The metabolic signature related to high plant growth rate in Arabidopsis thaliana

Abstract

The decline of available fossil fuel reserves has triggered world-wide efforts to develop alternative energy sources based on plant biomass. Detailed knowledge of the relations of metabolism and biomass accumulation can be expected to yield powerful novel tools to accelerate and enhance energy plant breeding programs. We used metabolic profiling in the model Arabidopsis to study the relation between biomass and metabolic composition using a recombinant inbred line (RIL) population. A highly significant canonical correlation (0.73) was observed, revealing a close link between biomass and a specific combination of metabolites. Dividing the entire data set into training and test sets resulted in a median correlation between predicted and true biomass of 0.58. The demonstrated high predictive power of metabolic composition for biomass features this composite measure as an excellent biomarker and opens new opportunities to enhance plant breeding specifically in the context of renewable resources.

Fig. 1.

Distribution of shoot biomass in the recombinant inbred line (RIL) population. Shown is the mean biomass (milligrams per plant) estimated by REML. The arrow indicates the biomass determined for the parental lines C24 (1.265 mg per plant) and Col-0 (1.254 mg per plant). The histogram of the shoot biomass of the RIL crosses to the parents is shown in Inset.

Fig. 2.

Significance (A) and predictive power (B) of the multiplicative model. (A) Histogram of canonical correlations between the metabolite matrix and random permutations of the biomass vector. The vertical line on the right corresponds to the canonical correlation between the actual biomass vector (DW) and the metabolite matrix (X)R. The distance to the median of the random correlations amounts to 17 standard deviations. (B) Prediction of the biomass by the metabolite matrix. Shown is one representative example of 20 repeats in the cross-validation. Size of the training set was 1,086, the 58 data points of the test set are displayed. The straight line represents the exact prediction.

Fig. 3.

Representation of the most important metabolites known by structure according to CCA on biochemical pathways. This representation of metabolism indicates all known metabolites we analyzed by using GC/MS that could be annotated in MapMan (28). Red color visualizes metabolites which are high ranked in CCA (positions 1–44), with ranking according to the color-coded scale bar.