Rapid classification of phenotypic mutants of Arabidopsis via metabolite fingerprinting

Abstract

We evaluated the application of gas chromatography-mass spectrometry metabolic fingerprinting to classify forward genetic mutants with similar phenotypes. Mutations affecting distinct metabolic or signaling pathways can result in common phenotypic traits that are used to identify mutants in genetic screens. Measurement of a broad range of metabolites provides information about the underlying processes affected in such mutants. Metabolite profiles of Arabidopsis (Arabidopsis thaliana) mutants defective in starch metabolism and uncharacterized mutants displaying a starch-excess phenotype were compared. Each genotype displayed a unique fingerprint. Statistical methods grouped the mutants robustly into distinct classes. Determining the genes mutated in three uncharacterized mutants confirmed that those clustering with known mutants were genuinely defective in starch metabolism. A mutant that clustered away from the known mutants was defective in the circadian clock and had a pleiotropic starch-excess phenotype. These results indicate that metabolic fingerprinting is a powerful tool that can rapidly classify forward genetic mutants and streamline the process of gene discovery.

Figure 1.

Starch content of leaves of wild-type (white bars) and mutant (gray bars) plants harvested 1 h before the end of the night. The insoluble fractions of perchloric acid extracts were assayed for starch. FW, Fresh weight. Values are the means ± se (n = 4). Note the scale change on the y axis.

Figure 2.

Example of GC-MS results from Col wild-type (red) and mex1 (blue) extracts. The different metabolites were identified by their retention time and characteristic mass spectra. The detailed mass spectrum of a metabolite found in large amounts in mex1 and in low abundance in the wild type is inset. This peak corresponds to maltose.

Figure 3.

Metabolic maps of central metabolism displaying the changes in the mean metabolite levels in sex4 and sex3 relative to the wild type (data from Supplemental Table S2). Increases are shown in orange and decreases are shown in blue. The color intensity indicates the magnitude of the changes compared to the wild type as indicated (bottom right). Metabolites in gray were not measured. Metabolites in black were measured but unchanged. Note that not all measured metabolites are visualized on these metabolic maps.

Figure 4.

HCA of metabolic profiles. Normalized metabolite data (Supplemental Table S2) were transformed by log₁₀: analysis was performed with Euclidean distances, using an incremental linkage method to generate the clusters. Each branch represents the metabolite profile of a single biological sample. Colors indicate the genotype, given on the left.

Figure 5.

Probability-based comparisons of the metabolite profiles of wild types and mutants. A, Probabilities (percentages) that each metabolic profile is identified as that of a known type. In the top part of the chart, known types are compared with one another, establishing similarity-based relationships. In the bottom part, the four unknown mutants are compared with the known types. B, Visualization of metabolite changes in each of the wild types or mutants. Biological replicates are plotted together. Deviations from the baseline for each metabolite of each genotype indicate the relative increases or decreases relative to the technical replicate samples. Colored circles centered on specific metabolite-genotype combinations give an indication of its importance to the classification in A.