Methods for Fungicide Efficacy Screenings: Multiwell Testing Procedures for the Oomycetes Phytophthora infestans and Pythium ultimum

Abstract

Oomycetes-borne diseases represent a serious problem for agriculture sustainability due to the high use of chemical products employed for their control. In recent years, increasing concerns on side effects associated with fungicide utilization have led to the reduction of the permissible modes of action, with the remaining ones continuously threatened by the increase of resistant strains in the pathogen populations. In this context, it is mandatory to develop new generation fungicides characterized by high specificity towards the target species and low environmental impact to guarantee the sustainability, productivity, and quality of food production. Fungicide discovery is a lengthy and costly process, and despite these urgent needs, poor description and formalization of high-throughput methodologies for screening the efficacy of active compounds are commonly reported for these kinds of organisms. In this study, a comprehensive picture of two high-throughput practices for efficient fungicide screening against plant-pathogenic oomycetes has been provided. Different protocols using multiwell plates were validated on approved crop protection products using Phytophthora infestans and Pythium ultimum as the model species. In addition, detailed statistical inputs useful for the analysis of data related to the efficacy of screenings are included.

Keywords: fungicide efficacy; oomycetes; plant pathogens; statistical analyses.

Figure 1

Graphical scheme of the experimental activities carried out on P. infestans (A) and P. ultimum (B).

Figure 2

(A) Mean OD₆₂₀ absorbance values measured in P. infestans sporangia inoculated wells from 0 to 3 dpi (24 h interval) with standard deviation (vertical bars). Lines were obtained through general additive model (GAM) regression fitting using the gam() function in R. (B) Curves obtained from the three-parameters logistic model predictions by analyzing GIP (growth inhibition percentage) indexes calculated for each assay (24 wells, 96 wells, and 9 cm Petri) at given mandipropamid concentrations. Points display GIP values recorded; estimated parameters for each curve are also reported.

Figure 3

(A) OD₆₂₀ values recorded from 0 to 4 dpi for P. ultimum mycelial suspensions treated and untreated with metalaxyl M in 96-well microtiter plates. Lines were obtained through general additive model (GAM) regression fitting using the gam() function in R. Letters display significant differences individuated by the Kruskal–Wallis test with LSD post hoc analysis for treated (KW = 11.6, df = 4, p-value = 0.02) and untreated wells (KW = 18.29, df = 4, p-value = 0.001). (B) Regression lines obtained from the MLR model on log-transformed OD values. Light-colored ribbons displaying 95% confidence intervals are reported. Dots show mean values with standard deviations (vertical lines).