    54. Manghwar, H.; Hussain, A.; Ali, Q.; Saleem, M.H.; Abualreesh, M.H.; Alatawi, A.; Ali, S.; Munis, M.F.H. Disease severity, resistance analysis, and expression profiling of pathogenesis-related protein genes after the inoculation of Fusarium equiseti in wheat. Agronomy 2021, 11, 2124.
    55. Li, Y.; Feng, Y.; Lü, Q.; Yan, D.; Liu, Z.; Zhang, X. Comparative proteomic analysis of plant-pathogen interactions in resistant and susceptible poplar ecotypes infected with Botryosphaeria dothidea. Phytopathology 2019, 109, 2009–2021.
    56. Yang, F.E.N.; Jensen, J.D.; Svensson, B.; Jørgensen, H.J.; Collinge, D.B.; Finnie, C. Secretomics identifies Fusarium graminearum proteins involved in the interaction with barley and wheat. Mol. Plant Pathol. 2012, 13, 445–453.
    57. Huser, A.; Takahara, H.; Schmalenbach, W.; O’Connell, R. Discovery of pathogenicity genes in the crucifer anthracnose fungus Colletotrichum higginsianum, using random insertional mutagenesis. Mol. Plant-Microbe Interact. 2009, 22, 143–156.
    58. Reuveni, M.; Sheglov, N.; Eshel, D.; Prusky, D.; Ben-Arie, R. Virulence and the production of endo-1, 4-β-glucanase by Isolates of Alternaria alternata involved in the moldy-core disease of apples. J. Phytopathol. 2007, 155, 50–55.
    59. Zipfel, C. Plant pattern-recognition receptors. Trends Immunol. 2014, 35, 345–351.
    60. Silva, M.D.C.; Guerra-Guimarães, L.; Diniz, I.; Loureiro, A.; Azinheira, H.; Pereira, A.P.; Tavares, S.; Batista, D.; Várzea, V. An overview of the mechanisms involved in Coffee-Hemileia vastatrix interactions: Plant and pathogen perspectives. Agronomy 2022, 12, 326.
    61. Balotf, S.; Wilson, R.; Tegg, R.S.; Nichols, D.S.; Wilson, C.R. Shotgun proteomics as a powerful tool for the study of the proteomes of plants, their pathogens, and plant-pathogen interactions. Proteomes 2022, 10, 5.
    62. Spina, R.; Saliba, S.; Dupire, F.; Ptak, A.; Hehn, A.; Piutti, S.; Poinsignon, S.; Leclerc, S.; Bouguet-Bonnet, S.; Laurain-Mattar, D. Molecular identification of endophytic bacteria in Leucojum aestivum in vitro culture, NMR-based metabolomics study and LC-MS analysis leading to potential Amaryllidaceae alkaloid production. Int. J. Mol. Sci. 2021, 22, 1773.
    63. Keller, N.P. Fungal secondary metabolism: Regulation, function and drug discovery. Nat. Rev. Microbiol. 2019, 17, 167–180.
    64. Houbraken, J.; Frisvad, J.C.; Samson, R.A. Fleming’s penicillin producing strain is not Penicillium chrysogenum but P. rubens. IMA Fungus 2011, 2, 87–95.
    65. Quinn, R. Rethinking antibiotic research and development: World War II and the penicillin collaborative. Am. J. Public Health 2013, 103, 426–434.
    66. Gonçalves, M.F.M.; Esteves, A.C.; Alves, A. Marine Fungi: Opportunities and Challenges. Encyclopedia 2022, 2, 559–577.
    67. Xu, T.-C.; Lu, Y.-H.; Wang, J.-F.; Song, Z.-Q.; Hou, Y.-G.; Liu, S.-S.; Liu, C.-S.; Wu, S.-H. Bioactive secondary metabolites of the genus Diaporthe and anamorph Phomopsis from terrestrial and marine habitats and endophytes: 2010–2019. Microorganisms 2021, 9, 217.
    68. Chen, H.; Singh, H.; Bhardwaj, N.; Bhardwaj, S.K.; Khatri, M.; Kim, K.H.; Peng, W. An exploration on the toxicity mechanisms of phytotoxins and their potential utilities. Crit. Rev. Environ. Sci. Technol. 2022, 52, 395–435.
    69. Feussner, I.; Polle, A. What the transcriptome does not tell—Proteomics and metabolomics are closer to the plants’ patho-phenotype. Curr. Opin. Plant Biol. 2015, 26, 26–31.
    70. Tan, K.C.; Ipcho, S.V.; Trengove, R.D.; Oliver, R.P.; Solomon, P.S. Assessing the impact of transcriptomics, proteomics and metabolomics on fungal phytopathology. Mol. Plant Pathol. 2010, 10, 703–771.
    71. Mashabela, M.D.; Masamba, P.; Kappo, A.P. Metabolomics and Chemoinformatics in Agricultural Biotechnology Research: Complementary Probes in Unravelling New Metabolites for Crop Improvement. Biology 2022, 11, 1156.
    72. Dickinson, E.; Rusilowicz, M.J.; Dickinson, M.; Charlton, A.J.; Bechtold, U.; Mullineaux, P.M.; Wilson, J. Integrating transcriptomic techniques and k-means clustering in metabolomics to identify markers of abiotic and biotic stress in Medicago truncatula. Metabolomics 2018, 14, 126.
    73. Jones, O.A.H.; Griffin, J.L.; Jung, Y.H.; Shibato, J.; Rakwal, R.; Agrawal, G.K.; Jwa, N.S. Using metabolic profiling to assess plant-pathogen interactions: An example using rice (Oryza sativa) and the blast pathogen Magnaporthe grisea. Eur. J. Plant Pathol. 2011, 129, 539–554.
    74. Hu, Z.; Chang, X.; Dai, T.; Li, L.; Liu, P.; Wang, G.; Liu, P.; Huang, Z.; Liu, X. Metabolic profiling to identify the latent infection of strawberry by Botrytis cinerea. Evol. Bioinform. 2019, 15, 1176934319838518.
    75. Zeiss, D.R.; Mhlongo, M.I.; Tugizimana, F.; Steenkamp, P.A.; Dubery, I.A. Metabolomic profiling of the host response of tomato (Solanum lycopersicum) following infection by Ralstonia solanacearum. Int. J. Mol. Sci. 2019, 20, 3945.
    76. Kage, U.; Karre, S.; Kushalappa, A.C.; McCartney, C. Identification and characterization of a fusarium head blight resistance gene TaACT in wheat QTL-2DL. Plant Biotechnol. J. 2017, 15, 447–457.
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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 03 June 2023).
      Hilário S, Gonçalves MFM. Omics to Study Fungal Plant Pathogens. Encyclopedia. Available at: https://encyclopedia.pub/entry/42580. Accessed June 03, 2023.
      Hilário, Sandra, Micael F. M. Gonçalves. "Omics to Study Fungal Plant Pathogens" Encyclopedia, https://encyclopedia.pub/entry/42580 (accessed June 03, 2023).
      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.
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