This is the report from a BSPP Undergraduate ‘Vacation’ Bursary.
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Fusarium Head Blight (FHB) is a destructive fungal disease of globally important cereal crops such as wheat, maize, and barley. It results in staggering yield losses and poses a threat to animal and human health through the secretion of toxigenic metabolites such as deoxynivalenol (DON). The predominant causal agent of FHB is Fusarium graminearum (Fg). During infection, Fg secretes proteinaceous effectors where some of them can enter the host cells and target subcellular organelles. Identifying the host targets of these effectors is key to understanding how the pathogen manipulates plant immunity and thus it will help to devise novel and more durable resistant strategies to FHB.
In-silico prediction of secreted effector proteins combined with subcellular localisation inside plant cells is an active research area. Bioinformatic tools search for sequence signatures associated with protein secretion and/or subcellular localisation. Localizer, for example, uses the transit peptide sequences of plant proteins that localise in the mitochondrion, chloroplast, or nucleus to predict pathogen effector destinations. Accurate prediction of host subcellular localisation for fungal effectors requires laboratory validation of their destination(s). In-silico prediction and laboratory validation formed the two strands of my BSPP-funded summer project.
Undertaken in Dr Kim Hammond-Kosack’s lab at Rothamsted Research, my 12-week project was jointly supervised by Dr Martin Darino and Dr Dan Smith, who oversaw the molecular and computational parts of my project, respectively. Previously in the Hammond-Kosack lab, a candidate effector list was generated by Claire Kanja. Localizer was run on this list and two effectors were predicted with a subcellular localisation. Effector proteins FgSSP41 and FgSSP51 were predicted to localise in mitochondrion and chloroplast, respectively. The main aim of my project was to validate the mitochondrial localisation of FgSSP41.
To validate the mitochondrial localisation of FgSSP41, I cloned a construct consisting of the effector C-terminally fused to an mCherry fluorescent tag (FgSSP41-mCherry). I co-infiltrated the FgSSP41-mCherry construct with ScCox4-GFP, a mitochondrial marker in Nicotiana benthamiana leaves. As control, I co-infiltrated a construct only expressing mCherry with ScCox4-GFP. Five days post infection, I cut leaf discs of the different infiltrated leaves and I visualised fluorescence emission using confocal microscopy. FgSSP41-mCherry localises in a subcellular structure different from the mitochondria as there is no merge between the mCherry and GFP signals (Fig. 1a). In addition, FgSSP41 is responsible of the subcellular localisation as mCherry alone only localises in the nucleus and the cytoplasm (Fig. 1b). To test if FgSSP41 might localise in the chloroplast, I cloned a construct where FgSSP41 was C-terminally fused to GFP (FgSSP41-GFP). I co-infiltrated FgSSP41-GFP with a chloroplast marker protein fused to mCherry (NbpMSRA-mCherry). Merge between the mCherry and GFP signals indicated that FgSSP41 localises in the chloroplast (Fig. 1c).
Closely tied to my work in the lab, I used bioinformatic tools to expand the current subset of candidate effectors. Over the last five years, novel Fg strains have been sequenced, allowing us to work with novel genomes. My goal was to incorporate this data to generate a larger list of candidate effectors where new effectors with a predicted subcellular localization can be identified. I developed a bioinformatic pipeline with the help of Dr Dan Smith where I worked with proteome data from 18 fusaria genomes. The first step in the pipeline consisted of the identification of candidate secreted proteins. I used SignalP5, a software that predicts if a protein is secreted by the presence of a signal peptide. However, secreted proteins can also possess other domains that anchor the proteins to the plasma membrane. To remove secreted plasma membrane proteins, I used the webserver program BUSCA. Then, I used EffectorP to discriminate between apoplastic effectors and effectors that can be translocated into the host cell. Finally, I used Localizer to predict the subcellular localisation for those effectors that are translocated into the host cell. This produced a large dataset, so in my last few weeks at Rothamsted, I inputted this into an SQLite database. This will hopefully become the go to resource for future study on this project.
The breadth of my project meant that I gained skills ranging from molecular and cell biology techniques to working with databases. I particularly enjoyed working with the confocal microscope and seeing first-hand the power of bioimaging. Through creating the database, the project also allowed me to feel as if I made a valuable contribution to the group, which I was extremely fortunate to spend 12-weeks with.
In the future, I hope to use this breadth of experience to pursue a career in plant pathology. I would like to thank the team at Rothamsted, particularly Dan and Martin, for making my time so enjoyable, as well as the BSPP for providing the funding that made this wonderful opportunity possible.
Imperial College London