Field pathogenomics of wheat yellow (stripe) rust
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Field pathogenomics of wheat yellow (stripe) rust

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Diane Saunders, The Genome Analysis Centre

Diane Saunders, The Genome Analysis Centre

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Field pathogenomics of wheat yellow (stripe) rust Presentation Transcript

  • 1. Field pathogenomics of wheat yellow (stripe) rust Diane Saunders Diane.Saunders@tgac.ac.uk http://yellowrust.com/
  • 2. Pathogen diversity at the field level applying gene sequence technology to the surveillance of emerging and re- emerging plant pathogens Puccinia striiformis f. sp. tritici
  • 3. •  Despite the success of the UKCPVS and our understanding of the phenotypic diversity of wheat yellow rust, we know almost nothing about its genetic diversity in the UK Field pathogenomics of wheat yellow rust No. of samples 1 43 Unknown UK location: 2 Mean incidence of YR on leaves 1998-2007 UKCPVS 212 samples for 2013
  • 4. Field pathogenomics to complement traditional pathology 1. Receive sample from the field 2. Purify and multiply field isolates 3. Infect wheat lines and score infection type 4. Assess phenotypic diversity 4.0 3.0 2.0 1.0 0.0 Susceptible Resistant Phenotype Results reported 6+ months later
  • 5. Field pathogenomics to complement traditional pathology 2. Extract RNA 1. Receive sample from the field 3. Sequence genes with latest technology Gene sequence data from the pathogen Gene sequence data from the host (wheat) 4. Assess pathogen genotypic diversity 5. Determine wheat variety Discussed in: Bayles, Saunders & Uauy (2013) NIAB-TAG Landmark Bulletin Results reported within 1-2 months
  • 6. ID Location Variety 13/65 Cambridge Claire RB1 Cambridge Oakley RB2 Cambridge Torch 13/520 Cambridge Warrior 13/182 Cambridge Crusoe T13/1 Dorset Triticale T13/2 Dorset Triticale T13/3 Dorset Triticale 13/27 Gloucestershire Oakley 13/26 Gloucestershire Haratio 13/28 Gloucestershire Solstice 13/20 Hampshire KWS Rowan 13/23 Lincolnshire KWS Santiago 13/42 Lincolnshire Audace 13/09 Lincolnshire Oakley 13/33 Lincolnshire Recital 13/38 Lincolnshire ReR138 13/123 Lincolnshire Ambition 13/40 Lincolnshire Fairplay 13/25 Lincolnshire KWS Dali 13/30 Lincolnshire ReR22 13/12 Lincolnshire RW41088 13/35 Lincolnshire ReR64 13/34 Lincolnshire ReR32 13/22 Lincolnshire Solstice 13/39 Lincolnshire Toisandor 13/37 Lincolnshire ReR11O 13/24 Lincolnshire KWS Kielder 13/36 Lincolnshire Allez-Y 13/32 Lincolnshire ReRO4 13/120 Lincolnshire Ambition 13/21 Norfolk Solstice CL1 Norfolk Triticale 13/29 Oxford Solstice 13/17 Suffolk Duxford 13/15 Suffolk Torch 13/19 Suffolk LGW56 (Panacea) 13/18 Suffolk KWS196 (KWS Dali) 13/14 Suffolk Oakley 13/71 Yorkshire Laurier •  Gene sequence data from 40 samples •  Samples selected based on location and wheat variety Field pathogenomics data UK 2013 1 9   Resistancerating 2012/13 Crusoe (8) Torch (4) Oakley (2)
  • 7. How are these samples related to other archived isolates?
  • 8. Warrior isolate different to older UK isolates •  Based on full genome sequences •  Lengthy and time- consuming process SCPRID partners  
  • 9. 2013 field isolates: similar genetically to Warrior Maximum likelihood 4275 genes 3rd codon position
  • 10. Four distinct populations in 2013 field isolates Bar chart: STRUCTURE, K=4 Lincolnshire Norfolk Gloucestershire Suffolk Cambridgeshire Yorkshire Oxfordshire Hampshire Dorset Location 0001 13/123 13/19 13/520 13/26 13/15 13/18 RB1 RB2 13/65 13/32 13/71 13/40 13/29 13/30 13/12 13/35 13/39 13/36 13/34 13/14 13/24 13/20 13/25 13/37 13/28 13/22 13/42 13/23 13/33 13/182 13/21 13/09 13/38 T13/3 CL1 T13/2 T13/1 13/120 13/27 Cluster I Cluster II Cluster III Cluster IV 100 100 100 100 100 100 57 51 Triticale
  • 11. Tri'cale   Correlation between genotypic and phenotypic data SP, Spaldings Prolific; War, Warrior; Amb, Ambition; Tim, Timber; Ren, Rendezvous. 13/26 13/65 13/09 13/182 13/24 13/36 Yr7 SP War Amb Tim Ren 3.1 3.0 3.5 4.0 3.5 1.2 3.5 3.1 3.3 4.0 3.2 1.9 2.0 1.0 2.1 0.0 0.0 3.0 3.2 0.0 1.9 0.0 0.0 3.3 3.0 3.0 1.8 0.6 0.6 2.2 3.2 3.5 2.1 0.1 2.2 3.2 Cluster I Cluster III Cluster IV 13/120 2.8 3.1 3.0 3.5 3.1 0.0 13/123 3.0 3.0 3.0 3.2 4.0 1.4 13/29 3.2 4.0 2.1 0.4 0.3 2.8 4.0 3.0 2.0 1.0 0.0 Susceptible Resistant Phenotype 0001 13/1 23 13/19 13/520 13/26 13/15 13/18 RB1 RB2 13/65 13/32 13/71 13/40 13/29 13/30 13/12 13/35 13/39 13/36 13/34 13/14 13/24 13/20 13/25 13/37 13/2 8 13/22 13/42 13/23 13/33 13/182 13/2 1 13/09 13/38T13/3 CL1 T13/2 T13/1 13/120 13/27 Cluster I Cluster II Cluster III Cluster IV 100 100 100 100 100 100 57 51 13/26 13/65 13/09 13/1 Yr7 SP War Amb Tim Ren 3.1 3.0 3.5 4.0 3.5 1.2 3.5 3.1 3.3 4.0 3.2 1.9 2.0 1.0 2.1 0.0 0.0 3.0 3.2 0.0 1.9 0.0 0.0 3.3 Cluster I Cluster III 13/120 2.8 3.1 3.0 3.5 3.1 0.0 13/123 3.0 3.0 3.0 3.2 4.0 1.4
  • 12. Tri'cale   Correlation between genotypic and phenotypic data SP, Spaldings Prolific; War, Warrior; Amb, Ambition; Tim, Timber; Ren, Rendezvous. 13/26 13/65 13/09 13/182 13/24 13/36 Yr7 SP War Amb Tim Ren 3.1 3.0 3.5 4.0 3.5 1.2 3.5 3.1 3.3 4.0 3.2 1.9 2.0 1.0 2.1 0.0 0.0 3.0 3.2 0.0 1.9 0.0 0.0 3.3 3.0 3.0 1.8 0.6 0.6 2.2 3.2 3.5 2.1 0.1 2.2 3.2 Cluster I Cluster III Cluster IV 13/120 2.8 3.1 3.0 3.5 3.1 0.0 13/123 3.0 3.0 3.0 3.2 4.0 1.4 13/29 3.2 4.0 2.1 0.4 0.3 2.8 4.0 3.0 2.0 1.0 0.0 Susceptible Resistant Phenotype 0001 13/1 23 13/19 13/520 13/26 13/15 13/18 RB1 RB2 13/65 13/32 13/71 13/40 13/29 13/30 13/12 13/35 13/39 13/36 13/34 13/14 13/24 13/20 13/25 13/37 13/2 8 13/22 13/42 13/23 13/33 13/182 13/2 1 13/09 13/38T13/3 CL1 T13/2 T13/1 13/120 13/27 Cluster I Cluster II Cluster III Cluster IV 100 100 100 100 100 100 57 51
  • 13. How many genes are specifically differentially expressed for a particular population cluster?
  • 14. How many genes are differentially expressed? No. of genes specifically differentially expressed in all pair-wise comparisons What is the function of the genes that are specifically differentially expressed? FDR = 0.05; p-value 0.05
  • 15. What are the functions of the proteins they encode? •  Small proportion of proteins annotated •  All predicted secreted proteins are non- annotated •  Several secreted proteins are in our list of top effector candidates (Cantu et al. Saunders & Uauy BMC Genomics, 2013)  
  • 16. Could these genes encode polymorphic effector candidates? Secreted candidate effector protein
  • 17. Can we differentiate the wheat variety in the transcriptome data?
  • 18. Can RNAseq data differentiate wheat varieties? Match to Variety 1 AAGTGCCTCGGATCGAT C 100% match CT Partial match T No match Match transcriptome SNPs to variety of interest Warrior sample: 52-65 SNPs Sample from Warrior
  • 19. Accelerating the response to changes in population dynamics •  Rapid progress from sample submission to comprehensive genotyping •  Ability to detect important new pathotypes that may emerge •  Generation of a valuable countrywide record of field isolates ➔ New models for population dynamics Rapid response to emerging/re-emerging pathogens is vital to keep pace with fast evolving pathogens in a changing environment
  • 20. Tools and data available via yellowrust.com http://yellowrust.com/ •  Raw reads relating to genome sequences of 18 PST isolates •  Phenotypic data from field trials of the Watkins landraces New tools to access published data from five PST isolates Unpublished data available to download (SCPRID project)
  • 21. Acknowledgments TSL: Sophien Kamoun Kentaro Yoshida Dan MacLean Graham Etherington JIC: Cristobal Uauy Clare Lewis Albor Dobon TGAC: Mark McMullan UEA: Cock Van Oosterhout NIAB: Tina Barsby Rosemary Bayles Jane Thomas Amelia Hubbard SCPRID partners