Rising demand for food and bioenergy makes it imperative to breed for increased crop yield. Vegetative plant growth could be driven by resource acquisition or developmental programs. Metabolite profiling in 94 Arabidopsis accessions revealed that biomass correlates negatively with many metabolites, especially starch. Starch accumulates in the light and is degraded at night to provide a sustained supply of carbon for growth. Multivariate analysis revealed that starch is an integrator of the overall metabolic response. We hypothesized that this reflects variation in a regulatory network that balances growth with the carbon supply. Transcript profiling in 21 accessions revealed coordinated changes of transcripts of more than 70 carbon-regulated genes and identified 2 genes (myo-inositol-1-phosphate synthase, a Kelch-domain protein) whose transcripts correlate with biomass. The impact of allelic variation at these 2 loci was shown by association mapping, identifying them as candidate lead genes with the potential to increase biomass production.Arabidopsis ͉ association mapping ͉ biomass ͉ metabolites ͉ predictive P lants use light energy to convert CO 2 into carbohydrates.Although we might expect plant growth to be driven by the availability of carbohydrates and other central metabolites, recent studies point to a more complex interaction. Numerous free air CO 2 elevation studies show that higher rates of photosynthesis do not lead to a commensurate increase in biomass and yield (1). Studies of natural genetic diversity reveal a negative correlation between the levels of metabolites and biomass or yield (2-4). Although biomass was only very weakly correlated with individual metabolites in an Arabidopsis recombinant inbred line (RIL) population, a highly significant prediction was obtained when multivariate analysis was used on the entire metabolite profile (3). These results indicate that much of the genetic variation for biomass production affects the balance between resource availability and developmental programs, which determine how rapidly these resources are used for growth.Plants are exposed to a changeable environment and need to cope with continual changes in carbon (C) availability. One striking example is the daily alternation between a positive C balance in the light and a negative C balance in the dark. Growth nevertheless continues at night (5). This continued growth is possible because some newly fixed C accumulates as starch in the light and is remobilized at night to support respiration and growth. Starch is almost completely exhausted by the end of the night. If a change in the conditions (e.g., longer nights) leads to a temporary period of C starvation, the C budget is rebalanced (6-11) by increasing the rate of starch synthesis, decreasing the rate of starch breakdown, and decreasing the rate of growth (10,11). Starchless mutants illustrate the importance of this buffer; they cannot grow in a light/dark cycle because they become C-starved every night, leading to an inhibition of growth that is no...

The decline of available fossil fuel reserves has triggered worldwide 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.biomass ͉ canonical correlation ͉ metabolic profiling ͉ recombinant inbred line population ͉ biomarker

To evaluate components of fruit metabolic composition, we have previously metabolically phenotyped tomato (Solanum lycopersicum) introgression lines containing segmental substitutions of wild species chromosome in the genetic background of a cultivated variety. Here, we studied the hereditability of the fruit metabolome by analyzing an additional year's harvest and evaluating the metabolite profiles of lines heterozygous for the introgression (ILHs), allowing the evaluation of putative quantitative trait locus (QTL) mode of inheritance. These studies revealed that most of the metabolic QTL (174 of 332) were dominantly inherited, with relatively high proportions of additively (61 of 332) or recessively (80 of 332) inherited QTL and a negligible number displaying the characteristics of overdominant inheritance. Comparison of the mode of inheritance of QTL revealed that several metabolite pairs displayed a similar mode of inheritance of QTL at the same chromosomal loci. Evaluation of the association between morphological and metabolic traits in the ILHs revealed that this correlation was far less prominent, due to a reduced variance in the harvest index within this population. These data are discussed in the context of genomics-assisted breeding for crop improvement, with particular focus on the exploitation of wide biodiversity.

SummaryPlant growth and development are tightly linked to primary metabolism and are subject to natural variation. In order to obtain an insight into the genetic factors controlling biomass and primary metabolism and to determine their relationships, two Arabidopsis thaliana populations [429 recombinant inbred lines (RIL) and 97 introgression lines (IL), derived from accessions Col-0 and C24] were analyzed with respect to biomass and metabolic composition using a mass spectrometry-based metabolic profiling approach. Six and 157 quantitative trait loci (QTL) were identified for biomass and metabolic content, respectively. Two biomass QTL coincide with significantly more metabolic QTL (mQTL) than statistically expected, supporting the notion that the metabolic profile and biomass accumulation of a plant are linked. On the same basis, three out the six biomass QTL can be simulated purely on the basis of metabolic composition. QTL based on analysis of the introgression lines were in substantial agreement with the RIL-based results: five of six biomass QTL and 55% of the mQTL found in the RIL population were also found in the IL population at a significance level of P £ 0.05, with >80% agreement on the allele effects. Some of the differences could be attributed to epistatic interactions. Depending on the search conditions, metabolic pathway-derived candidate genes were found for 24-67% of all tested mQTL in the database AraCyc 3.5. This dataset thus provides a comprehensive basis for the detection of functionally relevant variation in known genes with metabolic function and for identification of genes with hitherto unknown roles in the control of metabolism.Keywords: metabolic quantitative trait loci (mQTL), recombinant inbred line (RIL), introgression line (IL), Arabidopsis, GC-MS, metabolomics.