Understanding the evolution of fungicide resistance Prof John Lucas

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1 Understanding the evolution of fungicide resistance Prof John Lucas The use of chemistry to control pests, pathogens and weeds in crops is, in the main, a success story. A diversity of highly active compounds, which are effective against many of the major threats to crop production, have been developed. These have made an important contribution to yield stability and crop quality for several decades, this situation is now changing. The pipeline of new actives is declining due to an adverse regulatory environment and the increasing costs of discovery and development. At the same time, the efficacy of existing compounds is under threat due to the emergence of pesticide resistance. This problem has increased exponentially since the widespread use of highly active site specific inhibitors. There is now a real concern, analogous to the situation with antibiotics and human diseases, that some pests, pathogens and weeds may soon no longer be adequately controlled by the agrochemicals currently available to growers. While the threat posed by pesticide resistance has increased in recent years, advances in molecular biology, genetics and genomics, have improved understanding of the mechanisms underlying the development of resistance and provided new techniques for the detection and monitoring of resistance in field populations. We now have sensitive and specific technologies for tracking the genetic changes responsible for resistance in time and space. These include methods for sampling pest and pathogen populations by insect or pathogen inoculum trapping coupled to molecular diagnostics. Early warning of the emergence of new resistant biotypes buys time to deploy strategies for managing the problem. The availability of full genome sequences for several pests and pathogens has also provided opportunities to more efficiently define polymorphisms in pesticide target genes, as well as discovering novel resistance mechanisms. This, together with improving technologies for the analysis of gene function, has enabled the impact of identified polymorphisms on phenotype, including inherent fitness cost, to be determined. Parallel genome and RNA sequencing of strains or lab mutants with novel phenotypes can not only provide clues to the mode of action of new molecules, and the genetic potential of field populations to develop resistance, but also insights into the evolutionary processes leading to resistance. Molecular modelling of target site and pesticide interactions can now clarify how mutations affect pesticide binding, and may potentially suggest novel chemical conformations to restore activity. The impact of new technologies on understanding resistance and practical application in risk assessment and resistance management will be illustrated with examples from recent research on fungicides, with particular reference to major chemical classes, such as the QoIs, azoles and SDHIs and the wheat leaf pathogen Mycosphaerella graminicola. The recent evolution of fungicide target proteins and genes under selection by fungicide treatments as revealed by analysis of the Rothamsted long term experiment crop archives will also be presented as a unique case history. Finally, what are the prospects for predicting future resistance problems? Can new technologies inform the probability of resistance development and where and when it will occur?

2 Understanding the evolution of fungicide resistance John Lucas, Hans Cools and Bart Fraaije Department of Biological Chemistry and Crop Protection The importance of fungicides Since the first routine use of fungicides on UK winter cereals in the 197s they have made an important contribution to yield stability and quality. No fungicide 2 sprays triazole 1

3 Average wheat yields in the UK, and % crops sprayed Tonnes per hectare Percent crops sprayed Year MBCs DMIs QoIs SDHIs But was this quick fix for disease control sustainable? 2

4 The evolutionary context Modern single site fungicides are active at low concentrations high selection pressure. Plant pathogens have large populations and short generation times. Mutation and sexual recombination generate genetic variation. Airborne spores facilitate dispersal and gene flow over long distances. Fungicide timings Seed dressing High early disease Foliar diseases Ear diseases pressure Stem base 3

5 Rothamsted 212 Case history: Septoria tritici leaf blotch (Mycosphaerella graminicola Zymoseptoria tritici) Most important foliar disease of wheat in Europe. Major fungicide market Biology and epidemiology Epidemics founded in autumn by airborne ascospores Disease spreads up plant via cycles of splashborne asexual spores Diverse pathogen population with multiple different genotypes in field and on individual host plants. 4

6 Single site fungicides Methyl benzimidazoles Cytoskeleton β tubulin QoIs (strobilurins) SDHIs Energy production Mitochondrial cytochrome b Succinate dehydrogenase Azoles Membrane synthesis Sterol 14α demethylase CYP51 Note: Multisite fungicides chlorothalonil, folpet, dithiocarbamates The crop archives at Rothamsted provide a unique resource to track historical changes in fungicide target genes 5

7 Evolution of fungicide resistance Septoria populations in Broadbalk Use of MBC fungicides MBC resistance (beta tubulin E198A marker) MBC R allele frequency (%) Year High throughput bioassay G143A substitution isolates Azoxystrobin concentration ( g ml 1 ) RF > 1 Alamar Blue as growth indicator 6

8 23 G143A -% high medium low Data: FRAC M. graminicola: Spread of QoI Resistance 24 G143A frequency high medium Source: Bayer CropScience low 7

9 Evolution in response to fungicides 2. QoIs Septoria populations in Broadbalk 1 pathogen DNA QoI resistance using cytochrome b G143A as marker 12 Pathogen DNA (pg) QoI R-allele-frequency (%) Year Use of QoI (strobilurin) fungicides Azoles used to control Septoria leaf blotch tebuconazole flusilazole epoxiconazole prothioconazole 8

10 Cumulative frequency (%) Azole sensitivity shifts Epoxiconazole EC5 curves England 28 Germany 28 France 28 New Zealand 28 England EC5 (ug/ml) Isolates collected from single untreated fields/locations. Azole sensitivity of Rothamsted field populations Cumulative frequency (%) Cumulative frequency (%) Tebuconazole EC5 values (ppm) Prochloraz EC5 values (ppm) Cumulative frequency (%) Cumulative frequency (%) Epoxiconazole EC5 values (ppm) 1 Prothio desthio EC5 values (ppm) 9

11 CYP51 Site of action of azole fungicides Sterol 14α demethylase (CYP51) Essential enzyme of the sterol (ergosterol in most fungi) biosynthesis pathway Belongs to the cytochrome P45 (CYP) superfamily E I β3 CYP51 only P45 present in all phyla Potential ancestor of all bacterial, plant, fungal and animal P45s G F A L B β1 M. graminicola CYP51 (MgCYP51; Mullins et al., 211, PLoS ONE) 1

12 .1 2/9/213 Current MgCYP51 diversity (21 populations) R1-19cds. R1-6cds. R1-26cds. R1-37cds. R1-49cds. R1-28cds. TAG74-6cds. R1-5cds. R1-33cds. R1-36cds. R1-3cds. SAC17cds. R1-16cds. R1-38cds. R1-2cds. L5S, S188N, A379G, I381V, Y459/G46, N513K R1-47cds. R1-34cds. R1-8cds. R1-1cds. R1-24cds. L5S, I381V, Y461S TAG1-35cds. ROS cds. R1-21cds. TAG74-1cds. SAC73-2cds. R1-13cds. TAG74-3cds. TAG1-18cds. R1-53cds. L5S, S188N, A379G, I381V, Y459/G46, S524T L5S, S188N, A379G, I381V, Y459D, S524T R1-1cds. A379G, Y459/G46, N513K L5S, S188N, I381V, Y459/G46, N513K L5S, V136A, S188N, I381V, Y459/G46, N513K R1-52cds. L5S, S188N, Y459/G46, S524T TAG1-16cds. SAC1-35cds. L5S, V136A, S188N, Y459/G46, S524T SAC73-16cds. L5S, V136A, S188N, Y459/G46, N513K R1-7cds. R1-11cds. R1-4cds. R1-18cds. R1-32cds. L5S, I381V, Y459D R1-15cds. R1-29cds. R1-39cds. MgrCYP51F1p. R1-31cds. R1-23cds. R1-14cds. R1-4cds. R1-35cds. R1-22cds. R1-27cds. L5S, I381V, Y461H L5S, V136C, Y461S SAC1-6cds. SAC73-24cds V136C, Y461H. SAC15cds L5S,. V136A, Y461H R1-51cds. TAG74-11cds. BAY6cds. R1-9cds. BAY2cds. TAG74-17cds. ROS cds. TAG1-9cds. ROS1-4-1cds. IC1cds*. D17V, I381V, Y461H L5S, D134G, V136A, I381V, Y461H L5S, V136A, I381V, Y461H, S524T L5S, V136A, I381V, Y461S, S524T TAG71-3cds* L5S,. D134G, I381V, Y461S, S524T IC5cds*. IC4cds*. SAC41cds. D17V, I381V, N513K, S524T L5S, V136A, Y461S, S524T What is the impact of these changes, individually and in combination, on fungicide efficacy and enzyme function? What is the fitness cost to the pathogen? 11

13 Impact of individual mutations I381V Native CYP51 + Native CYP51 1 x 1 6 5x x 1 6 5x 1 3 pyes2 (vector) pyes Mg51wt pyes Mg51L5S pyes Mg51Y461H pyes Mg51L5S/Y461H pyes Mg51I381V pyes Mg51L5S/I381V pyes Mg51I381V/Y461H pyes Mg51L5S/I381V/Y461H Naturally occurring M. graminicola MgCYP51 variant Characterisation of less sensitive isolates Isolate CYP51 variant Epoxi Prochl Tebu Prothiodesthio Reference EC5 values in μg/ml n=4 Wild type Resistance factor n=5 Y137F nd n=5 Y137F & S524T nd n=11 L5S, V136A & Y461H n=35 L5S, I381V & Y461H n=4 L5S, S188N, I381V, & N513K* n=47 L5S, S188N, A379G, I381V, & N513K n=19 L5S, S188N, I381V, & N513K** n=6 L5S, V136A, Y461S & S524T n=2 V136C, I381V, Y461H & S524T n=1 L5S, D134G, V136A, Y461S & S524T n=35 L5S, D134G, V136A, I381V & Y461H n=6 L5S, V136A, I381V, Y461H & S524T n=1 L5S, S188N, A379G, I381V,, N513K & S524T n=1 L5S, S188N, H33Y, A379G, I381V, & N513K n=6 L5S, D134G, V136A, I381V, Y461H & S524T n=6 L5S, V136A, S188N, A379G, I381V, & S524T n=4 L5S, V136C, S188N, A379G, I381V, & S524T n=3 L5S, V136A, S188N, A379G, I381V,, N513K & S524T ** CYP51 over expressing strains (Cools et al., 212, Pest Manag. Sci.) 12

14 Azole fungicide use and the emergence of MgCYP51 alterations in the Broadbalk archive V136A A379G I381V Y459/G46 S524T D134G Y461H Y137F G46D Y461S propiconazole prochloraz triadimefon Y459D tebuconazole cyproconazole flutriafol epoxiconazole metconazole Prothioconazole Evolutionary pathways to azole resistance (Nichola Hawkins) L5S L5S V136C G312A I381V I381V I381V Y461H Y461H Y461H S524T S524T L5S L5S I381V V136A Y461H L5S I381V V136C D17V V136A Y461H L5S I381V I381V L5S I381V S524T D134G Y461H Y461H D17V L5S V136A Y461H.7735 V136A I381V Y461H I381V I381V N513K Y461H Y461H Y137F S524T L5S S524T S524T L5S D134G V136A L5S V136A Y461H D134G I381V V136C V136A Y461H Y461H Y461H L5S A311G L5S L5S Y137F Y461S V136A V136A Y461H L5S L5S V49L I381V A379G Y461S Y461S Y461S I381V S524T S524T Y461S S524T D17V L5S L5S V136G L5S V136A L5S S259F Y461S V136A Y461S D134G L5S Y461S S524T V136A L5S L5S V136C Y461S V136C WT Y461S S524T S188N L5S L5S L5S L5S A379G N178S D134G V136C S188N I381V L5S G476S S188N V136G S188N A379G Δ459/46 V136A Y461S I381V I381V S524T S188N Y461H Δ459/ A379G S524T S524T L5S I381V L5S L5S V136A Δ459/46 Y459C Y459D S188N S188N S188N S542T I381V L5S Δ459/46 L5S.482 Y461H S188N S524T V136A L5S Δ459/46 L5S S188N Y459C V136C I381V L5S L5S S188N L5S Δ459/46 L5S I381V S188N Δ459/46 V136A N513K V136A V136A Y459D G46D N513K L5S N513K S188N S188N S188N S188N Δ459/46 A379G Y459C Δ459/46 N513K L5S I381V L5S L5S N513K V136A Δ459/46 G312A L5S S188N L5S S188N N513K Y459D S188N G46D S188N L5S Δ459/46 L5S S524T A379G I381V S188N N513K S188N I381V L5S Δ459/46 A379G S524T A379G L5S L5S Y459D S188N N513K I381V I381V I381V G312A S524T Y459C Δ459/46 Δ459/46 Y459S I381V N513K N513K N513K Y459D S524T L5S L5S S188N S188N L5S L5S L5S L5S L5S L5S G46D I381V S188N S188N S188N S188N S188N S188N N513K Δ459/46 A379G S28T N284H H33Y A379G A379G N513K I381V A379G A379G A379G I381V I381V ** Δ459/46 I381V I381V I381V A41T G412A N513K Δ459/46 Δ459/46 Δ459/46 Δ459/46 Δ459/46 ** N513K N513K N513K N513K N513K (Prothioconazole desthio used for in vitro testing) Highest EC 5 Lowest EC 5 13

15 What next for azoles and Mycosphaerella? The genome of Mycosphaerella graminicola (isolate IPO323) Position of all predicted genes displayed Chromosomes (OmniMapFree software Antoniw et al., 211, BMC Bioinformatics) Dispensable chromosomes (nearly 6 % of the predicted genes) Total genes: 1,933 Genome size: 39.6 Mb Signal peptides: 1,141 Goodwin et al., PLoS Genetics

16 Genomic insights into fungicide resistance Identification of novel mutations and mechanisms Development of resistance diagnostics Understanding variation within pathogen populations Inputs to modelling target x toxophore interactions resistance breaking chemistry? Identification of new druggable targets Effect of Y137F on triadimenol docking I381 F137 Y137F Triadimenol 15

17 Response to a multisite inhibitor fungicide Omar Gutierrez Alonso Acknowledgements Rothamsted Research Nichola Hawkins Sarah Atkins Omar Gutierrez Alonso Swansea University Josie Parker Steve Kelly Diane Kelly Jonathan Mullins Roberto Togawa Fungicide Research Group, Rothamsted Research BASF, Bayer CS, Du Pont, Syngenta 16