Artigo Acesso aberto Revisado por pares

Minimization of between-well sample variance of antifungal activity using a high-throughput screening microplate bioassay

2007; Future Science Ltd; Volume: 42; Issue: 2 Linguagem: Inglês

10.2144/000112328

ISSN

1940-9818

Autores

Scott W. Pryor, Donna M. Gibson, Gary C. Bergstrom, Larry P. Walker,

Tópico(s)

Mycotoxins in Agriculture and Food

Resumo

BioTechniquesVol. 42, No. 2 BenchmarksOpen AccessMinimization of between-well sample variance of antifungal activity using a high-throughput screening microplate bioassayScott W. Pryor, Donna M. Gibson, Gary C. Bergstrom & Larry P. WalkerScott W. PryorCornell University, Ithaca, NY, USANorth Dakota State University, Fargo, ND, USA, Donna M. GibsonUSDA ARS Plant Protection Research Unit, Ithaca, NY, USA, Gary C. BergstromCornell University, Ithaca, NY, USA & Larry P. Walker*Address correspondence to Larry P. Walker, Department of Biological and Environmental Engineering, 232 Riley- Robb Hall, Cornell University, Ithaca, NY 14853, USA. e-mail: E-mail Address: lpw1@cornell.eduCornell University, Ithaca, NY, USAPublished Online:16 May 2018https://doi.org/10.2144/000112328AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail The use of microplate bioassays, or broth microdilution assays, to measure the activity of biological and synthetic compounds against fungal pathogens has increased in recent years; this technique has been identified as the most promising in vitro bioassay for quantifying antifungal activity (1). Quantification of fungal growth by spectrophotometric methods can be imprecise, however, because mycelial growth by nature is filamentous and therefore heterogeneous in liquid media. Studies using these methods have shown that there is variability in mycelial growth within a single well but such variability has not been quantified (2,3). Sample variability among replicate wells has been reported, but variability of absorbance readings within a single well has not been taken into account (4–6). Despite the growing popularity of this type of high-throughput assay, and possible limitations because of high within-well variance, sample variability and the introduction of sampling error have received little attention.This study presents an improvement of a high-throughput screening method presented earlier (7) and based upon a previously published method (8). The assay was used to quantify the antifungal activity of a Bacillus subtilis biocontrol product (BCP) against plant pathogenic fungi of the genus Fusarium. The new method allows for more precise quantification of antifungal activity by accounting for variation in optical density within a single well of a 96-well plate.B. subtilis strain TrigoCor 1448 (accession no. 202152; ATCC, Manassas, VA, USA) (9) was grown in solid state fermentation (SSF) bioreactors as described previously (7). Methanol extraction of the active compounds and subsequent solid phase extraction (SPE) for sample purification were also carried out as described (7). Briefly, fermentation samples were suspended in approximately 2 volumes (v/w) methanol. Methanol extracts were dried via rotoevaporation and partially purified using a C-18 SPE column (Burdick & Jackson™; Honeywell, Muskegon, MI, USA). For assays comparing extract inhibition of cultures of Fusarium oxysporum and Fusarium graminearum, SSF biocontrol products were dried to <15% moisture content (wet basis) before extracting with methanol. Crude methanol extracts were diluted 1:1 with water and applied directly to the SPE column; the active fractions were eluted with 100% methanol and dried via rotoevaporation and under nitrogen to obtain extract weights. Assays verified no loss of activity using this process (data not shown).The microplate bioassay to measure inhibition of F. oxysporum f. sp. melonis (isolate no. 81 of F. oxysporum race 2) (10), as described in Reference (7) was altered to reduce sample variability between triplicate wells. The method was also used to quantify control of F. graminearum (isolate Gz014NY98). Fungal cultures were cultivated for 1–2 weeks on quarter-strength potato dextrose agar plates. Plates were flooded with 10–15 mL sterile distilled water and gently scraped to form a spore suspension. Spore suspensions were diluted to 1×104 spores/mL for use in bioassays.Extract inhibition was tested by adding 100 µL potato dextrose broth, 10 µL BCP extract (7) or solvent control, and 90 µL fungal spore suspension to each well. All treatments were measured in triplicate. A Synergy™ HT plate reader (Bio-Tek Instruments, Winooski, VT, USA) was used to read plates at an absorbance of 620 nm before and after incubating for 48 h at 25°C. The heterogeneous nature of mycelial growth even in relatively small wells led to high sample variability between replicate wells. The KC4™ software (Bio-Tek Instruments) controlling the plate reader allowed the user to read absorbance from a single point at the center of each well or scan the well across a user-defined grid. The standard software only allowed the minimum 3 × 3 scanning grid for the small well size in a 96-well plate. This scanning protocol, however, led to high absorbance readings at the corners of the grid because of meniscus and sidewall effects occurring near the well edge.To reduce this problem, the micro-plate geometry profile was adjusted using the KC4 software so that the well diameter throughout the plate was reduced from 6860–4600 µm. Although the actual plate well size was not changed, the reader scanned the plate as if a plate with such geometry were being used. At such a small well diameter, the software will not allow for the full 3 × 3 grid scan. The corners of the 3 × 3 scanning grid are not included, but readings are still recorded at the center and all four sides of each well for a total of five readings per well. These absorbance readings are all far enough away from the side of the well to diminish interference occurring near the well edge. Final absorbance in each well was measured as the mean absorbance of the five readings within each well. Extract mean inhibition and standard deviation were calculated as described in Reference (7).A set of nine sample extracts diluted at concentrations of 0.05, 0.3, and 2 mg extract/mL was used to test sample variability with the modified plate reading protocol. The bioassay was set up as described previously (7) and read twice after initial loading and twice after the 48 h incubation period. The first reading was done with a standard single-point absorbance reading protocol. The plate was then read a second time using the well-scanning protocol described here.Inhibition values for the scanning method were less than those for the point reading method (P < 0.0001) with a mean difference between readings of 6.5%. Results of the standard deviation calculations of sample inhibition readings for the two plate-reading protocols are shown in Figures 1 and 2. Most of the reductions in standard deviation come from samples producing higher levels of inhibition (Figure 1). At very low levels of inhibition (<10%), the well-scanning protocol provides little or no improvement in inhibition standard deviations. The scanning protocol gives significantly lower standard deviations, however, when inhibition values are above approximately 15%. At the lower inhibition levels, standard deviations using the two methods were comparable but all were <10%. At higher inhibitory values, however, the point-reading protocol produced inhibition standard deviations as high as 18%. The scanning protocol reduced this variation to <9% for all samples and <5% for most readings.Figure 1. Microplate bioassay variability based on inhibition levels for point- and scan-reading protocols.Figure 2. Microplate bioassay improvement in variability with scan-reading protocol.Figure 2 shows a similar trend by plotting the reduction in sample standard deviation from using the point-reading protocol and the scanning protocol for each sample. When the point-reading inhibition standard deviation was below 6%, the scanning protocol provided little or no improvement. When the point-reading protocol yielded standard deviations higher than approximately 10%, however, well-scanning provided a significant improvement. These standard deviations dropped by 67% with the scanning protocol from a mean of 13.2% down to just 4.3%. Although the well-scanning protocol did not decrease sample variance in all cases and actually increased variance in a small fraction at very low levels of inhibition, the largest improvements were seen in those samples with the greatest variance using the standard point-reading protocol. The protocol is therefore most useful in situations where it is most needed. This differential improvement in sample variance for samples with higher levels of inhibition can be explained by the nature of the material in each well. At very low levels of inhibition, the fungal mycelia are relatively dense. There is a high degree of light scattering as it passes through the well, and individual points within the scanning grid will not differ significantly from one another. For extracts exhibiting moderate to high levels of inhibition, some mycelial growth is seen in the wells but not as much as in the control wells (0% inhibition). The density of mycelial growth for these samples is more heterogeneous across the well, and the scanning grid will detect more significant differences at different points within each well. Using a mean of all of these individual points within the grid allows a more accurate assessment of the growth for quanti-tation of inhibition.Several BCP extracts with a range of antifungal activities were tested for inhibition against another related plant pathogen, F. graminearum. A comparison of inhibition data against these two pathogens using five representative samples is shown in Figure 3. Inhibition trends were similar to those observed with F. oxysporum, but overall inhibition levels were much greater against F. graminearum than they were against F. oxysporum. Figure 4 shows a control dose-response curve for this assay using the synthetic fungicide benomyl with F. oxysporum. Data from both Figures 3 and 4 were obtained using the well-scanning protocol.Figure 3. Comparison of inhibition data in microplate bioassays using Fusarium oxysporum and Fusarium graminearum as test organisms.Error bars represent sample standard deviations. BCP, biocontrol product.Figure 4. Control assay measuring the inhibition of Fusarium oxys-porum with benomyl.Error bars represent sample standard deviations. ppm, parts per million.The modified well-scanning protocol presented was shown to decrease inhibition sample variance for the microplate bioassay. Absolute inhibition values were also significantly lower using the well-scanning method. This method should improve accuracy, reproducibility, and quantitative detection of differences between treatments for high-throughput screening processes inhibition assays or growth curves using the microplate format. Fungal bioassays are likely to benefit most from this improved format of data analysis because of the heterogeneous nature of mycelial growth. Bacterial bioassays could also show improvement, however, especially in cases where flocculent growth is present.AcknowledgmentsWe thank Dr. Thomas Zitter in the Department of Plant Pathology, Cornell University for providing the Fusarium oxysporum culture. This research was supported in part though a United States Department of Agriculture (USDA) National Needs Fellowship, a USDA Multidisciplinary Graduate Education Traineeship (MGET) program, and a grant from the U.S. Wheat and Barley Scab Initiative of the USDA under Agreement no. 59-0790-4-093. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the USDA.Competing Interests StatementThe authors declare no competing interests.References1. Hadacek, F. and H. Greger. 2000. Testing of antifungal natural products: methodologies, comparability of results and assay choice. Phytochem. Anal. 11:137–147.Crossref, CAS, Google Scholar2. Marchetti, O., P. Moreillon, M.P. Glauser, J. Bille, and D. Sanglard. 2000. Potent synergism of the combination of fluconazole and cyclosporine in Candida albicans. Antimicrob. Agents Chemother. 44:2373–2381.Crossref, Medline, CAS, Google Scholar3. Wilson, C.L., J.M. Solar, A. El Ghaouth, and M.E. Wisniewski. 1997. Rapid evaluation of plant extracts and essential oils for antifungal activity against Botrytis cinerea. Plant Dis. 81:204–210.Crossref, Medline, CAS, Google Scholar4. Oliva, A., K.M. Meepagala, D.E. Wedge, D. Harries, A.L. Hale, G. 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Plant Dis. 81:592–596.Crossref, Medline, CAS, Google ScholarFiguresReferencesRelatedDetailsCited ByEngineering a Non‐Natural Photoenzyme for Improved Photon Efficiency**2 December 2021 | Angewandte Chemie International Edition, Vol. 61, No. 2Engineering a Non‐Natural Photoenzyme for Improved Photon Efficiency**2 December 2021 | Angewandte Chemie, Vol. 131SnTox1, a Parastagonospora nodorum necrotrophic effector, is a dual‐function protein that facilitates infection while protecting from wheat‐produced chitinases4 April 2016 | New Phytologist, Vol. 211, No. 3Postinfection Activity of Synthetic Antimicrobial Peptides Against Stemphylium vesicarium in PearPhytopathology®, Vol. 104, No. 11Optimisation of an in vitro antifungal protein assay for the screening of potential antifungal proteins against Leptosphaeria maculansJournal of Microbiological Methods, Vol. 84, No. 1Comparative Study of Antimicrobial Peptides To Control Citrus Postharvest Decay Caused by Penicillium digitatum15 September 2007 | Journal of Agricultural and Food Chemistry, Vol. 55, No. 20Optimization of Spore and Antifungal Lipopeptide Production During the Solid-state Fermentation of Bacillus subtilis17 April 2007 | Applied Biochemistry and Biotechnology, Vol. 143, No. 1 Vol. 42, No. 2 Follow us on social media for the latest updates Metrics History Received 22 July 2006 Accepted 19 October 2006 Published online 16 May 2018 Published in print February 2007 Information© 2007 Author(s)AcknowledgmentsWe thank Dr. Thomas Zitter in the Department of Plant Pathology, Cornell University for providing the Fusarium oxysporum culture. This research was supported in part though a United States Department of Agriculture (USDA) National Needs Fellowship, a USDA Multidisciplinary Graduate Education Traineeship (MGET) program, and a grant from the U.S. Wheat and Barley Scab Initiative of the USDA under Agreement no. 59-0790-4-093. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the USDA.Competing Interests StatementThe authors declare no competing interests.PDF download

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