Dataset Information

Macrophomina phaseolina Secretome LC-ESI-MS-MS

ABSTRACT: Macrophomina phaseolina is a global devastating necrotrophic fungal pathogen. It causes charcoal rot disease in more than 500 host plants. It is essential to understand the host microbe interaction and the diseases pathogenesis which can ensure global agricultural crop production and security. An array of virulence factors of M. phaseolina were identified which were found to be involved in pathogenesis of other plant pathogenic fungi also. In conclusion the present study has provided a better understanding of how necrotrophic fungi M. phaseolina modulates host plant defensive processes.

INSTRUMENT(S): Xevo G2 XS Tof

ORGANISM(S): Macrophomina Phaseolina Ms6

TISSUE(S): Fungal Cell

SUBMITTER: NILANJAN SINHA

LAB HEAD: Prof. Sanjay Ghosh

PROVIDER: PXD032749 | Pride | 2022-05-20

REPOSITORIES: Pride

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Publications

Secretome Analysis of Macrophomina phaseolina Identifies an Array of Putative Virulence Factors Responsible for Charcoal Rot Disease in Plants.

Sinha Nilanjan N Patra Sourav Kumar SK Ghosh Sanjay S

Frontiers in microbiology 20220405

Macrophomina phaseolina is a global devastating necrotrophic fungal pathogen. It causes charcoal rot disease in more than 500 host plants including major food crops, pulse crops, fiber crops, and oil crops. Despite having the whole-genome sequence of M. phaseolina, understanding the M. phaseolina genome-based plant-pathogen interactions is limited in the absence of direct experimental proof of secretion. Thus, it is essential to understand the host-microbe interaction and th ...[more]

PMID: 35479629

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Project description:Worldwide, 20-25% of all harvested fruit and vegetables are lost annually in the field and throughout the postharvest supply handling chain due to spoilage by fungal pathogens. Most impactful postharvest pathogens exhibit necrotrophic lifestyles, resulting in rotting of the host tissues and complete loss of marketable commodities. Necrotrophic fungi can readily infect ripe fruit leading to the rapid establishment of disease symptoms. However, these pathogens generally fail to infect unripe fruit, or remain quiescent until host and environmental conditions stimulate a successful infection. Current research on necrotrophic infections of fruit was mainly focused on the host by characterizing genetic and physicochemical factors that inhibit or promote the disease. However, the pathogenicity and virulence strategies employed by necrotrophic pathogens in ripe and unripe fruit are mostly understudied. Here, we provide a first comparative transcriptomics study of fungal postharvest pathogens: Botrytis cinerea, Rhizopus stolonifer and Fusarium acuminatum, all of which display necrotrophic behavior when infecting fruit. We de novo assembled and annotated the transcriptomes of R. stolonifer, and F. acuminatum and performed a differential gene expression analysis comparing the three fungal transcriptomes during fruit infection with that of fungal in-vitro growth. Analysis of the differentially expressed genes for enrichment of functional annotations revealed shared strategies of the three fungi during infection of compatible (ripe fruit) and incompatible (unripe fruit) hosts. We furthermore selected candidate genes that are involved in these strategies to characterize their expression during infection of unripe and ripe-like fruit of the non-ripening (nor) tomato mutant, both of which are physiologically and biochemically similar to unripe wildtype fruit. By enabling a better understanding of fungal necrotrophic infection strategies, we move closer to generating accurate models of fruit diseases and development of early detection tools and effective management strategies.

Project description:Alternaria brassicicola is a successful saprophyte and necrotrophic plant pathogen. Several A. brassicicola genes have been characterized as virulence factors affecting pathogenesis of Brassica species. To study regulatory mechanisms of pathogenesis, we mined 421 genes in silico encoding putative transcription factors in a machine-annotated, draft genome sequence of A. brassicicola. Targeted gene disruption mutants for 114 of the genes with proteins predicted to contain at least one putative zinc-finger domain were produced and functionally analyzed. Six of these genes were associated with pathogenesis. Disruption mutants corresponding to five of the genes were ≥50% less virulent than the wild type. Unexpectedly, the mutants of one gene (designated Amr1) were 100% more virulent. Amr1 is a homolog of Cmr1, a transcription factor previously found to regulate melanin biosynthesis in several fungi. Gene deletion mutants (Δamr1) were created and their phenotypes characterized. The Δamr1 mutants utilized pectin as a carbon source more efficiently than the wild type, were melanin-deficient, and more sensitive to UV light and glucanase digestion. The Amr1 protein was localized in the nuclei of hyphae and in highly melanized conidia during the late stage of plant pathogenesis. RNA-seq analysis revealed that three genes in the melanin biosynthesis pathway, along with the deleted Amr1 gene, were not expressed in the mutants. In contrast, many hydrolytic enzyme-coding genes were expressed at much higher levels in the mutants than in the wild type during pathogenesis. The results of this study suggested that only a small number of transcription factors with zinc finger domains are needed to maintain strong virulence. Furthermore, a gene important for survival in nature negatively affected virulence, probably by less efficient use of pectin. We speculate that the functions of the Amr1 gene are important to the success of A. brassicicola as a competitive saprophyte and plant parasite gene expression profile comparisons between wild type and a transcription factor mutant during the late stage of host infection

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Dataset Information

Macrophomina phaseolina Secretome LC-ESI-MS-MS

Publications

Secretome Analysis of <i>Macrophomina phaseolina</i> Identifies an Array of Putative Virulence Factors Responsible for Charcoal Rot Disease in Plants.

Similar Datasets

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