1887

Abstract

Ascochyta blight disease, caused by the necrotrophic fungus , is a major biotic constraint to chickpea production in Australia and worldwide. Detailed knowledge of the structure of the pathogen population and its potential to adapt to our farming practices is key to informing optimal management of the disease. This includes understanding the molecular diversity among isolates and the frequency and distribution of the isolates that have adapted to overcome host resistance across agroecologically distinct regions. Thanks to continuous monitoring efforts over the past 6 years, a comprehensive collection of isolates was collated from the major Australian chickpea production regions. To determine the molecular structure of the entire population, representative isolates from each collection year and growing region have been genetically characterized using a DArTseq genotyping-by-sequencing approach. The genotyped isolates were further phenotyped to determine their pathogenicity levels against a differential set of chickpea cultivars and genotype-phenotype associations were inferred. Overall, the Australian population displayed a far lower genetic diversity (average Nei’s gene diversity of 0.047) than detected in other populations worldwide. This may be explained by the presence of a single mating-type in Australia, MAT1-2, limiting its reproduction to a clonal mode. Despite the low detected molecular diversity, clonal selection appears to have given rise to a subset of adapted isolates that are highly pathogenic on commonly employed resistance sources, and that are occurring at an increasing frequency. Among these, a cluster of genetically similar isolates was identified, with a higher proportion of highly aggressive isolates than in the general population. The discovery of distinct genetic clusters associated with high and low isolate pathogenicity forms the foundation for the development of a molecular pathotyping tool for the Australian population. Application of such a tool, along with continuous monitoring of the genetic structure of the population will provide crucial information for the screening of breeding material and integrated disease management packages.

Funding
This study was supported by the:
  • Grains Research and Development Corporation (Award UM00052)
    • Principle Award Recipient: RebeccaFord
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000627
2021-07-20
2021-08-04
Loading full text...

Full text loading...

/deliver/fulltext/mgen/7/7/mgen000627.html?itemId=/content/journal/mgen/10.1099/mgen.0.000627&mimeType=html&fmt=ahah

References

  1. Bar I, Sambasivam PT, Davidson J, Farfan-Caceres LM, Lee RC et al. Dataset and code release for “Current population structure and pathogenicity patterns of Ascochyta rabiei in Australia”. [Internet]. Zenodo; 2020 https://zenodo.org/record/4311477 accessed 09 Dec 2020
  2. FAO GIEWS Crop Prospects and Food Situation #1, March 2019: Quarterly Global Report. In Crop Prospects and Food Situation (FAO) Vol 1 Rome, Italy: FAO; 2019 www.fao.org/3/ca3696en/CA3696EN.pdf accessed 22 May 2019
    [Google Scholar]
  3. Margier M, Georgé S, Hafnaoui N, Remond D, Nowicki M et al. Nutritional composition and bioactive content of legumes: Characterization of pulses frequently consumed in France and effect of the cooking method. Nutrients 2018; 10:1668 [View Article] [PubMed]
    [Google Scholar]
  4. Wallace TC, Murray R, Zelman KM. The nutritional value and health benefits of chickpeas and hummus. Nutrients 2016; 8:766 [View Article]
    [Google Scholar]
  5. Pulse Australia Chickpea; 2021 http://www.pulseaus.com.au/growing-pulses/bmp/chickpea accessed 26 Jan 2021
  6. Ford R, Moore K, Sambasivan P, Mehmood Y, Hobson K et al. Why adhering to integrated Ascochyta rabiei management strategy is now more important than ever to sustain a profitable chickpea industry. Grains Research and Development Corporation; 2018 https://grdc.com.au/resources-and-publications/grdc-update-papers/tab-content/grdc-update-papers/2018/03/why-adhering-to-integrated-ascochyta-rabiei-management-strategy-is-now-more-important-than-ever accessed 01 May 2019
  7. Mehmood Y, Sambasivam P, Kaur S, Davidson J, Leo AE et al. Evidence and consequence of a highly adapted clonal haplotype within the australian Ascochyta rabiei population. Front Plant Sci 2017; 8:1029 [View Article]
    [Google Scholar]
  8. Barve MP, Arie T, Salimath SS, Muehlbauer FJ, Peever TL. Cloning and characterization of the mating type (MAT) locus from Ascochyta rabiei (teleomorph: Didymella rabiei) and a MAT phylogeny of legume-associated Ascochyta spp. Fungal Genet Biol 2003; 39:151–167 [View Article] [PubMed]
    [Google Scholar]
  9. Fondevilla S, Krezdorn N, Rotter B, Kahl G, Winter P. In Planta identification of putative pathogenicity factors from the chickpea pathogen Ascochyta rabiei by de novo transcriptome sequencing using RNA-seq and massive analysis of cDNA ends. Front Microbiol 2015; 6:1329 [View Article] [PubMed]
    [Google Scholar]
  10. Chen W, Coyne CJ, Peever TL, Muehlbauer FJ. Characterization of chickpea differentials for pathogenicity assay of ascochyta blight and identification of chickpea accessions resistant to Didymella rabiei. Plant Pathol 2004; 53:759–769 [View Article]
    [Google Scholar]
  11. Sambasivam P, Mehmood Y, Bar I, Davidson J, Hobson K et al. Evidence of recent increased pathogenicity within the Australian Ascochyta rabiei population. bioRxiv 2020
    [Google Scholar]
  12. Singh KB, Hawtan GC, Nane YL, Reddy MV. Resistance in chickpeas to Ascochyta rabiei. Plant Dis 1981; 65:586
    [Google Scholar]
  13. Xin Z, Chen J. A high throughput DNA extraction method with high yield and quality. Plant Methods 2012; 8:26 [View Article] [PubMed]
    [Google Scholar]
  14. Kilian A, Wenzl P, Huttner E, Carling J, Xia L et al. Diversity arrays technology: a generic genome profiling technology on open platforms. Methods Mol Biol Clifton NJ 2012; 888:67–89
    [Google Scholar]
  15. Sansaloni C, Petroli C, Jaccoud D, Carling J, Detering F et al. Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high throughput, highly informative genotyping for molecular breeding of Eucalyptus. BMC Proc 2011; 5:P54
    [Google Scholar]
  16. R Core Team 2017 https://www.R-project.org accessed 01 May 2019
  17. Gruber B, Unmack PJ, Berry OF, Georges A. dartR: An R package to facilitate analysis of SNP data generated from reduced representation genome sequencing. Mol Ecol Resour 2018; 18:691–699 [View Article] [PubMed]
    [Google Scholar]
  18. Jombart T, Ahmed I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 2011; 27:3070–3071 [View Article] [PubMed]
    [Google Scholar]
  19. Bougeard S, Dray S. Supervised Multiblock Analysis in R with the ade4 Package. J Stat Softw 2018; 86:1–17 [View Article]
    [Google Scholar]
  20. Dray S, Dufour AB. The ADE4 package: Implementing the duality diagram for ecologists. J Stat Softw 2007; 22:1–20
    [Google Scholar]
  21. Jombart T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 2008; 24:1403–1405 [View Article] [PubMed]
    [Google Scholar]
  22. Kamvar ZN, Tabima JF, Grünwald NJ. Poppr: an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2014; 2:
    [Google Scholar]
  23. Kamvar ZN, Brooks JC, Grünwald NJ. Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Plant Genet Genomics 2015; 6:208
    [Google Scholar]
  24. Grünwald NJ, Everhart SE, Knaus BJ, Kamvar ZN. Best practices for population genetic analyses. Phytopathology 2017; 107:1000–1010 [View Article] [PubMed]
    [Google Scholar]
  25. Edwards AWF. Distances between populations on the basis of gene frequencies. Biometrics 1971; 27:873–881 [PubMed]
    [Google Scholar]
  26. Kolde R. pheatmap: Pretty heatmaps [Internet]; 2019 https://CRAN.R-project.org/package=pheatmap accessed 01 May 2019
  27. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw 2015; 67:1–48
    [Google Scholar]
  28. McCullagh P. Generalized Linear Models. CRC Press; 2018 https://books.google.com.au/books?id=UzmDDwAAQBAJ
  29. González JR, Mercader JM, Estivill X, Armengol L, Solé X et al. Snpassoc: An R package to perform whole genome association studies. Bioinforma Oxf Engl 2007; 23:644–645
    [Google Scholar]
  30. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 1995; 57:289–300
    [Google Scholar]
  31. Shah RM, Williams AH, Hane JK, Lawrence JA, Farfan-Caceres LM et al. Reference genome assembly for australian Ascochyta rabiei isolate arme14. G3 Genes Genomes Genet 2020; 10:2131–2140
    [Google Scholar]
  32. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J et al. blast+: architecture and applications. BMC Bioinformatics 2009; 10:421 [View Article] [PubMed]
    [Google Scholar]
  33. Jones P, Binns D, Chang HY, Fraser M, Li W et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 2014; 30:1236–1240 [View Article] [PubMed]
    [Google Scholar]
  34. Sperschneider J, Dodds PN, Gardiner DM, Singh KB, Taylor JM. Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0. Mol Plant Pathol 2018; 19:2094–2110 [View Article]
    [Google Scholar]
  35. Sperschneider J, Gardiner DM, Dodds PN, Tini F, Covarelli L et al. EffectorP: predicting fungal effector proteins from secretomes using machine learning. New Phytol 2016; 210:743–761 [View Article] [PubMed]
    [Google Scholar]
  36. Phan HTT, Ford R, Taylor PWJ. Mapping the mating type locus of Ascochyta rabiei, the causal agent of ascochyta blight of chickpea. Mol Plant Pathol 2003; 4:373–381 [View Article] [PubMed]
    [Google Scholar]
  37. Leo AE, Ford R, Linde CC. Genetic homogeneity of a recently introduced pathogen of chickpea, Ascochyta rabiei, to Australia. Biol Invasions 2015; 17:609–623 [View Article]
    [Google Scholar]
  38. Nourollahi K, Javannikkhah M, Naghavi MR, Lichtenzveig J, Okhovat SM et al. Genetic diversity and population structure of Ascochyta rabiei from the western Iranian Ilam and Kermanshah provinces using MAT and SSR markers. Mycol Prog 2011; 10:1–7 [View Article]
    [Google Scholar]
  39. Vieira MLC, Santini L, Diniz AL, Munhoz C de F. Microsatellite markers: what they mean and why they are so useful. Genet Mol Biol 2016; 39:312–328 [View Article] [PubMed]
    [Google Scholar]
  40. Tsykun T, Rellstab C, Dutech C, Sipos G, Prospero S. Comparative assessment of SSR and SNP markers for inferring the population genetic structure of the common fungus Armillaria cepistipes. Heredity (Edinb) 2017; 119:371–380 [View Article] [PubMed]
    [Google Scholar]
  41. Guichoux E, Lagache L, Wagner S, Chaumeil P, Léger P et al. Current trends in microsatellite genotyping. Mol Ecol Resour 2011; 11:591–611 [View Article] [PubMed]
    [Google Scholar]
  42. Varshney RK, Nayak SN, May GD, Jackson SA. Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotechnol 2009; 27:522–530 [View Article] [PubMed]
    [Google Scholar]
  43. Coventry SA. Factors affecting short and long distance dispersal of fungal pathogens: Chickpea ascochyta blight as a model; 2012 https://digital.library.adelaide.edu.au/dspace/handle/2440/75702 accessed 12 Nov 2020
  44. Khaliq I, Fanning J, Melloy P, Galloway J, Moore K et al. The role of conidia in the dispersal of Ascochyta rabiei. Eur J Plant Pathol 2020; 158:911–924 [View Article]
    [Google Scholar]
  45. McDonald BA, Linde C. Pathogen population genetics, evolutionary potential, and durable resistance. Annu Rev Phytopathol 2002; 40:349–379 [View Article] [PubMed]
    [Google Scholar]
  46. Kim W, Park JJ, Gang DR, Peever TL, Chen W. A novel type pathway-specific regulator and dynamic genome environments of a solanapyrone biosynthesis gene cluster in the fungus Ascochyta rabiei. . Eukaryot Cell 2015; 14:1102–1113 [View Article] [PubMed]
    [Google Scholar]
  47. Cruz VMV, Kilian A, Dierig DA. Development of DArT marker platforms and genetic diversity assessment of the U.S. Collection of the new oilseed crop Lesquerella and related species. PLOS ONE 2013; 8:
    [Google Scholar]
  48. Garavito A, Montagnon C, Guyot R, Bertrand B. Identification by the DArTseq method of the genetic origin of the Coffea canephora cultivated in Vietnam and Mexico. BMC Plant Biol 2016; 16:
    [Google Scholar]
  49. Lambert MR, Skelly DK, Ezaz T. Sex-linked markers in the North American green frog (Rana clamitans) developed using DArTseq provide early insight into sex chromosome evolution. BMC Genomics 2016; 17:844 [View Article] [PubMed]
    [Google Scholar]
  50. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One 2011; 6:e19379 [View Article] [PubMed]
    [Google Scholar]
  51. He J, Zhao X, Laroche A, Lu Z-X, Liu H et al. Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 2014; 5:484 [View Article]
    [Google Scholar]
  52. Li H, Vikram P, Singh RP, Kilian A, Carling J et al. A high density GBS map of bread wheat and its application for dissecting complex disease resistance traits. BMC Genomics 2015; 16:216 [View Article] [PubMed]
    [Google Scholar]
  53. Torkamaneh D, Laroche J, Belzile F. Genome-wide SNP calling from genotyping by sequencing (GBS) data: A comparison of seven pipelines and two sequencing technologies. PLoS One 2016; 11:e0161333 [View Article] [PubMed]
    [Google Scholar]
  54. Tabima JF, Coffey MD, Zazada IA, Grünwald NJ. Populations of phytophthora Rubi show little differentiation and high rates of migration among states in the western United States. Mol Plant Microbe Interact 2018; 31:614–622 [View Article] [PubMed]
    [Google Scholar]
  55. Talas F, Kalih R, Miedaner T, McDonald BA. Genome-wide association study identifies novel candidate genes for aggressiveness, deoxynivalenol production, and azole sensitivity in natural field populations of Fusarium graminearum. Mol Plant Microbe Interact 2016; 29:417–430 [View Article] [PubMed]
    [Google Scholar]
  56. Knaus BJ, Tabima JF, Shakya SK, Judelson HS, Grünwald NJ. Genome-wide increased copy number is associated with emergence of dominant clones of the Irish potato famine pathogen phytophthora infestans. mBio 2020; 11: [View Article]
    [Google Scholar]
  57. Plissonneau C, Benevenuto J, Mohd-Assaad N, Fouché S, Hartmann FE et al. Using population and comparative Genomics to understand the genetic basis of effector-driven fungal pathogen evolution. Front Plant Sci 2017; 8:119 [View Article]
    [Google Scholar]
  58. Singh K, Nizam S, Sinha M, Verma PK. Comparative transcriptome analysis of the necrotrophic fungus Ascochyta rabiei during oxidative stress: Insight for fungal survival in the host plant. PLoS One 2012; 7:e33128 [View Article]
    [Google Scholar]
  59. Badet T, Croll D. The rise and fall of genes: origins and functions of plant pathogen pangenomes. Curr Opin Plant Biol 2020; 56:65–73 [View Article] [PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000627
Loading
/content/journal/mgen/10.1099/mgen.0.000627
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Most cited this month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error