Cause and effects: bottlenecks in the discovery and deployment of effectors and markers for the control of cereal Dothideomycete diseases.

Transcription

1 Cause and effects: bottlenecks in the discovery and deployment of effectors and markers for the control of cereal Dothideomycete diseases. ISSDC Paris 2016

2 Dothideomycete Diseases of Wheat and Barley Wheat Tan spot Yellow spot Yellow leaf spot Septoria nodorum blotch Glume blotch Septoria tritici blotch Septoria leaf blotch Barley Net-type net blotch Spot-type net blotch Ramularia blotch Pyrenophora tritici-repentis Helminthosporium tritici-repentis Dreschlera tritici-repentis Parastagonospora nodorum Phaeosphaeria nodorum Leptosphaeria nodorum Stagonospora nodorum Septoria nodorum Zymoseptoria tritici Mycosphaerella graminicola Septoria tritici Pyrenophora teres f teres Pyrenophora teres f maculata Ramularia collo-cygni

3 Are we Major spending yields an constraints appropriate worldwide amount on these disease? Wheat annual production = 855 Mtonne = US$ 171Bn China 126 Mt India 94 Mt Russia 59 Mt USA 55 Mt France 38 Mt Canada 30 Mt Germany 28 Mt Pakistan 26 Mt Australia 25 Mt Ukraine 24 Mt Turkey 19 Mt UK 17 Mt Data sets Brennan and Murray 2009/2010 (Australia GRDC) ** Fones and Gurr FGB (STB in UK, FR, DE) 2015 ** Ciuffetti et al (Tan spot worldwide) 2014 * Borlaug Global Rust Initiative **

4 Worldwide Distributions SNB STB TS SNB STB TS TS STB TS TS TS SNB TS STB

5 Australian Current Losses out of Aus$5000m Brennan and Murray 2010 All other fungi stem and leaf rust $102m Tan Spot Pyrenophora tritici-repentis $212m Rhizoctonia $59m Fusarium $79m Yellow rust $127m Fungicide validation Oliver et al 2014 Salam et al kg/ha Septoria nodorum Blotch Parastagonospora nodorum $108m

6 Australian Current Losses out of 25 Mt Brennan and Murray 2010 Kilotonne pa STB 25 (rising fast) SNB 580 TS 885 All rusts 602 Research Spend $k pa STB 260 SNB 660 TS 900 All rusts 4500

7 European Current Losses out of 90 Mt Fones and Gurr 2014 Germany, UK, France (total production 90 Mtonne) 5-10% average losses on resistant cultivars sprayed up to 3 x Yield Losses Financial Loss Spraying costs Added value of spraying ca. 4 Mtonne 720 to 1440 EuroM 900 EuroM 4700 EuroM

8 Tan spot incidence Ciuffetti et al Genomics of Plant-Associated Fungi: Monocot Pathogens Australia = 3.5% India averaging 15% Most important disease in Canada Heaviest losses in South America and Africa Interpreted as Major = 5%; Minor = 2%;

9 Worldwide losses out of 800 Mtonne Estimated Annual Current Yield Losses STB 11 Mtonne SNB 3.3 Mtonne TS (and Spot blotch) 19 Mtonne All rusts 10 Mtonne Estimated Annual Current Research Spend US$M (papers from 2012) STB 10 (6220) SNB 2 (3250) TS 2 (inc Net blotch 3060) All rusts 50 (12,400) Public sector pre-breeding

10 Necrotrophic Effectors a strategy for progress The NE model and practical breeding TS and SNB Application in Australia Pathogen properties Host properties Breeder and grower awareness Application in ROW Pathogen effector profiles Host effector sensitivities Epistasis Discovering further NE s Other pathogens Discovering NE s in other species Application of NE model in other species

11 Necrotrophic Effectors a improved way forward Pathogen secretes active, stable and important NE into culture filtrate Interaction between a necrotrophic effector and the product of a host dominant sensitivity gene leads to disease Multiple effectors and recognisers in each disease The more matching effectors and recognisers, the more virulence Secreted effector Host recogniser Necrosis DISEASE

12 Necrotrophic Effector-Assisted Breeding Screen primary germplasm for sensitivity to all effectors Avoid use of sensitive germplasm Use sensitive germplasm but select for insensitive progeny Finished cultivar at least as resistant as most resistant current cultivar Increased resources for other traits

13 Known SNB and Tan Spot Effectors Effector Species Recogniser Function Effector versions Notes ToxA ToxB Tan spot Tsn1 5BL NBS-LRR-PK 1 Ubiquitous in Australia SNB 12 Varying activity; Ubiquitous in Australia Tan spot Tsc2 2BS? Gene copy number varies; absent in Australia ToxC Tan spot Tsc1 2AS?? Ubiquitous in Australia Tox1 SNB Snn1 1BS WAK 14 Ubiquitous in Australia Tox3 SNB Snn3 5BS? 6 Ubiquitous in Australia

14 The Role of ToxA in Tan Spot Disease Field resistance strongly correlated with ToxA insensitivity VS S-VS S MS-S MS MR-MS MR More than 100,000 breeding lines tested with ToxA by Australian breeders since 2009 Brennan and Murray alerted breeders and growers Previously disregarded as an important disease

15 Untargeted Discovery Many further effectors exist ToxA deleted strains Sunguard WT toxa-1 toxa-2 Estoc WT toxa-1 toxa-2 Scout WT toxa-1 toxa-2

16 Effectors in P. nodorum No cultivars that lack sensitivity to all three effectors Sensitivity varies to 1 and 3 Sensitivity also to culture filtrate lacking A, 1 and 3?? Sensitivity to any one effector is enough to give susceptibility S-VS S MS-S MS MR-MS MR Tox3 SNB rating Tox1 ToxA tox13 NA NA NA NA NA NA Derrimut Halberd Tincurrin Lincoln Zippy Tammarin Rock Espada Eradu Clearfield STL Axe Correll Arrino Binnu Westonia Mace EGA Bonnie Rock Young Bumper Wilgoyne Mitre Janz GBA Sapphire Bullaring Gladius EGA 2248 Bolac Yandanooka Braewood EGA Wentworth Wyalkatchem H45 Cascades Calingiri Datatine Clearfield JNZ Carinya Annuello AGT Scythe Catalina Spear EGA Gregory Machete EGA Bounty Carnamah H46 Sentinel Fortune Yitpi Frame Camm Cadoux Stiletto GBA Ruby EGA Eagle Rock Blade Magenta Fang Ellison EGA Jitarning Chlorosis/necrosis Mixed sensitivity Mild chlorosis

17 P. nodorum deleted in SnToxA, 1 and 3 can still infect Calingiri Emu Rock Halberd SN15 toxa13 Gelatin SN15 toxa13 Gelatin SN15 toxa13 Gelatin P. nodorum toxa13 still produces necrosis-inducing factor(s)

18 Calingiri x Wyalkatchem population SnToxA SnTox1 SnTox3 Calingiri +++ Wyalkatchem Commercial WA wheat varieties. Double haploid population (~230 lines) from Intergrain. SSR, EST-SSR and DArT markers (AWBMMP). We infiltrated the population with expressed SnTox1 and SnTox3 proteins, scored the symptom and performed QTL analyses. 0 = insensitive 1 = slight chlorosis 2 = chlorosis 3 = chlorosis with some necrosis 4 = necrosis

19 We then examine the relationship between disease and effector sensitivity on a population subset * * * * * * ** * * *

20 Tox3 expression is repressed by Tox1

21 Necrotrophic Effector-Assisted Breeding SNB and TS All evidence supports elimination of effector sensitivities as a valid strategy Zero or small gain in resistance possible Effector absent Epistasis Too small to measure phenotypically No cases of increased susceptibility

22 Effector-assisted breeding in ROW Limited knowledge of TS and SNB disease prevalence TS and SNB isolates that lack A, 1 and 3 Tsn1 wheat only ~ 10% of cultivars in EU McDonald, Stukenbrock, McDonald, Friesen, Lillemo FSOV WEAB, ERA-CAPS, Dk Multires, Nordic

23 Discovering further effectors Proteomic route 3Ko culture filtrate Pan-genomic route NE gene presence correlated with virulence Genome assemblies Effector gene prediction Small, cys rich, secreted Coding Quarry PM dn/ds Dispensable chromosomes AT-rich regions

24 Effector Prediction

25 Fungal gene prediction CodingQuarry: using RNA-seq data to improve gene prediction accuracy TESTA AC, HANE JK, ELLWOOD SR & OLIVER RP (2015) CodingQuarry: Highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts. BMC Genomics (1) 170 CodingQuarry-PM (pathogen mode): predicting effector genes that are missed by other methods Alison Testa, James Hane

26 CodingQuarry Fungal specific Self-trains Uses RNA-seq transcripts Capitalises on high-quality of fungal transcript reconstruction Corrects for merged transcript errors

27 CodingQuarry prediction steps

28 What about missed genes? Lots of effectors are missed RNA-seq driven methods do a better job But what about when RNA-seq coverage is low or non-existent? Species Isolate Effector Fusarium oxysporum f. sp. lycopersici Leptosphaeria maculans Magnaporthe oryzae Passalora fulva Cuff. ORF 4287 Avr3(Six1) M M C C C C P P P Six2 M M C C C C C C C SIX6 M M P P P C P P P Six5 M M C C M C C C C Avr2/SIX3 C C P P P P P P P v AvrLm4-7 P P M M M M M M C AvrLm6 M M M M M M M M C AvrLmJ1 C C M M M M M M M AvrLm1 P P P P M M P P P AvrLm2 C C C C M M C C C Avr-Pita M M M M M M M M M Avr-Pik M M M M M M M M M Pwl2 M M M M M M M M C Pwl3 M M C C C C C C C Bas1 M M M M M M M M C Bas107 M M C C C C C C C Avr-Piz-T M M M M M M M M C Bas4 M M C C C C C C C Bas2 C M C C C C C C C MC69 C M C C M M C C C Bas3 P P C C C C P P P Avr-Pi9 C M C C P C C C C Slp1 M M C C C C C C C CBS Avr2 C P M M M C M C C Arv5 C C M M M C M C C Avr9 C C M M M P M C C Ecp5 C C M M M C M C C Avr4 C C C C C C C C C Avr4E P M M M M M M M C Ecp2 C C C C C C C C C Ecp4 C C P P P C C C C Ecp6 C C C C C C C C C Ecp1 C C P P M M C C C GM-ES GM-ET AUG. BRA. CQ CQ+D CQ-PM

29 CodingQuarry-PM CodingQuarry-PM specifically targets effectors that are missed by other methods Does not rely on RNA-seq Effectors that are not expressed under in vitro conditions

30 Why are effectors missed? Parameters trained on general gene sets don t match effector coding sequences well High cysteine content Atypical codon usage Signal peptide CodingQuarry pathogen mode CodingQuarry-PM Finds missed genes in intergenic regions Trained to suit effector-like genes

31 AT-rich regions in fungal genomes OcculterCut: a novel tool to describe the presence and properties of AT-rich regions Effectors are present in AT-rich regions RIP -- FYKIN and PAG genes

32 AT-rich regions and effectors Fungal species Effector/avirulen ce gene name Distance to closest AT-rich region (nt) Passalora fulva Avr2 34,144 (Cladosporium fulvum) Avr4 5,240 Avr5 0 Avr9 688 Ecp1 1,317 (scaffold end) Ecp2 4,932 Ecp5 271 Ecp6 31,096 Leptosphaeria maculans AvrLm6 0 AvrLm4-7 0 AvrLmJ1 0 AvrLm1 0 AvrLm11 0 (Images from INRA) (Mesarich, C.H. et al, 2014; de Wit, P.J.G.M. et al, 2012; Rouxel, T. et al, 2011; Fudal, I. et al, 2010; Parlange, F. et al, 2009; Van de Wouw, A.P. et al 2014; Balesdent, M.H. et al, 2009)

33 OcculterCut: an easy to use tool to document AT-rich genome regions Genome segmentation Recursive Jensen-Shannon divergence segmentation method used in some isochore studies GC-content of genome segments Bimodal or unimodal? Fit a mixture of Cauchy distributions to the data Select GC cutoff Categorises genome segments

34 Survey of 500+ fungal genomes 14 species (10 plant associated) with a higher AT-rich region content than L. maculans Different shapes, sizes and peak GC contents Broad variation in the GC cutoff selected

35 AT-rich regions survey outcomes Common in the Pezizomycotina subphylum More common in plant associated fungi

36 Dinucleotide frequency patterns RIP is known to have a CpA to TpA bias in some species

37 Possible non-synonymous amino acid changes resulting from C to T and G to A transitions

38 Possible non-synonymous amino acid changes resulting from C to T and G to A transitions

39 Coding sequences in AT-rich regions: Gene content of 19 fungi assessed Different patterns of amino acid usage

40 TS and SNB ok but - why aren t we finding NE s in the other pathogens Inadequate genomic resources NE s not secreted in vitro (ToxA in SNB) Mixed NE and biotrophic effector pathogens Resistance breakdown (dominant Avr) P. teres, Zymoseptoria, A. lentis

41 Current Status Linear hypothesis testing Culture filtrate induces necrosis in susceptible cultivars Y or N. Resistance is recessive. Isolate all relevant effectors but be aware that some my not be secreted, some may be Biotrophic. Use individual and characterised isolates Extend TS and SNB effector screening to ROW Wheat Initiative EWG Quantify losses Culture collection

42 Kar-Chun Tan Huyen Phan Caroline Moffat Pao-Theen See Simon Ellwood James Hane Alison Testa Rob Syme Acknowledgments Peter Solomon Tim Friesen Justin Faris Bruce McDonald James Cockram Lise Jorgensen Marc-Henri Lebrun Morten Lillemo