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Identification and characterization of effector genes from wheat stripe rust
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Identification and characterization of effector genes from wheat stripe rust

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John Rathjen, The Australian National University

John Rathjen, The Australian National University

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  • 1. Discovering  the  effector  genes  of   Puccinia  striiformis  f.sp.  tri.ci   John  Rathjen   The  Australian  Na;onal  University  
  • 2. Stripe  rust  and  Australian  wheat  produc;on     Annual  losses   Control  cost   GM  Murray  &  JP  Brennan  2009.  Grains  Research  &  Development   Corpora?on.  Australian  Government    
  • 3. Stripe  rust  and  Australian  wheat  produc;on     Annual  losses   Control  cost   GM  Murray  &  JP  Brennan  2009.  Grains  Research  &  Development   Corpora?on.  Australian  Government    
  • 4. Urediniospores  (2n)   Wheat   Teliospores  (2n)   Dikaryo?c  –     Sexual  host   Two  haploid  nuclei   insignificant  in     Australia   Meiosis  alternate   Basidiospores     host   (1n)   Aeciospores     (2n)   Pycniospores     (1n)   Barberry  hAp://www.apsnet.org/edcenter/intropp/lessons/fungi/Basidiomycetes/Pages/StemRust.aspx  
  • 5. P.  striiformis  in  Australia     Psd   BGYR   (2000)   Pst-­‐1979   Psp   (~20  strains)   Pst-­‐WA     (2002)  Puccinia  striiformis  f.sp.  tri0ci  Barley  grass  yellow  rust    (~6  strains)  Psd–  grows  on  Dactylis  glomerata  (Cocksfoot)  Psp  –  grows  on  Poa  pratensis  (Kentucky  blue  grass)  Stripe  rust  of  Phalaris  spp.,  Bromus  spp.,  “wheat  grass”,  etc,  etc  
  • 6. How  can  we  define  effector  genes?  •  Generally,  effectors  are  thought  to  be  small  secreted   proteins.  •  This  is  sufficient  to  build  a  list  of  such  proteins  if   genomic  sequence  is  available.  •  In  some  cases,  amino  acid  mo?fs  such  as  RxLR  or  YxC   are  present…but  don’t  seem  to  be  diagnos?c.  •  Another  important  criterion  is  expression  of   candidate  effector  genes  in  planta,  where  that   informa?on  is  available.  
  • 7. Puccinia genomics•  Pgt (stem rust) genome (Duplessis et al. 2011) is about 90 Mb, encoding about 17,000 genes – Pgt expected to be similar.•  This was assembled with a lot of “last-generation sequencing” which helps with scaffolding and sequence assembly.•  Transposable elements account for about 45% of the genome.•  Calling genes from NGS assemblies can be problematic, and can be difficult to detect expression of fungal genes in infected tissue (but these are the most interesting genes).•  There are ongoing unresolved problems with the dikaryotic nature of rusts.•  Broad Institute (Cuomo) has a good Pst assembly in the pipeline.
  • 8. Perils  and  pi`alls  of  next-­‐genera?on  sequencing   (NGS).  •  NGS  –  boAom  up  or  ‘shotgun’  assembly  of  millions   of  small  sequence  reads,  using  high-­‐performance   compu?ng.  Technologies  include:  •  Illumina  –  millions  of  very  short  reads  (~100  bp).  •  Roche-­‐454  –  fewer  numbers  of  longer  reads  (~500   bp).  •  Tradi?onal  (Sanger)  sequencing  –  long  reads   800-­‐1000  bp.  
  • 9. DNA  sequencing;  the  impossible  triangle   NGS   Tradi?onal  Sanger  sequencing  of  physical  con?gs  
  • 10. Perils  and  pi`alls  of  next-­‐genera?on   sequencing  (NGS).  AATATAAAACCAAAGATACTGATATCTTAGCGGCTTTCCGAATGACCCCACAACCTGGAG  
  • 11. Nucleus  1   Nucleus  2  
  • 12. Detec?ons  of  sequence  polymorphisms  in  small-­‐ read  assemblies   X   X   X   X  AATATAAAACCAAAGATACTGATATCTTAGCGGCTTTCCGAATGACCCCACAACCTGGAG   C/G  
  • 13. Detec?ons  of  sequence  polymorphisms  in  small-­‐ read  assemblies  -­‐  II   X   X   X   X   X   X   X   X  AATATAAAACCAAAGATACTGATATCTTAGCGGCTTTCCGAATGACCCCACAACCTGGAG   T/A   C/G  
  • 14. Detec?ons  of  sequence  polymorphisms  in  small-­‐ read  assemblies  -­‐  II   X   X   X   X   X   X   X   X  AATATAAAACCAAAGATACTGATATCTTAGCGGCTTTCCGAATGACCCCACAACCTGGAG   T/A   C/G   T   C   A   C   The  “phase”  problem   T   G   A   G  
  • 15. Repeats  and  mul?copy  genes  are  difficult  to  assemble  from  small  reads   Repeats  (transposons…effectors?)  assemble  poorly  or  not  at  all.   This  is  obvious  in  NGS  genome  assemblies.   It’s  a  considerable  problem  for  genomics  of  Puccinia  spp.  
  • 16. NGS datasets for stripe rust bioinformatics Transcriptome   Genome   454  mate-­‐pair   454  RNA-­‐seq   Illumina  mate-­‐paired   Illumina  RNA-­‐seq   Illumina  pair-­‐end  (2)   454  RNA-­‐seq   Illumina  RNA-­‐seq  
  • 17. 454 sequencing of isolated haustoria transcriptome 16831 contigs Contamina;on  removal   14682 contigs Secreted  proteins  predic;on   Non-­‐transmembrane  domains   1299 ORFs-SP Unique  or  non-­‐overlapping  ORFs   515 ORFs-SP Illumina Protein  length  ≤  300aa  sequencing 418 ORFs-SP High  expression   100 ORFs-SP Lab tests
  • 18. Prediction of small secreted proteins (SSPs) from the haustorial transcriptome 433    ≤  300  aa   Protein  length   No  memes   98  >    300  aa   No  clusters/tribes   311  ≤    4  Cysteines     Cysteine  content     220  >    4  Cysteines   91  have  1  mo?f  ,  18  in  the  ‘correct’  loca?on   Y/F/WxC  mo?f     42  have  2  mo?ves,  23  correct  loca?on   9  have  3  or  more  mo?ves,  8  correct  loca?on   Invertase                    BLASTn                                        BLASTx   1,3-­‐β-­‐glucosidase   Pgt  hypothe?cal  protein     74   211   Pepsin  A    e-­‐val  ≤  10-­‐25     Chi?n  deacetylase   Glucose-­‐regulated  it  rotein  from  Pgt)   Specific  h p (most   38   29   Previous  SP  from  Pst      e-­‐val    >  10-­‐25     Not  available   419   291    
  • 19. Validation and investigation of effector candidates AvrM  type-­‐III  delivery/  P.  fluorescens   AvrM  75   avrM  24   Agro/AvrM   Narayana  Upadhyaya  and  Diana  Garnica   100  sequenced  and  cloned  in  TOPO     Ø R-­‐AvrR  recogni?on  assay   Ø Inhibi?on  of  plant  cell  death   Ø Localisa?on   Ø Influence  on  host  metabolism  
  • 20. PST-80 housekeeping genes are not single allele Housekeeping  Gene  Copy  Number 10 9 8 7 Copy  number 6 5 4 3 2 1 0 18 39 60 81 221 102 123 144 165 186 207 233 254 275 312 333 368 389 418 453 483 521 295 467 443 511Boeva  V,  et  al.  (2011)  Control-­‐FREEC:  Bioinforma?cs.  2011  Dec  6    
  • 21. PST-80 Effector genes are present with variable copy number Effector  gene  copy  number   PST_80  Effector  Allele  Number   7   Effector  Allele   Number,  6   6   5  Copy  number  Allele  Number   4   3   2   1   0   1   21   41   61   81   101   121   141   161   181   201   221   241   261   281   326   346   366   415   456   290   486   471   519   308   494   434   Effector  gene,  nominal  ranking  
  • 22. Effector copy number variations between Pst-80 and BGYR Effector  gAllele  Number   Effector  ene  copy  number   7   6   Axis   umber   5  Copy  nTitle   4   3   2   1   0   1   51   101   151   201   251   301   351   401   451   501   Effector  rAxis  Tnominal)   ank  ( itle  
  • 23. Copy  nNumber   Allele   umber   0   2   4   6   8   10   12   1   13   25   37   49   61   73   85   97   109  Cantu  et  al.  PLOS  One  (2011)   121   133   145   157   169   181   193   205   217   229   241   253   265   277   289   Effector  Number   301   313   325   337   349   361   Effector  gene  copy  number   Effector  number  (nominal)   373   PST_130  Effector  Allele  Number   385   397   409   421   433   445   457   469   Effector copy number variations 481   493   between Pst-80 and Pst-130 (US) 505   517   Allele   Effector   Number  
  • 24. Housekeeping  genes  do  not  show  the   same  degree  of  varia?on  in  copy  number   Conserved  Gene  Copy  Number BGYR   Control-­‐FREEC  predic?on  of  CNVs   Pst-­‐80   7Predicted  Copy  N umber 6 5 4 3 2 1 0 1 51 101 151 201 251 301 351 401 451 501 Gene Boeva  V,  et  al.  (2011)  Control-­‐FREEC:  Bioinforma?cs.  2011  Dec  6    
  • 25. Copy  number  varia?on  in  Pst  effectors  •  Copy  number  varia?ons  are  readily  apparent   in  Pst  effector  genes,  with  many  single  copy.  •  Sequence  polymorphisms  are  also  apparent,   but  these  are  harder  to  annotate  because  of   NGS  assemblies.  •  Single-­‐copy  effectors  may  allow  the  pathogen   to  mutate  rapidly  to  virulence.  
  • 26. Barley grass yellow rust (BGYR) – a stripe rust that jumped? wheat   Barley  grass   BGYR   (2000)  Wheat  stripe   (1980)   Stripe  rust  and  BGYR  99+%  iden?cal  in  effector  genes  so  far  sequenced  
  • 27. Sequencing summary•  We amplified and sequenced the PCR products of 50 candidate effector genes from Pst-80 and BGYR and found 99 single nucleotide polymorphisms (SNPs).•  These were ALWAYS of a particular pattern – twin peak ‘dimorphisms’, rather than clear SNPs (dSNPs).•  50 of these wereinformative dSNPs - 34 from BGYR, and 16 from Pst-80.•  We amplified and sequenced these alleles from BGYR and Pst-80.•  When we did this, we found that BGYR ALWAYS shared an allele with Pst-80, and the alternative allele was divergent.•  We think that this is related to the dikaryotypic nature of P. striiformis.
  • 28. 5 3 5 3 1 1Pst-­‐80   2 2 8 6 8 6 4 7 4 7 5 3 5 3 1 1 2 2 BGYR   8 6 8 6 4 7 4 7
  • 29. 5 3 5 3 1 1 2 2BGYR   8 6 8 6 4 7 4 7
  • 30. Model for the origins of BGYR Pst BGYR unknown ancestorAnastamosis +Heterokaryosis BGYR
  • 31. Where did BGYR come from?•  One line of evidence suggests that heterokaryosis is an underlying mechanism for the host jump – but we need to address the phase problem.•  In the 1950’s, this was proposed as a mechanism to explain frequent mutation to virulence of stem rust on wheat.•  We have detected four deleted effector genes, and will test these for recognition on barley grass by bacterial delivery.•  Heterokaryosis potentially increases effector hemizygosity, which could both increase the effective effector compliment (for virulence) and allow rapid deletion of recognised effectors.
  • 32. Acknowledgments    •  Diana  Garnica  •  William  Jackson  •  CSIRO  Black  Mountain  •  Narayana  Upadhyaya    •  Peter  Dodds  •  Jeff  Ellis  •  Univ  Sydney  CobbiAy  •  Colin  Wellings   Robert  Park  •  Univ  Exeter,  UK  •  David  Studholme  
  • 33. Germinated  spores:  Ø Use  lipid  reserves  to  generate  energy  Ø Grow  (DNA  replica?on,  cell  division)  Ø Modify  chi?n  to  avoid  recogni?on     Haustoria:   Ø Take  nutrients  (sugars  and  aminoacids)   from    host   Ø Generate  precursors  of  metabolites  and   energy   Ø Biosynthesise  compounds  necessary  for   the  ul?mate  produc?on  of  spores     Ø Secrete  pathogenicity  factors  (effectors)    
  • 34. Many effector genes are single copy PST_80  Effector  Copy  Number,  Allele  Number  and  SNP   14   80   Number   12   70  Copy,  Allele  and  SNP  Number   60   10   Effec 50   tor   8   Cand 40   idate   6   Copy   30   Num 4   ber   20   2   10   0   0   1   23   45   69   91   113   135   157   179   201   223   245   267   336   409   431   492   290   436   517   502   398   314   358   Effector  Number  
  • 35. Copy,  Allele  and  SNP  Number   0   2   4   6   8   10   12   14   16   1   13   25   38   50   62   74   86   98   110   122   134   146   159   172   185   198   211   223   235   247   259   271   283  Effector  Number   295   307   320   332   344   356   368   380   PST_130  Effector  Gene  Variability   392   404   416   428   440   452   464   476   488   500   512   PST_80 effector genes in PST_130 0   20   100   120   have undergone significant modification 40  SNP   Copy   Allele   Effector   60  Effector   80  Effector   Number   Number   Number  
  • 36. Mapping  BGYR  genomic  reads  against  500   ‘conserved’  Pst  genes   Conserved  Gene  Copy  Number BGYR   Control-­‐FREEC  predic?on  of  CNVs   Pst-­‐79   7Predicted  Copy  N umber 6 5 4 3 2 1 0 1 51 101 151 201 251 301 351 401 451 501 Gene Boeva  V,  et  al.  (2011)  Control-­‐FREEC:  Bioinforma?cs.  2011  Dec  6    
  • 37. Mapping  BGYR  genomic  reads  against   500  Pst  effector  candidates   Effector  Candidate  Copy  Number BGYR   Control-­‐FREEC  predic?on  of  CNVs   Pst-­‐79  86420 1 51 101 151 201 251 301 351 401 451 501 GeneBoeva  V,  et  al.  (2011)  Control-­‐FREEC:  Bioinforma?cs.  2011  Dec  6    
  • 38. ToxA  cell  death  dependent  on  Tsn1  is  suppressed     by  stripe  rust  infec;on   +ToxA   +H2O   +ToxA  +  stripe  rust   stripe  rust  Diana  Garnica  with  help  from  the  Solomon  lab  
  • 39. PST_79 effector gene Pstv_4835_1 has one copy and two alleles

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