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Targeting soil pathogens – opportunities and challenges for plant biotechnology Anne Osbourn – John Innes Centre Some examples of soil-borne diseases Rhizomania Scab Root rot Nematodes Vascular wilts Soil-borne diseases • Causal agents - viruses, bacteria, fungi, oomycetes and nematodes • Spread - vegetative growth, water (propagules), vectors (insect, nematode, fungal), or agricultural practices (ploughing) • The soil: – Protects against temperature extremes, water fluctuations, pesticides – A competitive environment The impact of climate change Mild wet winters and drier summers will favour pathogen multiplication and symptom development UKCIP08 report Impact on sustainability: e.g. take-all disease • Poor nutrient status encourages the disease - Fertilisation required • Diseased plants are unable to absorb nutrients; mineral N accumulates in the soil at harvest, leading to eutrophication Nitrate nitrous oxide Potent greenhouse gas (300 x worse than CO2) Methods for control: • • • • Crop rotation Pesticides Biological control Genetic resistance Take-all disease of wheat: •The most damaging root disease of wheat worldwide •The most widespread and costly disease problem faced by UK cereal growers •Conservative estimate of takeall associated yield losses in the UK £75 m - £300 m Take-all severity increases in successive wheats Yield Take-all level 1 2 3 4 Take-all can make growing second and subsequent wheats unviable 5 Years of cereal cropping Wheat area (million ha) % following wheat 2007 1.83 42% Harvest year 2008 2009 2020 2.08 1.95 ? 35% 52% ? Current methods for take-all control rely on: • Crop rotation • Fungicides • Biological control None of these methods provide effective and durable protection L Thomashow & D Weller J Bacteriol. 1988 August; 170(8): 3499–3508. Potential sources of take-all resistance 60 s.e.d. 8.56 FPr<0.01 Ease of transfer Take-all index (max. 100) 50 40 30 20 10 Aegilops Triticum Triticeae species Transgenesis Introgressions Wild emmer Synthetics Hexaploid wheat Traditional crossing Oa t Rye Ry e Oat He 0 re w ard Ro big us T.m .M DR 03 7 Tr T.m itical e .M DR T.m 22 9 .M DR 04 6 Degree of resistance? Defining and deploying genetic resistance to take-all Take-all LoLa Extreme resistance Characterised Oat genes Oat Transformation (GM) Terpene metabolic engineering Rapid test of other cloned genes Systematic screens Triticeae spp. Introduction into hexaploid wheat and validation Sources of take-all resistance T. monococcum (WGIN2) Multi-gene technology for wheat Mechanisms of resistance Ae. tauschii Genetic architecture of take-all resistance QTL and fine-mapping Genotyping platforms Alien Introgression Platform Wheat pre-breeding programme (WISP) Anne Osbourn, Cristobal Uauy, Chris Ridout - JIC; Kim Hammond-Kosack and Richard Gutteridge - Rothamsted Research; Emma Wallington, Andy Greenland – NIAB Plant breeders HGCA Sustainable take-all resistance (Durability, stacking) Introduction into hexaploid wheat (beyond lifetime of takeall LoLa) 5-7 MYA Triticum urartu (AUAU) Triticum monococcum (AmAm) 400,000 years eg. Ae. speltoides (“BB”) T. turgidum ssp. dicoccoides (AABB) Wild emmer Ae. tauschii (DD) 10,000 years T. turgidum ssp. durum (AABB) T. aestivum (AABBDD) Effectiveness and nature of resistance 60 s.e.d. 8.56 FPr<0.01 Take-all index (max. 100) 50 40 A B C 30 20 t Oa Ry e He 0 re w ard Ro big us T.m .M DR 03 7 T ri t ica T.m le .M DR T.m 22 9 .M DR 04 6 10 Hexaploid wheat Benchmarking against standards Cytological/molecular characterisation of resistance mechanisms •Phenotypic characterization •Genetic characterization, validation and deployment Oats have extreme resistance to take-all and are used as a break crop in rotations Can we engineer wheat for take-all resistance? Operon-like gene cluster Control of take-all disease of wheat Terpene toolkit ..and ultimately for other complex multi-gene traits? Genetic resistance for the control of take-all disease of wheat Non-GM Promising leads •John Innes Centre •Rothamsted Research •NIAB •UK Plant Breeders GM Suite of different resistance mechanisms Extreme resistance (genetically defined) Take-all resistant wheat •Conservative estimate, savings to UK wheat production > £75-300 m/annum Take-all severity increases in successive wheats and then declines (take-all decline – TAD) Yield Take-all level 1 2 3 4 5 Years of cereal cropping Take-all isolates from field-grown wheat can be divided into two subgroups of Gaeumannomyces graminis var. tritici (Ggt) Genetic similarity (%) 80 85 90 95 100 G1 100 G2 Disease severity % 60 50 40 30 20 10 0 0 20 40 G2 frequency (%) Adapted from Lebreton et al. (2004) Environmental Microbiology 6:1174 Adapted from Lebreton et al. (2007) Environmental Microbiology 9:492 60 Ggt population structure changes during take-all epidemics in successive wheat crops 80 Isolate frequencies % 70 G1 R G2 N 60 50 40 30 20 10 0 1st 3rd Wheat crop Adapted from Lebreton et al. (2007) Environmental Microbiology 9:492 4th 6th Take-all inoculum build-up (TAB) Large plots of Avalon x Cadenza DH mapping lines grown as a 1st wheat, then the entire field over-sown with wheat WGIN TSB Each QTL results in a considerable yield increase Avalon x Cadenza 62 DH lines + 4lines parental seed multiplication plots Avalon x Cadenza in 2008,asoversown with in 2008, sown withRelationship Oakley in between 2009 wheat cv. over Oakley in 2009. take-all patch score and yield Relationship between take-all patch score and yield. 11 Cadenza Avalon 10 R = 0.9245 Yield t/ha 9 8 7 6 5 4 0 20 40 60 80 100 Take-all patch score (% area of plot affected) 120 Genotype x environment interactions From: Hardoim et al., Trends Microbiol (2008) 16: 463-471 1011 stars in our galaxy Slide: Eddy Rubin, Joint Genome Initiative, Walnut Creek, US 3/4 of sequenced genomes belong to just three bacterial phyla bacterial and archaeal phyla with Easy to culture isolates Slide: Eddy Rubin, Joint Genome Initiative, Walnut Creek, US Phylum Level Soil v Wheat Rhizosphere 20000 18000 # Reads Assigned 16000 14000 12000 10000 8000 6000 Soil Mean Reads Wheat Mean Reads 4000 2000 0 Phil Poole, JIC # Hits MG_RAST Metabolic Profiling 300 250 200 150 100 50 0 Subsystem Hierachy 1 176 reads in 13 other Subsystems Phil Poole, JIC Recent advances in epidemiological modelling: Soil-borne pathogens Modelling scale effects from small scale to plant, patch, field and regional Epidemiological and Modelling University of Cambridge: Prof Chris Gilligan What modelling will enable us to do: • Integrate a range of complex processes • Identify when a chemical or genetic “treatment” is likely to have a significant effect on epidemic development • Assess the likely effects of selection pressure Towards disease control • Exploit genetic resistance • Understand mechanisms of disease suppression/enhancement • Understand broader genotype x environment interactions (root exudates, rhizobiome) • Exploit predictive modelling • Explore other potential strategies for engineered disease resistance, e.g. PAMPtriggered plant immunity