All Aphid Effectors on DEK
Lead Research Organisation:
John Innes Centre
Department Name: Crop Genetics
Abstract
In recent years it has become clear that cross-kingdom RNA plays a key role in mediating host-parasite interactions. Research on cross-kingdom RNA has mostly focused on microbial parasites and small RNAs. However, we showed in a recent publication that aphid insect pests use long non-coding RNAs (lncRNAs) as virulence factors, i.e. the peach-potato aphid/green peach aphid (GPA) Myzus persicae, which is a global pest, translocates at least one long lncRNA virulence factor, named Ya1, into divergent plant species, including crops. We show that Ya1 migrates systemically throughout these plant species and promotes GPA colonization on plants.
Aphids are sap-sucking insects that are vectors of a range of viruses and bacterial plant pathogens, and also transmit viroids, which consists of only nucleic acid without a protein coat. Our finding that aphids translocate their own nucleic acids that modulate plant processes is novel and mechanisms involved could contribute to the reasons of these sap-sucking insects being such efficient pathogen vectors.
In this proposal, we wish to further understand what the aphid nucleic acids do in plants and how they interfere with plant processes. We will focus on aphid lncRNA Ya1 and then also study at least two other aphid lncRNA virulence factors in plants. Data so far suggest that the aphid RNAs interfere with aspects of nucleus-chloroplast communications, including plant defense responses to biotic stress, that so far are still poorly understood. We will use the aphid RNAs to further dissect these processes.
We also found that the aphid Ya lncRNAs migrate away from aphid feeding site to distal locations of plants. It has been known for a while that nucleic acids, including those of aphid-transmitted viruses and viroids, migrate throughout plants, but the mechanisms involved have remained largely obscure. In this proposal, we will also use the aphid lncRNAs to better understand processes involved in long-distance migration of nucleic acids in plants.
The proposed research will focus on dissecting fundamental plant processes that are likely to play important roles in the ability of aphids to colonize plants and the efficiency of virus/viroid transmission by these insects. In the longer term, we anticipate that this research project will provide the basis for future research that is focused on identification of benign methods to manage outbreaks of aphids and aphid-transmitted pathogens in crops.
Aphids are sap-sucking insects that are vectors of a range of viruses and bacterial plant pathogens, and also transmit viroids, which consists of only nucleic acid without a protein coat. Our finding that aphids translocate their own nucleic acids that modulate plant processes is novel and mechanisms involved could contribute to the reasons of these sap-sucking insects being such efficient pathogen vectors.
In this proposal, we wish to further understand what the aphid nucleic acids do in plants and how they interfere with plant processes. We will focus on aphid lncRNA Ya1 and then also study at least two other aphid lncRNA virulence factors in plants. Data so far suggest that the aphid RNAs interfere with aspects of nucleus-chloroplast communications, including plant defense responses to biotic stress, that so far are still poorly understood. We will use the aphid RNAs to further dissect these processes.
We also found that the aphid Ya lncRNAs migrate away from aphid feeding site to distal locations of plants. It has been known for a while that nucleic acids, including those of aphid-transmitted viruses and viroids, migrate throughout plants, but the mechanisms involved have remained largely obscure. In this proposal, we will also use the aphid lncRNAs to better understand processes involved in long-distance migration of nucleic acids in plants.
The proposed research will focus on dissecting fundamental plant processes that are likely to play important roles in the ability of aphids to colonize plants and the efficiency of virus/viroid transmission by these insects. In the longer term, we anticipate that this research project will provide the basis for future research that is focused on identification of benign methods to manage outbreaks of aphids and aphid-transmitted pathogens in crops.
Technical Summary
The long-term goal of research in the Hogenhout lab is to identify key processes in plant-microbe and plant-insect interactions that enable pathogens and insects to colonize plants. The objective of this project is to further dissect the role of aphid lncRNAs, and specifically Ya genes, in aphid-plant interactions. The proposed research is based on a recent publication (Chen et al., 2020. PNAS) showing that the peach-potato aphid/green peach aphid (GPA) Myzus persicae translocates the Ya1 long non-coding RNA (lncRNA) into plants and that this lncRNA migrates systemically and promotes aphid fecundity. In this project we want to further investigate if GPA Ya1 and other Ya lncRNA form complexes with another GPA effector, MpC002. Moreover, we will investigate if MpC002-Ya complexes interact with plant non-histone DNA-binding chromatin factors and interfere with the splicing of specific transcripts leading to altered JA, SA signaling and nucleus-chloroplast communications and promotion of aphid colonization. Finally, we will investigate whether MpC002 and Ya lncRNAs migrate as complexes via interactions with specific plant proteins. The proposed work will improve our knowledge of (i) the functions of cross-kingdom RNAs; (ii) molecular aspects that mediate plant-insect interactions, (iii) the functions of plant DEK proteins in mRNA splicing and regulation of plant defence, and (iv) mechanisms involved in systemic spread of RNA-protein complexes in plants. All these areas involve basic biological processes that are not yet well understood. We anticipate that, in the long term, the research will contribute to translational/applied science projects. To make sure we maximize opportunities for impact, we will build on our existing network and experience in translational science projects to plan regular meetings with (current) industrial collaborators, and engage with stakeholders, (under)graduate students, and the (inter)national research community.
Publications
| Description | We have functionally characterised two aphid virulence proteins that aphids secrete into the plant during feeding. One suppresses the plant immune response by degrading cell surface receptors involved in inducing plant immunity and the other appears to hijack cytoplasmic bodies involved in RNA translational processes. Results have been shared at international conferences, published as preprints in Biorxiv, and submitted to peer-reviewed journals. Datasets used in the manuscripts were uploaded into Zenodo and public databases such as GenBank. |
| Exploitation Route | Collaborations with seed breeding industry |
| Sectors | Agriculture Food and Drink Chemicals Environment Manufacturing including Industrial Biotechology |
| Description | Collaboration with seed breeding company |
| First Year Of Impact | 2024 |
| Sector | Agriculture, Food and Drink,Environment,Manufacturing, including Industrial Biotechology |
| Impact Types | Economic |
| Description | Served as member of the JIC Appointments Committee |
| Geographic Reach | Multiple continents/international |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | Recruiting talented scientists to the UK and JIC. |
| Title | Confocal microscopy data associated with "Aphid effectors suppress plant immunity via recruiting defence proteins to processing bodies" |
| Description | Confocal microscopy data associated with "Aphid effectors suppress plant immunity via recruiting defence proteins to processing bodies" (Liu et al., 2024). In Liu et al. (2024), we reported that the cathepsin B (CathB) effectors of the peach-potato aphid, Myzus persicae, suppress plant immunity via recruiting plant proteins with key roles in regulating immune response to processing bodies. Here we provide the raw data for all confocal microscopy analyses conducted in this paper. Please refer to Liu et al. (2024) for a full description of the data and the methods. Subcellular localization Colocalization analysis using confocal microscopy Colocalization analysis was conducted by co-expression of different fluorescence protein-tagged proteins in N. benthamiana epidermal cells or A. thaliana protoplasts followed by observation with a Leica TCS SP8X upright confocal laser scanning microscope. Please refer to Liu et al. (2024) for full methods. Figure 2. CathB6 forms mobile puncta and colocalizes with processing body markers VCS and DCP5 in N. benthamiana leaf cells. Figure2_A-D.lif Fig.2A: Confocal images of CathB6-GFP-induced puncta. Fig.2B: Time-lapse confocal images of a CathB6-GFP puncta fusion. Fig.2C: Confocal images of CathB6-GFP colocalization with VCS-mCherry and intensity profiles below the images indicating overlap. Fig.2D: Confocal images of CathB6-GFP colocalization with DCP5-mCherry and intensity profiles below the images indicating overlap. Figure2_E-F.lif Fig.2_E-F: Analysis of VCS-mCherry puncta size distributions in the presence of GFP or CathB6-GFP. Figure 3. CathB6 interacts with EDS1 and relocates EDS1, PAD4 and ADR1 to p-bodies. Figure3_C-F.lif Fig.3C: Confocal images illustrating EDS1-mCherry localized in puncta with CathB6-GFP. Fig.3D: Confocal images illustrating colocalization of EDS1-GFP and VCS-mCherry with HA-CathB6. Fig.3E: Confocal images illustrating colocalization of EDS1-GFP and PAD4-mCherry in puncta with HA-CathB6. Fig.3F: Confocal images illustrating colocalization of EDS1-GFP and ADR1-L1-mCherry in puncta with Flag-PAD4 and HA-CathB6. Figure 5. Acd28.9 counteracts CathB6 recruitment of EDS1 to p-bodies and contributes to plant resistance against aphids. Figure5_B-C.lif Fig.5B: Confocal images illustrating Acd28.9-mCherry co-expression depletes CathB6-GFP puncta in N. benthamiana. Fig.5C: Confocal images illustrating EDS1-GFP forms puncta with HA-CathB6, and does not form puncta with Acd28.9-mCherry and HA-CathB6. Figure S5. M. persicae CathB6 locates to mobile puncta within plant cells. FigureS5_A-B.lif Fig.S5A: Confocal images of GFP and CathB6-GFP in N. benthamiana cells with mCherry serving as a reference marker. Fig.S5B (top): Confocal image of CathB6-GFP puncta. FigureS5B_statistics_Repeat1.lif FigureS5B_statistics_Repeat2.lif FigureS5B_statistics_Repeat3.lif Fig.S5B (Bottom): Classification of CathB6-GFP puncta into three size categories: Large (L), Medium (M), and Small (S) based on area. Puncta frequency was determined as the ratio of cells with CathB6-GFP puncta to the total number of cells observed in the field. Data were compiled from 61 observation fields across three independent experiments, with 22-40 cells per field. Fig. S7. M. persicae CathB6 and CathB3, and to a lesser extent CathB9, form cytoplasmic puncta in N. benthamiana epidermal cells and A. thaliana protoplasts. FigureS7.lif Fig.S7A: CathB6 forms puncta in N. benthamiana cells when fused to GFP (CathB6-GFP) or RFP (CathB6-RFP), with free RFP or GFP as internal references. Fig.S7B: CathB3-GFP forms puncta of varying sizes in N. benthamiana cells, whereas CathB9-GFP generates significantly fewer puncta. Fig.S7C: CathB6-GFP forms puncta of varying sizes in A. thaliana protoplasts. Fig. S8. CathB6-GFP colocalizes with p-body markers VCS, DCP5 and DCP1 in plant cells. FigureS8.lif Fig.S8A (left): CathB6-GFP colocalizes with VCS-mCherry in N. benthamiana cells. Fig.S8B (left): CathB6-GFP colocalizes with DCP5-mCherry in N. benthamiana cells. Fig.S8C (left): CathB6-GFP colocalizes with DCP1-RFP in N. benthamiana cells. Fig.S8D: Confocal images of YFP-DCP1 and VCS-mCherry in N. benthamiana cells, showing YFP-DCP1 localizing within VCS-mCherry puncta. FigureS8A_statistics_3Repeats.lif Fig.S8A (Right): Quantification of colocalization from three independent experiments across all observation fields. Top: ratios of cells expressing CathB6-GFP and VCS-mCherry. Middle: proportions of cells with large, medium, and small CathB6-GFP puncta colocalized with VCS-mCherry. Bottom: Proportions of VCS-mCherry puncta colocalized with CathB6-GFP in cells with CathB6-GFP. FigureS8B_statistics_3Repeats.lif Fig.S8B (Right): Quantification of colocalization from three independent experiments across all observation fields. Top: ratios of cells expressing CathB6-GFP and DCP5-mCherry. Middle: proportions of cells with large, medium, and small CathB6-GFP puncta colocalized with DCP5-mCherry. Bottom: Proportions of DCP5-mCherry puncta colocalized with CathB6-GFP in cells with CathB6-GFP. FigureS8C_statistics_3Repeats.lif Fig.S8C (Right): Quantification of colocalization from three independent experiments across all observation fields. Top: ratios of cells expressing CathB6-GFP and DCP1-RFP. Middle: proportions of cells with large, medium, and small CathB6-GFP puncta colocalized with DCP1-RFP. Bottom: Proportions of DCP1-RFP puncta colocalized with CathB6-GFP in cells with CathB6-GFP. Figure S9. CathB6-RFP colocalizing with p-body markers YFP-VCS and YFP-DCP1 in N. benthamianaleaf cells and A. thaliana protoplasts. FigureS9.lif Fig.S9A: CathB6-RFP colocalizing with p-body markers YFP-VCS and YFP-DCP1 in N. benthamiana leaf cells. Fig. S9B: CathB6-RFP colocalizing with p-body markers YFP-VCS and YFP-DCP1 in A. thaliana protoplasts. Figure S10. CathB6-GFP puncta partially colocalize with the stress granule marker RBP47b-mCherry, while no obvious colocalizations were observed with RFP-ATG8 or NPR1-mCherry in N. benthamiana epidermal cells. FigureS10.lif Fig.S10A: CathB6-GFP puncta partially colocalize with the stress granule marker RBP47b-mCherry. Fig.S10B: CathB6-GFP puncta don't colocalize with RFP-ATG8. Fig.S10C: CathB6-GFP puncta don't colocalize with NPR1-mCherry. Figure S14. CathB6-RFP relocates EDS1-GFP to puncta in N. benthamiana epidermal cells and A. thaliana protoplasts. FigureS14.lif Fig.S14A: CathB6-RFP relocates EDS1-GFP to puncta in N. benthamiana epidermal cells Fig.S14B: CathB6-RFP relocates EDS1-GFP to puncta in A. thaliana protoplasts. Fig. S15. EDS1 alone localizes to cytoplasmic puncta/p-bodies, while the EDS1-SAG101 complex remains localized to nuclei, in the presence of M. persicae CathB6 in N. benthamiana leaves. FigureS15.lif Fig.S15A: EDS1 alone localizes to cytoplasmic puncta/p-bodies in the presence of M. persicae CathB6 in N. benthamiana leaves. Fig.S15B: EDS1-SAG101 complex remains localized to nuclei in the presence of M. persicae CathB6 in N. benthamiana leaves. Time-lapse recording using confocal microscopy Time-lapse analysis was performed by transient expression of GFP-tagged CathB in N. benthamianaepidermal cells followed by observation with a Leica Stellaris 8 FALCON upright confocal microscope. Please refer to Liu et al. (2024) for full methods. Movie S1. M. persicae CathB6 locates to mobile puncta within cells of N. benthamiana leaves. MovieS1.lif MovieS1: Time-lapse recording of CathB6-GFP in N. benthamiana leaves. Fluorescence Recovery After Photobleaching (FRAP) CathB6-GFP was transiently expressed in N. benthamiana epidermal cells. CathB6-GFP punctum was photobleached with laser, and the recovery was recorded post-bleaching with a Leica Stellaris 8 FALCON upright confocal microscope. Please refer to Liu et al. (2024) for full methods. Fig. S5. M. persicae CathB6 locates to mobile puncta within plant cells. FigureS5C.lif Fig.S5C: Fluorescence recovery after photobleaching (FRAP) analysis of CathB6-GFP puncta. Three representative FRAP images show fluorescence recovery over time, with Time 0 marking the photobleaching pulse. FigureS5D.lif Fig.S5D: Time-course plot of fluorescence recovery for CathB6-GFP puncta after photobleaching. Recovery was quantified as the percentage of fluorescence intensity post-bleaching relative to pre-bleaching intensity. Data are analyzed from 25 independent experiments. FLIM-FRET analysis Constructs for genes of interest were co-expressed in N. benthamiana epidermal cells. Leaf sections were examined using a Leica Stellaris 8 FALCON scanning confocal microscope. FLIM experiments were conducted in TCSPC (Time-Correlated Single Photon Counting) mode. FLIM data were recorded using Leica LAS X software. FRET efficiency was calculated as 1 - (t? / t0). Please refer to Liu et al. (2024) for full methods. Figure 5. Acd28.9 counteracts CathB6 recruitment of EDS1 to p-bodies and contributes to plant resistance against aphids. Figure5D.lif Fig.5D: FLIM-FRET analysis showing reduced fluorescence lifetime of CathB6-GFP with EDS1 and Acd28.9, but not mCherry and VCS, with CathB6-GFP fluorescence and FLIM images. Figure5_E-F.lif Fig.5E: Lifetime measurements of CathB6-GFP of FLIM in Fig.5D. Fig.5F: Statistical lifetime and FRET efficiency of all FLIM. Acknowledgements We are grateful to JIC Bioimaging Facility for all confocal microscopy included in this submission. This work was funded by UK Research and Innovation (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) grants to SAH (BB/V008544/1 and BB/R009481/1) with help from The Gatsby Charitable Foundation, The Sainsbury Laboratory, Norwich, UK. Additional Support is provided by the BBSRC Institute Strategy Programmes (BBS/E/J/000PR9797 and BBS/E/JI/230001B) awarded to the JIC. The JIC is grant-aided by the John Innes Foundation. Files included: Figure2_A-D.lif Figure2_E-F.lif Figure3_C-F.lif Figure5_B-C.lif Figure5D.lif Figure5_E-F.lif FigureS5_A-B.lif FigureS5B_statistics_Repeat1.lif FigureS5B_statistics_Repeat2.lif FigureS5B_statistics_Repeat3.lif FigureS5C.lif FigureS5D.lif FigureS7.lif FigureS8.lif FigureS8A_statistics_3Repeats.lif FigureS8B_statistics_3Repeats.lif FigureS8C_statistics_3Repeats.lif FigureS9.lif FigureS10.lif FigureS14.lif FigureS15.lif MovieS1.lif Reference Liu, Q., Neefjes, A.C.M., Kobylinska, R., Mugford, S.T., Marzo, M., Canham, J., Schuster, M., van der Hoorn, R.A.L., Chen, Y., and Hogenhout, S.A. (2024). Aphid effectors suppress plant immunity via recruiting defence proteins to processing bodies. bioRxiv, 2024.11.20.624400. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | These data are described in Liu et al., 2024. bioRxiv, 2024.11.20.624400. As per 8 Mar 2025, the datasets has 14 views and 113 downloads. |
| URL | https://zenodo.org/doi/10.5281/zenodo.14450692 |
| Title | The proximity interactome of the peach-potato aphid (Myzus persicae) cathepsin B in Arabidopsis thaliana |
| Description | Introduction In agriculture, the peach-potato aphid Myzus persicae (Sulzer) has one of the broadest host ranges among insects and cause devastating crop losses worldwide (CABI, 2022). They are highly adaptable, displaying a wide range of plastic responses to environmental cues, including the ability to develop as either winged or wingless forms and to reproduce through either asexual or sexual means (Brisson, 2010; Ogawa and Miura, 2014; Grantham and Brisson, 2018). Remarkably, M. persicae differentially regulate the transcription of certain gene clusters to facilitate colonization of diverse plant species (Mathers et al., 2017; Chen et al., 2020). Among these gene clusters are members of the cysteine protease family, cathepsin B (CathB). Host responsive CathB genes are organized in tandemly repeated clusters in the M. persicae genome and belong to a recently expanded clade in phylogeny (Mathers et al., 2017). They are upregulated when aphids feed on Arabidopsis thaliana and Brassica rapa and knock down of their expression using RNA interference reduces aphid reproduction on A. thaliana (Chen et al., 2020). Intriguingly, peptides corresponding to CathB proteins are detected in M. persicae oral secretion (OS), indicating that at least some CathB proteins are directly delivered into plant cells during aphid feeding (Guo et al., 2020; Liu et al., 2024). Among M. persicae CathB proteins, CathB6 is most highly expressed in aphids on A. thaliana (Chen et al., 2020) and most abundant in M. persicae OS (Liu et al., 2024). To identify the potential plant targets of M. persicae CathB, we optimized the TurboID-based proximity labelling and MS (PL-MS) protocol (Fig. 1). As a first step, we generated stable transgenic A. thaliana lines producing GFP or CathB6 as C-terminal TurboID-3×FLAG fusions (GFP-TurboID or CathB6-TurboID). Seedlings of these plants were treated with biotin followed by affinity capture with streptavidin beads (Fig. 2A). Enrichment of biotinylated proteins was confirmed by western blotting (Fig. 2B), followed by nanoLC-MS/MS analyses. Principal component analysis (PCA) of the MS data showed that the three CathB6-TurboID samples were grouped together, separately from three GFP-TurboID samples (Fig. 2C). Furthermore, MA plot confirmed that the CathB6-TurboID and GFP-TurboID samples are distinct (Fig. 2D). From the complete dataset, 267 A. thaliana proteins exhibited statistically significant enrichment (p-value < 0.05) of more than 2-fold and were consistently identified in at least two replicates of the CathB6-TurboID samples compared to the GFP-TurboID controls (Fig. 2E, Table 1). This compares to 223 proteins in the GFP-TurboID samples versus CathB6-TurboID samples (Fig. 2E, Table 1). Additionally, we identified 20 unique peptides corresponding to CathB6 in the CathB6-TurboID samples and 19 unique peptides corresponding to GFP in the GFP-TurboID samples (Table 1). These data suggest that this PL-MS protocol worked dnd identified genuine interactors of CathB6. Together, this dataset identifies 267 potential plant interactors of aphid CathB6, which may contribute to CathB6 modulation of A. thaliana plant for colonization. Further mechanistic studies should be done to characterize if these potential interactors are involved and how the relevant pathways are affected after CathB delivery through aphid feeding. Materials and Methods Plasmid construction For the construction of plasmids producing CathB6-TurboID-3×FLAG, the coding sequences corresponding to the catalytic domain (without signal peptide and prodomain regions) of CathB6 (Arg61-Asn338) and TurboID-3×FLAG were separately amplified. Then, the two fragments were connected using overlap PCR (Nelson and Fitch, 2011). After cloning of the sequence corresponding to the CathB6-TurboID-3×FLAG fragment into the pJET vector and sequencing, CathB6-TurboID-3×FLAG was amplified with primers containing attB extensions and cloned into the pDONOR207 vector, followed by the ligation to Gateway destination vector pB7WG2 containing a 35S promoter. Similar cloning methods were used for construction of GFP-TurboID-3×FLAG. Plant transformation The constructed plasmids were introduced into Agrobacterium tumefaciens strain GV3101, and the cultures were grown on plates at 28 °C for 24-48 hrs. Then, positive colonies were identified via PCR using plasmids extracted from overnight liquid cultures and gene-specific primers. Positive colonies were grown at 28 °C in liquid cultures and transformed into A. thaliana Col-0 plants using the floral dipping method (Bechtold, 1993). Transgenic seeds were harvested and selected on Murashige and Skoog (MS) medium supplemented with 20 µg/mL phosphinothricin (BASTA) and screened for ratio of 3:1 alive/dead segregation. After screening for two or three generations, transgenic plants were deemed to harbor single homozygous transgenes and were used for proximity labeling once germinated seeds achieved a 100% survival rate. Proximity labelling Seeds of A. thaliana plants stably expressing GFP-TurboID-3×FLAG or CathB6-TurboID-3×FLAG were sowed on ½ MS plates containing 1.0% sucrose and 0.3% phytagel and placed under long-day condition (16 h light/8 h dark) at 22 °C. After 10 days, 2.5 g seedlings were collected and submerged in 50 µM biotin solution for 4 hrs at RT. Afterwards, seedlings were rinsed with ice-cold MilliQ water for 5 times. After removing excess liquid with paper towel, seedings were ground with pestle, mortar and nitrogen to a fine powder. Protein extraction was performed in 5 mL of extraction buffer [150 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM EDTA, 10% Glycerol, 10 mM DTT, 0.4% Nonidet-40, 0.1% (w/v) Deoxycholic acid, 2% (w/v) PVPP, 1 tablet of cOmplete protease Inhibitor cocktail (Roche, Catalog number 10697498001)] and incubation on a rotor wheel at 4 °C for 30 min, followed by centrifugation of the tubes at 5000 g for 15 min to remove the cell debris. The upper soluble fraction was then run through the Zeba Spin Desalting Column (Thermo Fisher Scientific, Catalog number 89893) to remove excess biotin from the lysates. Fifty (50) µL of desalted lystate was used as input for western blot analysis, while the rest of the desalted lysate was incubated with High Capacity Streptavidin Agarose Resin (Thermo Fisher Scientific, Catalog number 20361) on a rotor wheel at 4 °C overnight. The next day, Streptavidin beads were sequentially washed once in 1 mL Buffer 1 (2% SDS in water), once in 1 mL Buffer 2 [150 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM EDTA, 10% Glycerol, 0.1% (w/v) Deoxycholic acid (w/v), 1% Triton X-100], once in buffer 3 [10 mM Tris-HCl (pH 7.4), 250 mM LiCl, 1 mM EDTA, 0.1% (w/v) Deoxycholic acid, 1% (v/v) NP40], twice in Buffer 4 [50mM Tris-HCl (pH 7.5)], and six times in Buffer 5 (50mM ammonium bicarbonate, pH 8.0). Finally, the streptavidin beads were resuspended in 200 µL of 50 mM ammonium bicarbonate. For quality control of the TurboID immunoprecipitation, 10% (20 µL) of the suspension was taken out for Western blot analysis, and the remaining bead suspension was flash-frozen in liquid nitrogen and stored at -80 °C and submitted to nano LC-MS/MS analysis. For western blot analysis, 20 µL of suspended streptavidin beads in washing buffer 5 were added to 10 µL of 4× LDS Sample Loading Buffer, 10 mM DTT and 2 mM biotin, and boiled for 10 min. Samples were loaded onto 12% SDS-PAGE gels (Invitrogen) and transferred to 0.22 µm PVDF membranes using the Bio-Rad mini-PROTEAN Electrophoresis system. Membranes were hybridized with Streptavidin-HRP. NanoLC-MS/MS Biotinylated proteins enriched with streptavidin beads were processed with trypsin via on bead digestion. The beads were washed in water and resuspended in of 1.5% sodium deoxycholate (SDC; Merck) in 0.2 M EPPS-buffer (Merck) to 50% bead slurry vol/vol, pH 8.5 and vortexed under heating. Cysteine residues were reduced with dithiothreitol, alkylated with iodoacetamide, and the proteins digested with trypsin in the SDC buffer according to standard procedures for 8 hrs. The beads were then pelleted by centrifugation and the supernatant was collected for SDC precipitation by adding trifluoroacetic acid (TFA) to a final concentration of 0.2%. The clear supernatant was subjected to C18 SPE using home-made stage tips with C18 Reprosil_pur 120, 5 µm (Dr. Maisch GmbH, Germany). Aliquots were analyzed by nano LC-MS/MS on an Orbitrap Eclipseâ„¢ Tribridâ„¢ mass spectrometer equipped with a FAIMS Pro Duo interface coupled to an UltiMate® 3000 RSLC nano LC system (Thermo Fisher Scientific, Hemel Hempstead, UK). The samples were loaded onto a trap cartridge (PepMapâ„¢ Neo Trap Cartridge, C18, 5um, 0.3x5mm, Thermo) with 0.1% TFA at 15 µl min-1 for 3 min. The trap column was then switched in-line with the analytical column (Aurora Frontier TS, 60 cm nanoflow UHPLC column, ID 75 µm, reversed phase C18, 1.7 µm, 120 Å; IonOpticks, Fitzroy, Australia) for separation at 55°C using the following gradient of solvents: A (water, 0.1% formic acid) and B (80% acetonitrile, 0.1% formic acid) at a flow rate of 0.26 µl min-1 : 0-3 min 1% B (parallel to trapping); 3-10 min increase B (curve 4) to 8%; 10-102 min linear increase B to 48; followed by a ramp to 99% B and re-equilibration to 0% B. Total runtime was 140 min. Mass spectrometry data were acquired between 10 and 110 min with the FAIMS device set to three compensation voltages (-35V, -50V, -65V) at standard resolution for 1.0 s each with the following MS settings in positive ion mode: OT resolution 120K, profile mode, mass range m/z 300-1600, normalized AGC target 100%, max inject time 50 ms; MS2 in IT Turbo mode: quadrupole isolation window 1 Da, charge states 2-5, threshold 1e4, HCD CE = 30, AGC target standard, max. injection time dynamic, dynamic exclusion 1 count for 15 s with mass tolerance of ±10 ppm, one charge state per precursor only. The mass spectrometry raw data were processed and quantified in Proteome Discoverer 3.1 (PD3.1) (Thermo) using the search engine CHIMERYS (MSAID, Munich, Germany); all mentioned tools of the following workflow are nodes of the proprietary Proteome Discoverer (PD) software. The A. thaliana protein sequence database (TAIR10, 35,386 entries, from 14/12/2010), the two sequences of the used TurboID constructs, and the MaxQuant contaminants database (240812, 246 entries) were imported into PD adding a reversed sequence database for decoy searches. The database search was performed using the search engine CHIMERYS (MSAID, Munich, Germany). The processing workflow started with spectrum recalibration, Minora Feature Detection with min. trace length 5, S/N 2.5, PSM confidence high, and Top N Peak Filter with 20 peaks per 100 Da. For CHIMERYS, the inferys_3.0.0_fragmentation prediction model with FDR targets 0.01 (strict) and 0.05 (relaxed), a fragment tolerance of 0.3 Da, enzyme trypsin with 2 missed cleavages, variable modification oxidation (M), fixed modification carbamidomethyl (C) were used. The consensus workflow in the PD3.1 software was used to evaluate the peptide identifications and to measure the abundances of the peptides based on the LC-peak intensities. For chromatographic alignment and feature mapping, a retention time tolerance of 2 min, a mass tolerance of 1 ppm, and an S/N threshold of 5 were used. For quantification, three replicates per condition were measured. In PD3.1, the following parameters were used for ratio calculation: normalization on total peptide abundances, protein abundance-based ratio calculation using the Top3 most abundant peptides, missing values imputation by low abundance resampling, hypothesis testing by t-test (background based), adjusted p-value calculation by BH-method. The results were exported into a Microsoft Excel table including data for protein abundances, ratios, p-values, number of peptides, protein coverage, the CHIMERYS identification score and other important values. Data availability statement The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier with the dataset identifier PXD057789 and 10.6019/PXD057789. Acknowledgements This research was funded by UK Research and Innovation (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) grants to SAH (BB/V008544/1 and BB/R009481/1). Additional Support is provided by the BBSRC Institute Strategy Programmes (BBS/E/J/000PR9797 and BBS/E/JI/230001B) awarded to the JIC. The JIC is grant-aided by the John Innes Foundation. Conflicts of Interest The authors declare that no conflicts of interest exist. Legends of figures and tables Figure 1. Principle of CathB6-TurboID based proximity labelling with MS (PL-MS). The TurboID biotin ligase (TurboID) is fused to C-terminus of CathB6. Exogenous addition of biotin (yellow stars) biotinylates proteins in the proximity of CathB6-TurboID fusion protein, whereas distal proteins are not biotinylated. The biotinylated proteins are captured by incubating total proteins extracts with streptavidin beads. Peptides derived from biotinylated proteins, most of which are in the proximity of CathB6-TurboID, are detected by nanoLC-MS. Fig. 2. Sample preparation and quantification for CathB6-TurboID interactome in A. thaliana. (A) Sample preparation working flow for TurboID-based proximity labeling. GFP-TurboID and CathB6-TurboID seedlings were treated with 50 µM biotin for 4 hrs at room temperature. (B) Visualization on western blots of biotinylated proteins detected after desalting step (input) and 12 wash steps of Streptavidin beads (Streptavidin IP) as per workflow shown in (A). (C) Principal component analysis (PCA) of three replicates of GFP-TurboID and CathB6-TurboID samples. (D) MA plot of three replicates of GFP-TurboID and CathB6-TurboID samples. (E) Venn diagrams showing the overlap of proteins identified in three biological replicates of GFP-TurboID (left) and CathB6-TurboID (right) upon a fold-change of CathB6-TurboID/GFP-TurboID > 2, n = 267. Table 1. Full list of proteins detected from CathB6-TurboID PL-MS. References Bechtold, N. (1993). In planta Agrobacterium-mediated gene transfer by infiltration of adult Arabidopsis thaliana plants. CR Acad. Sci. Paris, Life Sci. 316, 1194-1199. Brisson, J.A. (2010). Aphid wing dimorphisms: linking environmental and genetic control of trait variation. Philos Trans R Soc Lond B Biol Sci 365, 605-616. CABI, C.f.A.a.B.I. (2022). Myzus persicae (green peach aphid). CABI Compendium. Chen, Y., Singh, A., Kaithakottil, G.G., Mathers, T.C., Gravino, M., Mugford, S.T., van Oosterhout, C., Swarbreck, D., and Hogenhout, S.A. (2020). An aphid RNA transcript migrates systemically within plants and is a virulence factor. Proc Natl Acad Sci U S A 117, 12763-12771. Grantham, M.E., and Brisson, J.A. (2018). Extensive Differential Splicing Underlies Phenotypically Plastic Aphid Morphs. Mol Biol Evol 35, 1934-1946. Guo, H., Zhang, Y., Tong, J., Ge, P., Wang, Q., Zhao, Z., Zhu-Salzman, K., Hogenhout, S.A., Ge, F., and Sun, Y. (2020). An Aphid-Secreted Salivary Protease Activates Plant Defense in Phloem. Curr Biol 30, 4826-4836.e4827. Liu, Q., Goldberg, J.K., Mugford, S.T., Saalbach, G., Martins, C., Singh, A., Kaithakotti, G.G., Swarbreck, D., and Hogenhout, S.A. (2024). The salivary proteome of the green peach aphid/peach-potato aphid (Myzus persicae) (Sulzer, 1776) (Hemiptera, Aphididae) (Zenodo). Mathers, T.C., Chen, Y., Kaithakottil, G., Legeai, F., Mugford, S.T., Baa-Puyoulet, P., Bretaudeau, A., Clavijo, B., Colella, S., Collin, O., Dalmay, T., Derrien, T., Feng, H., Gabaldón, T., Jordan, A., Julca, I., Kettles, G.J., Kowitwanich, K., Lavenier, D., Lenzi, P., Lopez-Gomollon, S., Loska, D., Mapleson, D., Maumus, F., Moxon, S., Price, D.R., Sugio, A., van Munster, M., Uzest, M., Waite, D., Jander, G., Tagu, D., Wilson, A.C., van Oosterhout, C., Swarbreck, D., and Hogenhout, S.A.(2017). Rapid transcriptional plasticity of duplicated gene clusters enables a clonally reproducing aphid to colonise diverse plant species. Genome Biol 18, 27. Nelson, M.D., and Fitch, D.H. (2011). Overlap extension PCR: an efficient method for transgene construction. Methods Mol Biol 772, 459-470. Ogawa, K., and Miura, T. (2014). Aphid polyphenisms: trans-generational developmental regulation through viviparity. Front Physiol 5, 1. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | The dataset is described in Lui et al., 2024 - bioRxiv preprint doi: https://doi.org/10.1101/2024.11.20.624400. As per 8 Mar 2025, the datasets had 29 views and 25 downloads. |
| URL | https://zenodo.org/doi/10.5281/zenodo.14181686 |
| Title | The salivary proteome of the green peach aphid/peach-potato aphid (Myzus persicae) (Sulzer, 1776) (Hemiptera, Aphididae). |
| Description | *for correspondence: saskia.hogenhout@jic.ac.uk Introduction The green peach aphid/peach-potato aphid Myzus persicae colonizes hundreds of plant species, an ability that is in part due to the delivery of saliva proteins - often referred to as effectors - into the host plant that suppress plant defence. As a generalist herbivore with a remarkable ability to colonize new host plants (Dedryver et al., 2010), M. persicae represents an outstanding model system for studying the molecular mechanisms underlying plant-insect interactions. Recent advancements in mass spectrometry instrumentation (Yu et al. 2020) and database search software (Frejno et al., 2024), along with a new high-quality reference genome assembly for M. persicae (Mathers et al., 2021) and a simplified method for improved aphid saliva recovery that we describe here, collectively enhance the detection of saliva proteins with unprecedented sensitivity and specificity. Here, we present a high-quality salivary proteome of M. persicae generated using a nanoLC-MS/MS analysis in combination with an updated annotation of the Myzus persicae clone O genome. Saliva from over 10,000 M. persicae aphids was collected and analysed using nanoLC-MS/MS (Figure 1). We identified 1557 peptide sequences mapped to the M. persicae clone O genome v2.1 annotation (Table 1); 210 of those peptides additionally appeared as modified by oxidation (M) and/or carbamidomethylation (C) so that a total of 1767 peptide forms mapping to M. persicae were detected with high confidence (combined from both search engines). Of those peptides, 120 were only identified by the CHIMERYS search engine, and 52 were only identified by the Mascot search engine (all with high confidence). Most peptides were detected in the concentrated sample; of the 1767 peptide forms, only 411 were detected in the unconcentrated sample and only 10 of those were exclusively identified in the unconcentrated sample. Those peptides were assigned to a total of 423 M. persicae proteins with high confidence and at least 1 unique peptide including all proteins with shared peptides. The software generated 169 protein groups each represented by one master protein. The master protein is the largest protein with the most peptide matches in the group. Of the 169 protein groups, 126 groups were identified with at least 2 unique peptides and 43 groups were identified with only 1 unique peptide. Protein groups that included products of a single gene model were identified from the peptide output of Proteome Discoverer. The 169 protein groups included proteins encoded by 219 gene models which are listed in Table 3, of which 155 had peptides that did not match to any other gene model. Of note, 8 Cathepsin B cysteine peptidases are listed (highlighted in Table 3) which have previously been shown to play an important role in colonisation of host plants (Mathers et al. 2017). This is a recently diversified gene family including 28 members in the Myzus persicae genome. The detected proteins belong to several protein groups including highly similar or identical proteins with shared peptides detected, and also include CathB17 (MYZPE13164_O_EIv2.1_0124700) which was excluded from the database because it is identical to CathB18 (MYZPE13164_O_EIv2.1_0124690) Together, these findings suggest that aphid saliva contains enzymes that likely alter plant physiology by interacting with both plant proteins and small molecules. Future mechanistic studies will be able to precisely characterize the role of these salivary proteins to understand the pathways, hormones, and chemical defences that may be suppressed by aphids. Materials and Methods Aphid rearing M. persicae Clone O colonies were reared on Arabidopsis thaliana Col-0 plants in a growth chamber maintained at 20°C with a 14-hour light/10-hour dark cycle and 75% relative humidity. The A. thaliana plants were grown at short-day conditions (10 hours light/14 hours dark) at 22°C and 70% relative humidity. For aphid rearing, 4-week-old A. thaliana plants were used, and plants were replaced every two weeks to ensure optimal conditions for aphid growth. Saliva collection Figure 1 illustrates the schematic overview of our sample collection and preparation workflow. M. persicae Clone O aphids were transferred to 4-week-old A. thaliana plants and maintained for 2-3 weeks to enable reproduction. Approximately 100 aphids were then placed into a 50 mm Petri dish, which was sealed with parafilm and had a hole in the base for introducing the aphids. The hole was subsequently covered with mesh to allow ventilation while preventing aphid escape. A 300 µL aliquot of artificial diet (15% w/v sucrose in Milli-Q water, sterilized via 0.22 µm filtration) was added to the inverted lid of the Petri dish. The dish, with the aphid chamber positioned over the lid, was set up so that the parafilm made contact with the artificial diet. This setup was kept under short-day conditions (14 hours light/10 hours dark) at 20°C and 75% relative humidity for 24 hours. After 24 hours, the artificial diet, now containing aphid saliva, was collected and pooled to produce an unconcentrated saliva sample. A portion of this sample was then concentrated using a Vivaspin concentrator with a 3 kDa molecular weight cut-off (MWCO) at 4°C. The concentrated saliva was snap-frozen in liquid nitrogen and stored at -80°C until further analysis. This procedure was repeated until saliva was collected from approximately 10,000 aphids. Saliva preparation and nanoLC-MS/MS analysis Saliva samples were precipitated by adding 4 volumes of methanol and 1 volume of chloroform, followed by centrifugation at maximum speed for 10 minutes (Wessel and Flügge, 1984). After removing the supernatant, the pellet was washed once with acetone before proceeding to trypsin digestion. The protein pellet was resuspended in 50 µL of 1.5% sodium deoxycholate (SDC; Merck) in 0.2 M EPPS buffer (Merck), pH 8.5, and vortexed under heating. Cysteine residues were reduced with dithiothreitol, alkylated with iodoacetamide, and proteins were digested with trypsin in the SDC buffer following standard protocols. After digestion, SDC was precipitated by adjusting the solution to 0.2% trifluoroacetic acid (TFA). The clear supernatant was then subjected to C18 solid-phase extraction (SPE) using OMIX 10-100 µL C18 pipette tips (Agilent). The samples were analysed using nanoLC-MS/MS on an Orbitrap Eclipseâ„¢ Tribridâ„¢ mass spectrometer, coupled with an UltiMate® 3000 RSLCnano LC system (Thermo Fisher Scientific, Hemel Hempstead, UK). Samples were loaded onto a trap column (nanoEase M/Z Symmetry C18 Trap Column, Waters) with 0.1% TFA at a flow rate of 15 µL/min for 3 minutes. The trap column was then switched in-line with the analytical column (nanoEase M/Z HSS C18 T3, 1.8 µm, 100 Å, 250 mm x 0.75 µm, Waters) for separation at 40°C. The gradient used for separation was as follows: solvent A (water with 0.1% formic acid) and solvent B (80% acetonitrile with 0.1% formic acid) at a flow rate of 0.2 µL/min: 0-3 minutes at 3% B (parallel to trapping); 3-10 minutes with B increasing to 8%; 10-130 minutes with B linearly increasing to 45%; 130-145 minutes with B linearly increasing to 55%; followed by a ramp to 99% B and re-equilibration to 0% B, for a total runtime of 180 minutes. Mass spectrometry data were acquired in positive ion mode with the following settings: Orbitrap resolution at 120K, profile mode, mass range m/z 300-1800, normalized AGC target at 100%, and a maximum injection time of 50 ms. For MS2 analysis in IT Turbo mode, parameters included quadrupole isolation window of 1.2 Da, charge states 2-5, threshold at 1.9e4, HCD CE and CID CE both set to 33 in parallel, AGC target at 1e4, maximum injection time of 35 ms, and dynamic exclusion set to 1 count for 15 seconds with a mass tolerance of ±7 ppm. M. persicae v2.1 annotation The chromosome scale genome assembly of M. persicae clone O (Mathers et al. 2021) was re-annotated for accurate gene prediction as follows. Illumina short read RNAseq data from M. persicae used for previous annotation (Mathers et al. 2017; EBI ENA SAMEA4469192) was used in addition to stranded RNAseq reads of M. persicae clone O feeding from 9 different host plant species (Chen et al. 2020 ; NCBI GEO GSE129669); from males, alate asexual females and winged asexual females, and nymphs (Mathers et al. 2019; NCBI SRA PRJNA437622); dissected organs from winged and alate asexual female M. persicae (EBI ENA PRJEB79119), and PacBio Isoseq RNAseq data from asexual female M. persicae (EBI ENA PRJEB79119). Candidate transcript sequences were assembled from RNA-seq reads with Scallop (Shao and Kingsford 2017) and StringTie (Pertea et al. 2015) using a genome guided approach. A filtered set of non-redundant transcripts are derived using Mikado (Venturini et al. 2018) for the final transcript set for annotation. Mikado models together with aligned proteins and repeat annotation are provided as hints to Augustus (http://bioinf.uni-greifswald.de/augustus/). Multiple Augustus gene builds were created from alternative evidence inputs or weightings. These were supplemented with gene models derived directly from protein alignments and high confidence models from the Mikado transcript selection stage. Metrics were generated to assess how well supported each gene model is by available evidence and an integrated set of models produced by Mikado. Long non-coding (lnc) RNAs were identified from the assembled RNAseq. Transcripts with open reading frames (Transdecoder, https://transdecoder.github.io) showing similarity to arthropod protein coding genes (BlastP e<1e-5), or with HAMMER hits against the Pfam database were excluded. Remaining transcripts with coding potential >0.5 (CPC2, Kang et al. 2017) or that were shorter than 200bp were also excluded. Transcripts mapping to rRNA, tRNA, miRNA or transposon loci were excluded using Mikado (Venturini et al. 2018). In total we identified 37,720 total genes (with 58,609 total splice variant isoforms), including 22,796 (47,508 total isoforms encoding 39,681 unique proteins) protein coding and 7,990 (11,101 total isoforms) non-coding genes. (Further details of the annotation process and statistics can be found in CloneO_v2.1_annotation_summary_stats.txt and Myzus_persicae_O_annotation_readme.doc). Mass spectrometry data processing The mass spectrometry raw data were processed and quantified in Proteome Discoverer 3.1 (Thermo), all mentioned tools of the following workflows are nodes of the proprietary Proteome Discoverer (PD) software. The database search was performed using the search engines CHIMERYS (MSAID, Munich, Germany) and Mascot Server 2.8.3 (Matrixscience, London) in parallel on the following databases: MYZPE13164_O_EIv2.1.annotation.gff3.pep.fasta (39,681 entries after removal of duplicate protein sequences) and common contaminants (MaxQuant.org, 20240812, 246 entries). The databases were imported into PD adding a reversed sequence database for decoy searches. The processing workflow started with spectrum recalibration on the Myzus protein database, Minora Feature Detection with min. trace length 7, S/N 2.5, PSM confidence high, and Top N Peak Filter with 20 peaks per 100 Da. For CHIMERYS, the inferys_3.0.0_fragmentation prediction model with FDR targets 0.01 (strict) and 0.05 (relaxed), a fragment tolerance of 0.3 Da, enzyme trypsin with 2 missed cleavages, variable modification oxidation (M), fixed modification carbamidomethyl (C) were used. For Mascot, the same parameters were used including a precursor tolerance of 5 ppm and a fragment tolerance of 0.5 Da; validation was performed using Percolator based on q-values and FDR targets 0.01 (strict) and 0.05 (relaxed). The consensus workflow in the PD software was used to evaluate the peptide identifications and to measure the abundances of the peptides based on the LC-peak intensities. For identification, an FDR of 0.01 was used as strict threshold. Protein abundance was calculated using the Top3 most abundant peptides. The results were exported into Microsoft Excel including data for protein abundances, number of peptides, protein coverage, the search identification score and other important values (Tables 1 and 2). Identification of protein groups with members encoded by a single gene model was performed by first identifying peptides that mapped to a single gene model, then counting the number of peptides that were specific to each gene model. Data availability statement The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (https://www.ebi.ac.uk/pride/) partner repository with the dataset identifier PXD055051 and 10.6019/PXD055051. Acknowledgements We would like to thank the Informatics, and Entomology technology platforms at the John Innes Centre for technical support. Conflicts of Interest The authors declare that no conflicts of interest exist. Funding Information This work was funded by UK Research and Innovation (UKRI) Biotechnology and Biological Sciences Research Council (BBSRC) grants to S.A.H. (BB/V008544/1 and BB/R009481/1). Additional Support as provided by the BBSRC Institute Strategy Programmes (BBS/E/J/000PR9797 and BBS/E/JI/230001B) awarded to the John Innes Centre (JIC). The JIC is grant-aided by the John Innes Foundation. Figures and legends Fig. 1. Experimental procedure for detecting M. persicae secretome. (A) The workflow of M. persicae saliva collection and concentration for nanoLC-MS/MS. (B) The diagrammatic representation of mass spectrometry data analysis of M. persicae saliva. Table 1. Full list of detected peptides. List of peptides detected in M. persicae saliva from Mascot and Chimerys searches of the M. persicae clone O v2.1 database. Table 2. Full list of proteins detected List of proteins detected in M. persicae saliva. Closely related proteins with shared peptides are grouped into protein groups by Proteome Discoverer, the highest confidence of these is the Master protein of the group. Master proteins of the 169 protein groups are indicated in the 'Master' column as 'Master protein', other proteins belonging to these groups have shared peptides. Proteins belonging to groups containing only different isoforms (i.e. splice variants) from the same gene model are indicating by the number of peptides that specifically match that gene model Table 3. Annotated list of unique gene models representing detected proteins. Two hundred and nineteen (219) gene models that encode at least one protein detected in the M. persicae saliva. For each gene model, the highest confidence detected protein is shown: either a master protein of the protein group, or else the longest isoform of that protein. Annotation is derived from Interproscan, including descriptions from pfam, GO and BlastP hits, and include the identities of candidate effector proteins (e.g. Mp1, Mp2) identified in Bos et al. (2010). Cathepsin B proteins are highlighted in green. Supplementary files: Genome annotation files: Myzus_persicae_O_v2.0.scaffolds.fa (as described in Mathers et al. 2021) CloneO_v2.1_annotation_summary_stats.txt MYZPE13164_O_EIv2.1.annotation.gff3 MYZPE13164_O_EIv2.1.annotation_w.functions.gff3 MYZPE13164_O_EIv2.1.annotation.gff3.cdna.fasta MYZPE13164_O_EIv2.1.annotation.gff3.cds.fasta MYZPE13164_O_EIv2.1.annotation.gff3.metrics.txt MYZPE13164_O_EIv2.1.annotation.gff3.pep.fasta MYZPE13164_O_EIv2.1.annotation.gff3.cdna.LTPG.fasta MYZPE13164_O_EIv2.1.annotation.gff3.cds.LTPG.fasta MYZPE13164_O_EIv2.1.annotation.gff3.pep.LTPG.fasta Myzus_persicae_O_annotation_readme.doc Literature Cited Chen Y, Singh A, Kaithakottil GG, Mathers TC, Gravino M, Mugford ST, van Oosterhout C, Swarbreck D, Hogenhout SA. (2020). An aphid RNA transcript migrates systemically within plants and is a virulence factor. Proc Natl Acad Sci U S A. 117(23):12763-12771. doi: 10.1073/pnas.1918410117. Dedryver, C.A., Le Ralec, A., and Fabre, F. (2010). The conflicting relationships between aphids and men: a review of aphid damage and control strategies. C R Biol 333, 539-553. Frejno, M., Berger, M.T., Tüshaus, J., and Wilhelm, M. (2024). Unifying the analysis of bottom-up proteomics data with CHIMERYS. bioRxiv 2024.05.27.596040; doi: https://doi.org/10.1101/2024.05.27.596040 Kang, Y. J., Yang, D. C., Kong, L., Hou, M., Meng, Y. Q., Wei L., and Gao, G. (2017). CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Research 45, W12-W16. Mathers, T.C., Chen, Y., Kaithakottil, G., Hogenhout, S.A. (2017). Rapid transcriptional plasticity of duplicated gene clusters enables a clonally reproducing aphid to colonise diverse plant species. Genome Biol 18, 27. https://doi.org/10.1186/s13059-016-1145-3 Mathers TC, Mugford ST, Percival-Alwyn L, Chen Y, Kaithakottil G, Swarbreck D, Hogenhout SA, van Oosterhout C. (2020) Sex-specific changes in the aphid DNA methylation landscape. Mol Ecol. 28(18):4228-4241. doi: 10.1111/mec.15216. Mathers, T.C., Wouters, R.H.M., Mugford, S.T., Swarbreck, D., van Oosterhout, C., Hogenhout, S.A. (2021). Chromosome-Scale Genome Assemblies of Aphids Reveal Extensively Rearranged Autosomes and Long-Term Conservation of the X Chromosome, Molecular Biology and Evolution 38, 856-875. Perez-Riverol Y, Bai J, Bandla C, Vizcaíno JA (2022). The PRIDE database resources in 2022: A Hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res 50(D1):D543-D552 (PubMed ID: 34723319). Pertea, M., Pertea, G.M., Antonescu, C.M., Chang, T.C., Mendell, J.T. and Salzberg, S.L. (2015). StringTie enables improved reconstruction of a transcriptome from RNA-seq reads Nature Biotechnology 33,290-295. doi:10.1038/nbt.3122 Shao, M., Kingsford, C. (2017). Accurate assembly of transcripts through phase-preserving graph decomposition. Nat Biotechnol 35, 1167-1169 (2017). https://doi.org/10.1038/nbt.4020 Venturini, L., Caim, S., Kaithakottil, G.G., Mapleson, D.L., and Swarbreck, D. (2018). Leveraging multiple transcriptome assembly methods for improved gene structure annotation, GigaScience7giy093 https://doi.org/10.1093/gigascience/giy093 Wessel, D., and Flügge, U.I. (1984). A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. Analytical Biochemistry 138, 141-143. Yu, Q., Paulo, J.A., Naverrete-Perea, J., McAlister, G.C., and Gygi, S.P., and Schweppe, D.K. (2020). Benchmarking the Orbitrap Tribrid Eclipse for Next Generation Multiplexed Proteomics. Analytical Chemistry 2020 92, 6478-6485. DOI: 10.1021/acs.analchem.9b05685 |
| Type Of Material | Database/Collection of data |
| Year Produced | 2024 |
| Provided To Others? | Yes |
| Impact | This dataset is described in Liu et al., 2024 - bioRxiv preprint doi: https://doi.org/10.1101/2024.11.20.624400. As per 8 Mar, the dataset had 151 views and 186 downloads. |
| URL | https://zenodo.org/doi/10.5281/zenodo.13269256 |
| Description | Collaboration with INRAE, France |
| Organisation | INRA (UMR-MISTEA) Montpellier, France |
| Country | France |
| Sector | Academic/University |
| PI Contribution | We resequenced the genomes of insect samples sent to us by the collaborator |
| Collaborator Contribution | Collaborators sent us Xylella fastidiosa insect vectors collected from France and other countries in Europe |
| Impact | We will assess the global population structure of Xylella fastidiosa insect vectors |
| Start Year | 2021 |
| Description | Collaboration with The Sainsbury Laboratory, Norwich |
| Organisation | The Sainsbury Laboratory |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Provision of aphid samples, (HPLC-purified) aphid extracts and plant pull downs to analyze by mass spectrometry. Contribution of aphid, microbial and plant sequence data to analyze the mass spectrometry data against. |
| Collaborator Contribution | Sample analyses with mass spectrometry and help with interpretation of the mass spectrometry data output. |
| Impact | - An improved understanding of the physical properties of aphid elicitors that induce PTI-like plant defense responses. |
| Start Year | 2018 |
| Description | Collaboration with University of Oxford |
| Organisation | University of Oxford |
| Department | Department of Plant Sciences |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Visited colleagues at University of Oxford to discuss specific project and experimental approaches |
| Collaborator Contribution | Contributed knowledge and resources for new experiments |
| Impact | Progress with achieving research goals by graduate student and postdoctoral researcher in the lab. Making plans for a collaborative research proposal. |
| Start Year | 2019 |
| Description | Imperial College London |
| Organisation | Imperial College London |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Exchange of research methods, knowledge and results |
| Collaborator Contribution | Exchange of research methods, knowledge and results |
| Impact | Exchanged ideas for research projects and grant proposals. |
| Start Year | 2024 |
| Description | British Society for Plant Pathology (BSPP) meeting, Birmingham, UK |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Attended the British Society for Plant Pathology (BSPP) meeting, Birmingham, UK, 4-7 Sep '23. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://www.bspp.org.uk/conferences/plant-pathology-2023/ |
| Description | INRAE Rennes |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Visited Dr. Akiko Sugio and Dr. Jean-Christophe Simon, INRAE Rennes and presented a talk, 30 Jun - 1 Jul '23. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://igepp.rennes.hub.inrae.fr/listes-des-personnes/s/sugio-akiko |
| Description | Imperial College, London, UK |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Visited group of TG, Imperial College, London, UK, with postdoctoral researchers and MSc student of my group, to discuss progress on aphid research, 8 Mar,'24. |
| Year(s) Of Engagement Activity | 2024 |
| Description | NBI Accessible Science Talks 10th Oct 2023 |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Schools |
| Results and Impact | NBI Accessible Science Talks 10th Oct 2023- part of the organising committee for the event that saw half a day of accessible science and careers talks from NBI staff to an audience of staff students, and science students from a number of schools and colleges. https://www.jic.ac.uk/blog/science-is-more-than-just-scientists-an-inspirational-accessible-science-event/ https://www.jic.ac.uk/event/norwich-bioscience-institutes-accessible-science-event/ https://www.youtube.com/watch?v=tsZZCsupCjM This event was covered in the Eastern Promise podcast: https://easternpromise.podbean.com/e/episode-89-norwich-research-park-the-technician-commitment/ (''Eastern Promise' explores the full potential of the East of England. We talk to experts, innovators, entrepreneurs, disrupters and more, finding out why the Eastern region is the best place in the UK to start, run and grow a business.') |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://easternpromise.podbean.com/e/episode-89-norwich-research-park-the-technician-commitment/ |
| Description | Participation in International student course |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Contributed as instructor to the vector-borne disease course at the University of Idaho, June 13-18, 2022. The conference was attended by graduate students and professionals worldwide, including areas afflicted by vector-borne diseases such as Malaria and viruses, in Africa and Asia. |
| Year(s) Of Engagement Activity | 2022 |
| Description | Poster QL at IS-MPMI, Providence, RI, USA |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Poster presentation of QL at the International Society of Molecular Plant-Microbe Interactions (IS-MPMI) meeting, Providence, Rhode Island, USA, 16-20 Jul '23. |
| Year(s) Of Engagement Activity | 2023 |
| Description | Poster presentation MG at IS-MPMI congress, Providence, RI, USA |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Poster presentation of MG at the International Society of Molecular Plant-Microbe Interactions (IS-MPMI) meeting, Providence, Rhode Island, USA, 16-20 Jul '23. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://zenodo.org/records/8180698 |
| Description | Poster presentation SM at IS-MPMI, Providence, RI, USA |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Poster presentation at the International Society of Molecular Plant-Microbe Interactions (IS-MPMI) meeting, Providence, Rhode Island, USA, 16-20 Jul '23. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://zenodo.org/records/10034366 |
| Description | Presented a poster at a technology conference |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Reached out to industry and leaders in the field on CRISPR technologies at the 5th International Conference on CRISPR Technologies, Claremont Club and Spa, A Fairmont Hotel, Berkley, California, USA. The work was presented by PhD student in the team. |
| Year(s) Of Engagement Activity | 2022 |
| Description | Wild Inside Radio 4 recording SM and GSR |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Wild Inside Radio 4 recording. Recorded an episode of BBC4's Wild Inside with Prof Ben Garrod and Dr. Jess French, performing a dissection of an aphid along with discussion of their biology to a national audience, 30 Jan '24. |
| Year(s) Of Engagement Activity | 2024 |
| URL | https://www.bbc.co.uk/programmes/m001vs99 |