In this objective we are constructing genetic and metabolic network models for surface lipid accumulation on maize silks using a premier genetic resource in maize, a series of intermated B73xMo17 (IBM) mapping populations. These populations consist of 660 individual isolines, each of which carry a unique set of highly mosaic chromosomes derived from recombining the B73 and Mo17 genomes. The genetic factors controlling quantitative traits (termed QTLs) can be precisely mapped in these populations, which we have shown to exhibit a dynamic range of surface lipid metabolomes. We are employing a unique combination of metabolite analyses across this large set of maize IBM genotypes and along the length of the silk to determine and model the surface lipid reaction network. In synergy, we are employing transcriptomic approaches to identify candidate genes whose expression correlates with abundances of specific surface lipid metabolites. We will extend these integrated tools to predict and test how the genetic network drives the metabolic network for surface lipid production.

Figure 1: Surface lipid accumulation is being studied on silks that have emerged from the encasing husk leaves. In general, surface lipids such as hydrocarbons can accumulate to three to five-fold higher levels on emerged silks as compared to encased silks (Perera et al, 2010).

Figure 2: Husk leaves can be removed and the silks can be dissected to assess metabolite distribution as it relates to different developmental gradients.