APPROACH E: CHANGING MARKET SUPPLY BY RESTRICTING INPUTS

Agricultural and Food Policy,

Structural Characterization of the DEP Domains of P-Rex1

P-Rex1 is a guanine nucleotide exchange factor for Rho-GTPases, which is indirectly involved in the regulation of cell migration and proliferation. It contains a tandem DH/PH domain archetypal of the Dbl family of GEFs, two DEP and two PDZ domains, and a C-terminal end with weak homology to inositol polyphosphate 4-phosphatase. P-Rex1 is regulated by both intra-domain interactions and interactions with other proteins such as G-protein beta gamma, PKA and phosphatidylinositol (3,4,5)-trisphosphate. Upregulation of P-Rex1 has been found in multiple human cancers, making it a potential target for anti-cancer drug therapies. Therefore, structural characterization of P-Rex1 is critical. Currently, only the structures of the DH/PH tandem and PDZ1 domains of P-Rex1 have been determined. The goal of this project is to determine the structures of the DEP1 and DEP2 domains using X-Ray crystallography. P-Rex1-DEP1 (409-499 aa) protein was expressed in Escherichia coli and purified using affinity and size exclusion chromatography. The purified protein was then concentrated and used to set various crystallization screens. Small, well defined needles were observed and showed UV absorption, indicating that they consist of protein, and thus represent promising leads for a future structure determination. Optimization is in progress to grow bigger crystals or establish new conditions. Attempts are still being made to purify P-Rex1-DEP2 (500-602 aa), which thus far shows tendencies to aggregate

Development of a regional soil productivity index using an artificial neural network approach

Soil productivity indices represent ratings of the potential plant biomass production of soils. Inductive approaches determine productivity based on inferred effects of soil properties on yield. Conversely, deductive approaches use yield information to estimate productivity. Our objective was to compare the performance of both types of productivity indices for assessing regional soil productivity for wheat (Triticum aestivum L.) yield in the Pampas. Soil data from soil surveys and interpolated climate information were utilized. Wheat yield data from a 40-yr period and representing ?45 Mha were used. Inductive productivity indices showed a low correlation with observed yield (R2 < 0.45, P = 0.05). The best performance of deductive empirical methods was attained using a blind guess option, but soils could only be rated when yield data were available. Yield models based on the neural network approach had good performance (R2 = 0.614, root mean square error [RMSE] = 548 kg ha–1) and was used for regional productivity index development. This index could be extrapolated to soils for which yield data are not available, and its validation with yield averages was optimal (R2 = 0.728, P = 0.05). Regional high productivity was achieved for combinations of medium to high levels of soil organic C and soil available water storage capacity variables, which showed a positive interaction. This methodology for assessing soil productivity based on an empirical yield-based model may be applied in other regions of the world and for different crops.Fil: de Paepe, Josefina. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Alvarez, Roberto. Universidad de Buenos Aires. Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin