Discrimination of modes of action of antifungal substances by use of metabolic footprinting

Abstract

Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their "metabolic footprints" by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.

FIG. 1.

Growth curves (each an average of results from five replicate plate wells) from Bioscreen microtiter plate wild-type diploid cultures treated with 10 different (respiratory and nonrespiratory) fungicides at four different concentrations, plus DMSO control. (Concentrations are given in the graph key as parts per million.)

FIG. 2.

Inhibition profiles (when screened against wild-type diploid BY4743, on glucose-containing footprinting medium) induced by the (a) nonrespiratory and (b) respiratory fungicides selected for the metabolic footprinting mode of action comparison when applied at the concentrations given in Tables 1 (nonrespiratory inhibitors) and 2 (respiratory inhibitors). Growth inhibition was calculated as the difference in the OD of cultures (an average of six replicates) between 0 and 24 h expressed as a percentage of the change in the OD of DMSO controls over the same time span.

FIG. 3.

DFA was performed (and simultaneously cross-validated [1]) for PC scores (1 to 20, 99.9% of the variance) obtained from an analysis of footprints collected from wild-type diploid yeast cultures treated with 10 different inhibitors. DFA classes were assigned according to compound, regardless of the concentration at which it was applied. Labels are as follows: AE, AEC605025; AZ, azoxystrobin; CE, chlorimuron ethyl; CX, carboxin; E, epoxiconazole; F, fenpropimorph; FQ, fluquinconazole; T, triadimenol; Z, fluazinam. Concentrations 1 to 4 for each nonrespiratory inhibitor are given in Table 1. Concentrations 1 and 2 for each respiratory inhibitor are given in Table 2.

FIG. 4.

The DFA scores (1 to 3) from the analysis illustrated in Fig. 3 were averaged according to compound (i.e., scores for the members of each class were averaged) and subjected to HCA. A separation of the respiratory and nonrespiratory inhibitors was observed in the resulting dendrogram. Fluazinam (marked with an asterisk) is cited as an uncoupler of oxidative phosphorylation (17), and although it might conceivably be regarded as a respiratory inhibitor, it is not, of course, a respiratory chain inhibitor, and the level of inhibition it induces in cells growing on a fermentable carbon source is too great to arise from the inhibition of respiration-coupled processes alone; and therefore, this compound must inhibit other reactions within the cell, most likely on some proton-coupled uptake process necessary for fermentative growth.

FIG. 5.

Discrimination of respiratory and nonrespiratory inhibitors by using genetic programming to select discriminatory variables. Data were acquired as described above in the legends to Fig. 3 and 4 for all samples except fluazinam, and a genetic program was trained by using gmax-bio to evolve rules that can discriminate respiratory (symbols inside the top left dotted box) from nonrespiratory (symbols outside of the dotted box) modes of action. Variable (m/z) 144 was a member of all but one of the selected rules. In this example, its values are plotted against those of one of several other variables (m/z 141) that were used by the rules, here resulting in linearly separable clusters (shown as dotted lines). Triangles, training set; closed circles, test set for validation purposes; open circles, independent test set.

FIG. 6.

Data for the respiratory inhibitors were removed from the data set employed in the analyses illustrated in Fig. 3 and 4, and PCA and DFA were performed on the data for the remaining (nonrespiratory) inhibitors. Again, DFA classes were assigned according to compound, regardless of their concentration of application. DFA scores (1 to 3, averaged according to compound) were then subjected to HCA, and a dendrogram was produced.