A rust fungal effector binds plant DNA and modulates transcription

Ahmed, Boshir; Santos, Karen Cristine Gonçalves Dos; Sanchez, Ingrid Benerice; Pêtre, Benjamin; Lorrain, Cécile; Plourde, Mélodie B.; Duplessis, Sébastien; Desgagné‐Penix, Isabel; Germain, Hugo

doi:10.1038/s41598-018-32825-0

Cited by 41 publications

(40 citation statements)

References 65 publications

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“…The exclusive GO categories related to the selected variants have already been reported to be associated with resistance. Sphingolipid metabolism is intimately connected to programmed cell death [123][124][125] ; DNA topological change is a wider category with different implications in many biological processes, including responses to pathogens 126 ; differences in nitrogen compound transport might be related to the accumulation of this nutrient and its influence on resistance against pathogens 127 ; and phosphatidylinositol-mediated signaling includes important categories that also act on plants' responses to pathogens 125 . A considerable number of metabolic pathways related to both BACs and the selected scaffolds were also detected.…”

Section: Discussionmentioning

confidence: 99%

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Aono

Costa

Rody

et al. 2020

Preprint

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Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane. 3/154/15 10/15

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Section: Discussionmentioning

confidence: 99%

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Aono

Costa

Rody

et al. 2020

Preprint

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“…RNA isolation was carried out as described previously [2]. Briefly, total RNA was extracted from 4-day-old Arabidopsis plants grown in Petri dishes and quantified before sending for sequencing.…”

Section: Rna Extraction and Transcriptome Analysismentioning

confidence: 99%

“…During infection, plant pathogenic microbes deliver virulence proteins, known as effectors, into host cells to overcome plant immunity and promote parasitic colonization through the manipulation of cellular processes [1]. Once inside host tissues, effectors traffic to various cellular compartments where they interact with host proteins or nucleic acids and exert their virulence function [2][3][4][5][6]. To target these destinations, effectors possess domains or motifs in their sequence; for example, nucleus localized effectors can contain nuclear-localized signals (NLS) and some chloroplast localized effectors may carry a transit peptide [4,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…When expressed in N. benthamiana or A. thaliana, several CSEPs of Mlp have been shown to accumulate in diverse cell compartments of leaf tissues such as the nucleus, the nucleolus, chloroplasts, mitochondria, and plasmodesmata [5,11]. To date, a number of Mlp effectors have been identified as promoting plant susceptibility as determined by in planta assays [2,11].…”

Section: Introductionmentioning

confidence: 99%

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A Poplar Rust Effector Protein Associates with Protein Disulfide Isomerase and Enhances Plant Susceptibility

Madina

Rahman

Huang

et al. 2020

Biology

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Melampsora larici-populina (Mlp), the causal agent of Populus leaf rust, secretes an array of effectors into the host through the haustorium to gain nutrients and suppress immunity. The precise mechanisms by which these effectors promote virulence remain unclear. To address this question, we developed a transgenic Arabidopsis line expressing a candidate effector, Mlp124357. Constitutive expression of the effector increased plant susceptibility to pathogens. A GxxxG motif present in Mlp124357 is required for its subcellular localization at the vacuolar membrane of the plant cell, as replacement of the glycine residues with alanines led to the delocalization of Mlp124357 to the nucleus and cytoplasm. We used immunoprecipitation and mass spectrometry (MS) to identify Mlp124357 interaction partners. Only one of the putative interaction partners knock-out line caused delocalization of the effector, indicating that Arabidopsis protein disulfide isomerase-11 (AtPDI-11) is required for the effector localization. This interaction was further confirmed by a complementation test, a yeast-two hybrid assay and a molecular modeling experiment. Moreover, localization results and infection assays suggest that AtPDI-11 act as a helper for Mlp124357. In summary, our findings established that one of Mlp effectors resides at the vacuole surface and modulates plant susceptibility.

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“…Zhang et al, 2017). Interestingly, some of the described effectors have roles in RNA degradation or silencing, while others interact with DNA to regulate transcription (Ahmed et al, 2018;Yin et al, 2019).…”

Section: The Effectors Described Inmentioning

confidence: 99%

Identification of Candidate Effector Proteins from Fusarium graminearum during Infection of Arabidopsis thaliana Using Proximity-Dependant Biotin Identification (BioID)

Miltenburg¹

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Fusarium graminearum is a fungal pathogen that causes Fusarium head blight (FHB) in cereal crops. Identifying proteins that are secreted from pathogens to overcome plant defenses and cause disease, collectively known as effectors, can reveal new targets for fungicides or other control measures. Proximity-dependent biotin identification (BioID) was used to identify potential effector proteins secreted in planta by F. graminearum during the infection of Arabidopsis seedlings. BioID analysis revealed over 300 proteins from F. graminearum, of which 99 were considered to be candidate effector proteins (CEPs). A subset of CEPs was functionally characterized in wheat. Assays examining the ability of a CEP to induce cell death or affect the growth of a bacterial pathogen were performed to determine their role in plant defenses. The expression of four CEPs in wheat were found to alter bacterial growth, supporting their putative role as effector proteins promoting infection by F. graminearum.

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A rust fungal effector binds plant DNA and modulates transcription

Cited by 41 publications

References 65 publications

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

A Poplar Rust Effector Protein Associates with Protein Disulfide Isomerase and Enhances Plant Susceptibility

Identification of Candidate Effector Proteins from Fusarium graminearum during Infection of Arabidopsis thaliana Using Proximity-Dependant Biotin Identification (BioID)

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