To improve usability of methods for quantifying environmentally persistent entomophthoralean resting spores in soil, we modified and tested two methods using resting spores (azygospores) of the gypsy moth pathogen Entomophaga maimaiga. Both methods were effective for recovering resting spores at concentrations \u3e100 resting spores/g dry soil. While a modification of a method originally described by Weseloh and Andreadis (2002) recovered more resting spores than a modified method based on Percoll density gradients, the ability to estimate true densities from counts was similar for both methods. Regression equations are provided for predicting true resting spore densities from counts, with R 2 values for both methods P0.90

Hajek, Ann E.

Plymale, Ruth C.

Reilly, James R.

English

Ouachita Baptist University

Ouachita Baptist University Scholarly Commons @ Ouachita Articles Faculty Publications 8-7-2012 Comparing Two Methods for Quantifying Soil-borne Entomophaga maimaiga Resting Spores Ann E. Hajek Cornell University Ruth C. Plymale Ouachita Baptist University, plymaler@obu.edu James R. Reilly Cornell University Follow this and additional works at: https://scholarlycommons.obu.edu/articles  Part of the Forest Sciences Commons, and the Other Life Sciences Commons Recommended Citation Hajek, Ann E., Plymale, Ruth C., and Reilly, James R. "Comparing Two Methods for Quantifying Soil-borne Entomophaga maimaiga Resting Spores," Journal of Invertebrate Pathology, Elsevier, 111:2 (2012) Aug 7, 193-195. doi: 10.1016/j.jip.2012.07.021 This Article is brought to you for free and open access by the Faculty Publications at Scholarly Commons @ Ouachita. It has been accepted for inclusion in Articles by an authorized administrator of Scholarly Commons @ Ouachita. For more information, please contact mortensona@obu.edu. Short CommunicationComparing two methods for quantifying soil-borne Entomophaga maimaigaresting sporesAnn E. Hajek ⇑, Ruth C. Plymale 1, James R. ReillyDepartment of Entomology, Cornell University, Ithaca, NY 14853-2601, USAa r t i c l e i n f oArticle history:Received 8 March 2012Accepted 20 July 2012Available online 7 August 2012Keywords:Resting sporesAzygosporesEntomophthoralesSamplingEntomophaga maimaigaGypsy motha b s t r a c tTo improve usability of methods for quantifying environmentally persistent entomophthoralean restingspores in soil, we modified and tested two methods using resting spores (azygospores) of the gypsy mothpathogen Entomophaga maimaiga. Both methods were effective for recovering resting spores at concen-trations >100 resting spores/g dry soil. While a modification of a method originally described by Weselohand Andreadis (2002) recovered more resting spores than a modified method based on Percoll densitygradients, the ability to estimate true densities from counts was similar for both methods. Regressionequations are provided for predicting true resting spore densities from counts, with R2 values for bothmethods P0.90. 2012 Elsevier Inc. All rights reserved.1. IntroductionQuantification of persistent pathogen stages can be importantwhen investigating the epizootiology of insect diseases. Entom-ophthoralean zygospores or azygospores, commonly called restingspores, created during epizootics persist as inoculum reservoirs insoil, where only some germinate each year to cause primary infec-tions (Hajek, 1997). For the entomophthoralean gypsy moth path-ogen Entomophaga maimaiga, cadavers from infected later larvalinstars produce resting spores (Hajek, 1999). Resting spores of E.maimaiga are washed into the soil as cadavers decompose, mostlyremaining in the organic layer at bases of trees.Two methods for quantifying resting spores in soil have beendeveloped but never compared. Hajek and Wheeler (1994) de-scribed extraction and quantification of E. maimaiga resting sporesfrom soil by wet-sieving 5 g soil samples, followed by centrifuga-tion in a 3-layered Percoll density gradient (PM = Percoll method).The middle density (5 mL) was transferred to a 100 mm petri dishand entirely examined at 50. This approach to sample quantifica-tion was slow and counting one sample could take hours. Weselohand Andreadis (2002) created a ‘soil physical count’ method forquantifying E. maimaiga resting spores in 100 mL soil samples; soilwas wet-sieved, 800 mL water were added, after settling the super-natant was removed so the total volume was 200 ml, and sampleswere then taken for quantification (WM =Weseloh method). Wemodified the Percoll method (mPM) to decrease time for quantifi-cation. We modified the Weseloh method (mWM) so that the samesize soil samples as the mPMwere tested. In this paper, we report acomparison of these two modified methods for quantifying E.maimaiga resting spores in soil.2. Materials and methodsGypsy moth larvae were obtained as neonates from USDAAPHIS CPHST, Buzzards Bay, Massachusetts, and reared followingstandard protocols (Bernon, 1995). Fourth instar larvae were in-jected with 10 lL of 1  105 protoplasts/mL E. maimaiga isolate AR-SEF 6162 at the base of a proleg. Injected larvae were maintained at20 C for 3 weeks, allowing time for resting spores to mature with-in cadavers. Cadavers were soaked in water for 16 h and the result-ing slurry was washed through a 63 lm sieve. The resulting restingspore suspension was quantified using a hemocytometer.Soil was collected from three sites separated by 3.8 km at eachof two forests in Centre Co., Pennsylvania, both with high percent-age of oaks (Quercus spp.). The top 3 cm of soil was collected as farfrom tree bases as possible to minimize the potential presence of E.maimaiga resting spores. Soil collected from one forest contained65.7% sand, 28.4% silt, 5.9% clay and 15.6% organic matter; soilfrom the second forest contained 38.5% sand, 45.5% silt, 16.0% clayand 47.3% organic matter. The resting spore suspension was usedto seed soil samples at 101, 102, 103, 104 and 105 resting spores/gdry soil.0022-2011/$ - see front matter  2012 Elsevier Inc. All rights reserved.http://dx.doi.org/10.1016/j.jip.2012.07.021⇑ Corresponding author. Fax: +1 607 255 0939.E-mail address: aeh4@cornell.edu (A.E. Hajek).1 Present address: Department of Biology, Ouachita Baptist University, Arkadelphia,AR 71998, USA.Journal of Invertebrate Pathology 111 (2012) 193–195Contents lists available at SciVerse ScienceDirectJournal of Invertebrate Pathologyjournal homepage: www.elsevier .com/ locate/ j ipBoth modified methods began with 5 g of soil that was wet-sieved through a series of increasingly fine 7.6 cm diameter sieves(500, 250, 125, 63, and 20 lm) and material on the 20 lm sievewas processed further (see Supplementary Methods for details).For the modified Percoll method, in 30 mL Corex tubes, a 2.5 mLlayer of 1.13 g/mL Percoll was overlain with a 2.5 mL layer of1.05 g/mL Percoll. Then, a 5 mL subsample of the suspension fromthe 20 lm sieve was layered on top and the tube was centrifugedusing a fixed angle rotor. Two milliliters of the interface betweenthe two Percoll layers were removed and added with 2 mL 0.01%Triton-X-100 to a 6 cm diameter petri dish. Resting spores withina 10.5 mm2 eyepiece grid were counted at 60 for 20 randomlychosen locations, for 3–6 soil samples per concentration.For the modified Weseloh method the material on the 20 lmsieve was washed into a 500 mL beaker and water was added to250 mL. After 30 min for settling, 200 mL were decanted. Theremaining 50 mL were transferred to a 100 mL beaker, allowed tosettle for 3 min and a 5 mL aliquot was removed from the middleof the liquid. The aliquot was vortexed and resting spores in six20 lL samples were counted under 22 mm2 cover slips.The two modified methods for quantifying resting spores in soilwere compared using a mixedmodel (SAS, 2002–2008), with quan-tification method, site and concentration of resting spores added/gdry soil as main effects, replicate sites within forests as random ef-fects and proportion resting spores recovered as the dependentvariable. Least squares means tests were used to further investi-gate the concentration of resting spores added/g dry soil. To predictactual densities from counts, a nonlinear regression of the formy = mx + x/(a + x) was estimated using the nls() function in R (RDevelopment Core Team 2012). In this regression, y is the pre-dicted number of spores (log10 + 1 transformed) and x is the countof resting spores/g soil (log10 + 1 transformed). This equation forcesthe regression line through the origin, which is sensible since nei-ther method will give positive spore counts when no spores arepresent. The parameters m and a control the asymptotic slopeand how rapidly this slope is approached, respectively. Correlationcoefficients for the two methods were compared using a z test(Snedecor and Cochran, 1980).3. Results and discussionThe proportion seeded resting spores that were recovered didnot differ for soils from the two forests (F1,42 = 0.02, P = 0.9004),so these data were merged. The overall proportion of resting sporesrecovered from seeded soil using the modified methods was signif-icantly lower for the lowest resting spore concentration (101) thanthe other concentrations (P < 0.05). The proportion resting sporesrecovered differed significantly, with higher recovery for themWM (21.6 ± 4.6%; mean ± SE) and lower recovery for the mPM(11.5 ± 1.6%) (F5,48 = 4.28, P = 0.0027).Regression analysis provided conversions between count dataand the true number of resting spores/g dry soil for the two mod-ified methods (Fig. 1). Both methods suffer from decreased detec-tion at low densities but quickly become linear, indicating fairlyconsistent quantification rates above approximately 100 spores/g.The mWM shows less curvature in the estimated regression linedue to its slightly higher recovery rate. The correlation coefficientfor the regression of estimated density to true density for themPM was significantly greater than the correlation for the mWM(z = 2.11, P = 0.035). Using these equations, spore density predic-tions for counts of zero are assumed to be zero and do not relateto the R2 values of the regressions.ThemPMproduced slightlymore accurate predictions, but recov-ered fewer resting spores from soil that the mWM, implying that themWMcould bemoreuseful for estimating sampleswith lower densi-tiesof resting spores. Furthermore, themWMrequiredapproximately55 minper samplewhile themPMrequired75min/sample (althoughtime-efficiencywould increasewithmoresamplesbeing runsimulta-neously).Overall, bothmethodsenablegoodpredictionof true restingspore densities from counts, with R2 valuesP0.90 for both methods,suggesting accurate conversions from counts to real densities,especially at densities >100 resting spores/g soil. Thesemethods pro-vide a viable alternative to the real-time PCR resting spore quantifica-tionmethod developed by Castrillo et al. (2007), especially given thathigh levels of organic material in surface soil samples where restingspores are located make this procedure difficult and costly, giventhe DNA extraction methods currently available.AcknowledgmentsWe thank Jessica Greenberg and Charissa Fritzen for technicalassistance and Francoise Vermeylen for statistical advice. Thisstudy was supported by the USDA, Forest Service, Northern Re-search Station.Appendix A. Supplementary materialSupplementary methods associated with this article can befound, in the online version, at http://dx.doi.org/10.1016/j.jip.2012.07.021.Fig. 1. Predictive regressions for calculating the true densities of Entomophaga maimaiga resting spores in soil samples from spore counts made from soil sub-samples usingeither the (A). modified Weseloh (mWM) or (B). modified Percoll method (mPM). In these regression equations both number of resting spores counted/g soil and estimatedtrue density of resting spores/g soil were log10 + 1 transformed.194 A.E. Hajek et al. / Journal of Invertebrate Pathology 111 (2012) 193–195ReferencesBernon, G.L., 1995. Status of mass-reared gypsy moths: protocol. USDA, Forest Serv.,NE Forest Exp. Stn., Gen. Tech. Rep. NE-213, 54.Castrillo, L.A., Thomsen, L., Juneja, P., Hajek, A.E., 2007. Detection and quantificationof Entomophaga maimaiga resting spores in forest soil using real-time PCR.Mycol. Res. 111, 324–331.Hajek, A.E., 1997. Ecology of terrestrial fungal entomopathogens. Adv. Microb. Ecol.15, 193–249.Hajek, A.E., 1999. Pathology and epizootiology of the Lepidoptera-specificmycopathogen Entomophaga maimaiga. Microb. Mol. Biol. Rev. 63, 814–835.Hajek, A.E., Wheeler, M.M., 1994. Application of techniques for quantification ofsoil-borne entomophthoralean resting spores. J. Invertebr. Pathol. 64, 71–73.R Development Core Team, 2012. R: a language and environment for statisticalcomputing. R Foundation for Statistical Computing, Vienna, Austria. ISBN: 3-900051-07-0, URL: <http://www.R-project.org/>.SAS (Statistical Analysis System), 2002–2008. Version 9.2. Cary, North Carolina.Snedecor, G.W., Cochran, W.G., 1980. Statistical Methods, seventh ed. Iowa StateUniv. Press, Ames, Iowa.Weseloh, R.M., Andreadis, T.G., 2002. Detecting the titer in forest soils of spores ofthe gypsy moth (Lepidoptera: Lymantriidae) fungal pathogen, Entomophagamaimaiga (Zygomycetes: Entomophthorales). Can. Entomol. 134, 269–279.A.E. Hajek et al. / Journal of Invertebrate Pathology 111 (2012) 193–195 195

Comparing Two Methods for Quantifying Soil-borne Entomophaga maimaiga Resting Spores

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Comparing Two Methods for Quantifying Soil-borne Entomophaga maimaiga Resting Spores

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