| Title of Project |
| Identification of the subcellular localisation of fungal pathogen effectors |
| This project going to be… |
| Experimental (lab/field) remote/virtual |
| Full Name of Supervisor |
| Martin Darino |
| Institution Department and Address |
| Department of Biointeractions and Crop Protection, Rothamsted Research, West Common Harpenden, Hertfordshire AL5 2JQ United Kingdom Map It |
| Telephone |
| +44 1582763133 |
| martin.darino@rothamsted.ac.uk |
| Position held |
| Post-Doctoral Research Scientist |
| Full name of the day to day supervisor and/or arrangements for supervision |
| Dr. Martin Darino will be the main supervisor taking care of the day to day supervision. He possesses deep experience in fungal transformation, molecular and cell biology techniques developed it earlier (Darino et al., 2021). Dr. Darino will help the student in the cloning of the mitochondria marker and evaluation in N. benthamiana. He will provide the Fusarium strains expressing FgSSP41-mCherry and support the student in the wheat infections and confocal microscopy. Dr. Dan Smith will support the student with the evaluation of different bioinformatics approaches to identify new Fusarium effectors with subcellular localization. He is bioinformatic scientist in the institute with extensive experience in bioinformatic applied to study different plant – pathogen interactions. Finally, every 15 days it will be a meeting to discuss the results with Dr. Kim Hammond-Kosack, deputy head of the department. |
| Date of Project Commencement |
| 04/07/2022 |
| Duration (weeks) |
| 10 |
| Brief Description of Project |
| Fusarium graminearum (Fg) is a devastating fungal disease of wheat and a serious health hazard due to the contamination of the crop by harmful toxins. Fg is the predominant causal agent of Fusarium Head Blight (FHB). The pathogen secretes proteinaceous effectors that suppress the plant’s immune response to promote infection. After secretion some effectors exert their functions in the apoplast, whilst others can be translocated to the cytoplasm. Once inside the host cell, effectors can target different subcellular structures such as the nucleus, endoplasmic reticulum, Golgi and organelles. For example, one Fg effector (FgSSP41) is now thought to localise to the mitochondria of Nicotiana benthamiana leaves. The identification of the subcellular localisation of other FgSSP effectors is essential to identify the range of potential effector functions and their intended host targets. Using this new knowledge will help to devise novel, durable, crop disease control strategies. The aim of this project is to validate the mitochondria localisation of FgSSP41 and identify the intracellular localisation of other early in planta expressed FgSSP effectors previously revealed using different bioinformatic approaches. To verify or refute mitochondrial localisation of FgSSP41, the student will clone a mitochondria marker and evaluate co-localisation with FgSSP41 in N. benthamiana using Agrobacterium infiltration and confocal microscopy. Then, already available Fg strains expressing FgSSP41 fused to mCherry will be used to infect wheat, the native host, to explore subcellular localisation. Finally, different bioinformatic approaches including Localizer (https://localizer.csiro.au/), EffectorP (https://effectorp.csiro.au/) and SignalP 6.0 (https://services.healthtech.dtu.dk/service.php?SignalP) on the Fg genome / Fg pan-genome will be used to identify FgSSP effectors with sequence signatures that suggest localisation in the nucleus, chloroplasts and/or mitochondria. This project is laboratory based and is suitable for students looking to develop a career in pathogens infecting plants, animals, or humans. In addition, the bioinformatic analysis can be done remotely in case of new COVID restrictions. The student will gain experience in different molecular biology techniques like primer design, PCR, cloning and bacteria transformation; Agrobacterium transformation and N. benthamiana infiltrations; aseptic microbiology techniques, Fusarium infection of wheat plants, confocal microscopy and learn about different bioinformatic approaches to study effectors. The project will be based at Rothamsted Research in Harpenden, Hertfordshire, a world leading institute in crop sciences. In addition, this project will allow the student to improve their presentation and problem solving skills as well as provide the possibility to establish new connections for your future career development in academia or in industry. |
| Attach the recommended reading for the project |
| Literature about plant pathogen effectors: Figueroa et al., 2021 . Tactics of host manipulation by intracellular effectors from plant pathogenic fungi. Curr. Opin. in Plant Biol. 62:102054. https://doi.org/10.1016/j.pbi.2021.102054. Tzelepis et al., 2021 . Plant mitochondria and chloroplasts are targeted by the Rhizoctonia solani RsCRP1 effector. Biochem Biophys Res Commun. https://doi: 10.1016/j.bbrc.2021.01.019. Paper about the techniques to be used in this project: Darino et al., 2020. Ustilago maydis effector Jsi1 interacts with Topless corepressor, hijacking plant jasmonate/ethylene signaling. New Phytologist, 229 (6), 3393-3407. https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.17116 Wood et al., 2020 . Functional evaluation of a homologue of plant rapid alkalinisation factor (RALF) peptides in Fusarium graminearum. Fungal Biol., 124(9), 753-765. https://doi.org/10.1016/j.funbio.2020.05.001. Jiang et al., 2020 . An orphan protein of Fusarium graminearum modulates host immunity by mediating proteasomal degradation of TaSnRK1α. Nat. Commun. 11: 4382. https://doi.org/10.1038/s41467-020-18240-y. Papers about the bioinformatics tools to be used in the project: Sperschneider et al., 2017. LOCALIZER: subcellular localization prediction of both plant and effector proteins in the plant cell. Sci Rep 7: 44598. https://doi.org/10.1038/srep44598. Sperschneider et al., 2016. EffectorP: predicting fungal effector proteins from secretomes using machine learning. New Phytol 210(2):743-61. https://doi: 10.1111/nph.13794. Teufel et al., 2022. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat. Biotechnol. https://doi.org/10.1038/s41587-021-01156-3 A review about the most important fungal pathogens: Dean et al., 2012. The top 10 fungal pathogens in molecular plant pathology. Molecular Plant Pathology 13: 414–430. https://doi.org/10.1111/j.1364-3703.2011.00783.x |
