Advertisement

Tropical Plant Pathology

, Volume 44, Issue 1, pp 53–64 | Cite as

Genetic variation and structure of Sclerotinia sclerotiorum populations from soybean in Brazil

  • Anthony Pannullo
  • Zhian N. Kamvar
  • Thomas J. J. Miorini
  • James R. Steadman
  • Sydney E. EverhartEmail author
Original Article
  • 241 Downloads

Abstract

The clonal, necrotrophic plant pathogen, Sclerotinia sclerotiorum is the causal agent of white mold on soybean, which causes significant losses for Brazilian farmers each year. While assessments of population structure and clonal dynamics can be beneficial for determining effective management strategies, few studies have been performed. In this paper, we present a population genetic analysis with 11 microsatellite loci of 94 isolates of S. sclerotiorum from soybean fields in six Brazilian states (Goiás, N = 18; Rio Grande do Sul, N = 16; Paraná, N = 15; Bahia, N = 13; Minas Gerais, N = 7; Mato Grosso do Sul, N = 5) with Argentina (N = 5) and the U.S. (N = 15) as outgroups. Genotyping identified 87 multilocus genotypes with 81 represented by a single isolate. The pattern of genetic diversity observed suggested populations were not strongly differentiated because despite the high genetic diversity, there were few private alleles/genotypes. In addition, no multilocus genotypes were identified in both South and North America while one multilocus genotype was shared between Argentina and Brazil. Pairwise analysis of molecular variance between populations in Brazil revealed nine out of 15 pairs significantly different (P > 0.05). The population from the U.S. was most strongly differentiated in across all measures of population differentiation. Overall, our results found evidence for gene flow across populations with a moderate amount of population structure within states in Brazil. We additionally found shared genotypes across populations in Brazil and Argentina that suggests sclerotia may be transferred across states either through seeds or shared equipment. This represents the first population genetic study of S. sclerotiorum to cover a wide geographic area in Brazil.

Keywords

White mold Sclerotinia stem rot Soybean Glycine max 

Notes

Acknowledgments

We thank Rebecca Higgins and Rachana Jhala for assistance in the mycelial compatibility assays and curation of the culture collection. This work was funded in part by grant #58-5442-2-209 from the USDA-ARS National Sclerotinia Initiative to JRS/SEE and start-up funds from the University of Nebraska-Lincoln (UNL) to SEE. Additional financial support was provided by the Agricultural Research Division of the Institute of Agriculture and Natural Resources at UNL to AP. Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This project is based on research that was partially supported by the Nebraska Agricultural Experiment Station with funding from the Hatch Act (Accession Number 1007272) through the USDA National Institute of Food and Agriculture.

Supplementary material

40858_2018_266_MOESM1_ESM.pdf (6 kb)
ESM 1 (PDF 6 kb)
40858_2018_266_MOESM2_ESM.docx (60 kb)
ESM 2 (DOCX 59 kb)

References

  1. Agapow PM, Burt A (2001) Indices of multilocus linkage disequilibrium. Molecular Ecology Notes 1:101–102CrossRefGoogle Scholar
  2. Aldrich-Wolfe L, Travers S, Nelson BD (2015) Genetic variation of Sclerotinia sclerotiorum from multiple crops in the north Central United States. PLoS One 10:e0139188CrossRefGoogle Scholar
  3. Attanayake RN, Carter PA, Jiang D, del Río-Mendoza L, Chen W (2013) Sclerotinia sclerotiorum populations infecting canola from China and the United States are genetically and phenotypically distinct. Phytopathology 103:750–761CrossRefGoogle Scholar
  4. Boland GJ, Hall R (1994) Index of plant hosts of Sclerotinia sclerotiorum. Canadian Journal of Plant Pathology 16:93–108Google Scholar
  5. Botelho LS, Zancan WLA, Machado JC, Barrocas EN (2013) Performance of common bean seeds infected by the fungus Sclerotinia sclerotiorum. Journal of Seed Science 35:153–160Google Scholar
  6. Brown AHD, Weir BS (1983) Measuring genetic variability in plant populations. Developments in Plant Genetics and Breeding 1:219–239CrossRefGoogle Scholar
  7. Bruvo R, Michelis N, D’Sousa T, Schulenberg H (2004) A simple method for calculation of microsatellite genotypes irrespective of ploidy level. Molecular Ecology 13:2101–2106CrossRefGoogle Scholar
  8. Carbone I, Anderson JB, Kohn LM (1999) Patterns of descent in clonal lineages and their multilocus fingerprints are resolved with combined gene genealogies. Evolution (N Y) 53:11–21Google Scholar
  9. Carpenter MA, Frampton C, Stewart A (1999) Genetic variation in New Zealand populations of the plant pathogen Sclerotinia sclerotiorum. New Zealand Journal of Crop and Horticultural Science 27:13–21CrossRefGoogle Scholar
  10. Csardi, GNepusz T (2006) The igraph software package for complex network research. InterJournal Complex Sy:1695.Google Scholar
  11. Dunn AR, Kikkert JR, Pethybridge SJ (2017) Genotypic characteristics in populations of Sclerotinia sclerotiorum from New York state, USA. The Annals of Applied Biology 170:219–228CrossRefGoogle Scholar
  12. Ekins MG, Hayden HL, Aitken EAB, Goulter KC (2011) Population structure of Sclerotinia sclerotiorum on sunflower in Australia. Australasian Plant Pathology 40:99–108CrossRefGoogle Scholar
  13. Excoffier L, Smouse P, Quattro J (1992) Analysis of molecular variance infered from metric distances among DNA haplotypes: application to human mitochondrial DNA restricyion data. Genetics 131:479–491Google Scholar
  14. Gomes EV, Do Nascimento LB, De Freitas MA, Nasser LC, Petrofeza S (2011) Microsatellite markers reveal genetic variation within Sclerotinia sclerotiorum populations in irrigated dry bean crops in Brazil. Journal of Phytopathology 159:94–99.Google Scholar
  15. Grogan RG (1979) Sclerotinia species: summary and comments on needed research. Phytopathology 69:908–910CrossRefGoogle Scholar
  16. Hambleton S, Walker C, Kohn LM (2002) Clonal lineages of Sclerotinia sclerotiorum previously known from other crops predominate in 1999-2000 samples from Ontario and Quebec soybean. Canadian Journal of Plant Pathology 24:309–315CrossRefGoogle Scholar
  17. Haubold B, Travisano M, Rainey PB, Hudson RR (1998) Detecting linkage disequilibrium in bacterial populations [in process citation]. Genetics 150:1341–1348Google Scholar
  18. Hemmati R, Javan-Nikkhah M, Linde CC (2009) Population genetic structure of Sclerotinia sclerotiorum on canola in Iran. European Journal of Plant Pathology 125:617–628CrossRefGoogle Scholar
  19. Jombart T (2008) Adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405CrossRefGoogle Scholar
  20. Jombart T, Ahmed I (2011) Adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27:3070–3071CrossRefGoogle Scholar
  21. Jombart T, Devillard S, Balloux F (2010) Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genetics 11:94CrossRefGoogle Scholar
  22. Kamvar ZN, Tabima JF, Grünwald NJ (2014) Poppr : an R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2:e281CrossRefGoogle Scholar
  23. Kamvar ZN, Brooks JC, Grünwald NJ (2015) Novel R tools for analysis of genome-wide population genetic data with emphasis on clonality. Frontiers in Genetics 6:208CrossRefGoogle Scholar
  24. Kamvar ZN, Amaradasa BS, Jhala R, McCoy S, Steadman JR, Everhart SE (2017) Population structure and phenotypic variation of Sclerotinia sclerotiorum from dry bean ( Phaseolus vulgaris ) in the United States. PeerJ 5:e4152CrossRefGoogle Scholar
  25. Kohli Y, Kohn LM (1998) Random association among alleles in clonal populations of Sclerotinia sclerotiorum. Fungal Genetics and Biology 23:139–149CrossRefGoogle Scholar
  26. Kohn LM (1995) The clonal dynamic in wild and agricultural plant-pathogen populations. Canadian Journal of Botany 73:1231–1240CrossRefGoogle Scholar
  27. Legovic T (1991) Statistical ecology. A primer on methods and computing. John Wiley & SonsGoogle Scholar
  28. Lehner MS, Mizubuti ESG (2017) Are Sclerotinia sclerotiorum populations from the tropics more variable than those from subtropical and temperate zones? Tropical Plant Pathology 42:61–69CrossRefGoogle Scholar
  29. Lehner MS, Paula Júnior TJ, Hora Júnior BT, Teixeira H, Vieira RF, Carneiro JES, Mizubuti ESG (2015) Low genetic variability in Sclerotinia sclerotiorum populations from common bean fields in Minas Gerais state, Brazil, at regional, local and micro-scales. Plant Pathology 64:921–931CrossRefGoogle Scholar
  30. Lehner MS, De Paula Júnior TJ, Del Ponte EM, Mizubuti ES, Pethybridge SJ (2017a) Independently founded populations of Sclerotinia sclerotiorum from a tropical and a temperate region have similar genetic structure. PLoS One 12:e0173915Google Scholar
  31. Lehner MS, Pethybridge SJ, Meyer MC, Del Ponte EM (2017b) Meta-analytic modelling of the incidence–yield and incidence–sclerotial production relationships in soybean white mould epidemics. Plant Pathology 66:460–468CrossRefGoogle Scholar
  32. Li Z, Wang Y, Chen Y, Zhang J, Fernando WGD (2009) Genetic diversity and differentiation of Sclerotinia sclerotiorum populations in sunflower. Phytoparasitica 37:77–85CrossRefGoogle Scholar
  33. Litholdo Júnior CG, Gomes EV, Lobo Júnior M, Nasser LCB, Petrofeza S (2011) Genetic diversity and mycelial compatibility groups of the plant-pathogenic fungus Sclerotinia sclerotiorum in Brazil. Genetics and Molecular Research 10:868–877Google Scholar
  34. Malvárez G, Carbone I, Grünwald NJ, Subbarao KV, Schafer M, Kohn LM (2007) New populations of Sclerotinia sclerotiorum from lettuce in California and peas and lentils in Washington. Phytopathology 97:470–483CrossRefGoogle Scholar
  35. McDonald BA (1997) The population genetics of Fungi: tools and techniques. Phytopathology 87:448–453CrossRefGoogle Scholar
  36. McDonald BA, Linde C (2002) The population genetics of plant pathogens and breeding strategies for durable resistance. Euphytica 124:163–180CrossRefGoogle Scholar
  37. Milgroom MG (1996) Recombination and the multilocus structure of fungal populations. Annual Review of Phytopathology 34:457–477CrossRefGoogle Scholar
  38. Nei M (1978) Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 89:583–590Google Scholar
  39. Pannullo A, Kamvar ZN, Miorini TJJ, Steadman JR, Everhart SE (2018) Data and Analysis for Genetic variation and structure of Sclerotinia sclerotiorum populations from soybean in BrazilGoogle Scholar
  40. Paradis E (2010) Pegas: an R package for population genetics with an integrated – modular approach. Bioinformatics 26:419–420Google Scholar
  41. Paradis E, Claude J, Strimmer K (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289–290CrossRefGoogle Scholar
  42. Pielou EC (1975) Ecological diversity. Wiley, New YorkGoogle Scholar
  43. Prugnolle F, De Meeus T (2010) Apparent high recombination rates in clonal parasitic organisms due to inappropriate sampling design. Heredity (Edinb) 104:135–140CrossRefGoogle Scholar
  44. R Core Team (2017). R: A language and environment for statistical computing. R Found Stat Comput Vienna, Austria URL http://www.R-project.org/ R Foundation for Statistical Computing
  45. RStudio Team (2015) R-Studio: integrated development for R. R-Studio, Inc., Boston, MA, USA.Google Scholar
  46. Sambrook J, Fritsch EF, Maniatis T (1989) Molecular cloning: a laboratory manual. Cold Spring Harbor laboratory pressGoogle Scholar
  47. Shannon CE (1948) A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 5:3–55CrossRefGoogle Scholar
  48. Sirjusingh C, Kohn LM (2001) Characterization of microsatellites in the fungal plant pathogen, Sclerotinia sclerotiorum. Molecular Ecology Notes 1:267–269Google Scholar
  49. Smith JM, Smith NH, O’Rourke M, Spratt BG (1993) How clonal are bacteria? Proceedings of the National Academy of Sciences 90:4384–4388CrossRefGoogle Scholar
  50. Stoddart JA, Taylor JF (1988) Genotypic diversity: estimation and prediction in samples. Genetics 118:705–711Google Scholar
  51. U.S. Canola Association. (n.d.). National Sclerotinia Initiative. Retrieved December 10, 2014, from http://www.uscanola.com/site/epage/102383_956.htm
  52. Wickham H (2009) ggplot2 elegant graphics for data analysis. Springer-Verlag, New YorkGoogle Scholar
  53. Wrather A, Shannon G, Balardin R Carregal L, Escobar R, Gupta GK, Ma Z, Morel W, Ploper D, Tenuta A (2010) Effect of diseases on soybean yield in the top eight producing countries in 2006. Plant Health Progress 10:2008–2013Google Scholar

Copyright information

© Sociedade Brasileira de Fitopatologia 2018

Authors and Affiliations

  1. 1.Department of Plant PathologyUniversity of NebraskaLincolnUSA
  2. 2.Department of Microbiology and ImmunologyUniversity of IowaIowa CityUSA
  3. 3.MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public HealthImperial CollegeLondonUK
  4. 4.Carrington Research Extension CenterNorth Dakota State UniversityCarringtonUSA

Personalised recommendations