Stacking transgenic event DAS-Ø15Ø7-1 alters maize composition less than traditional breeding.

The impact of crossing ('stacking') genetically modified (GM) events on maize-grain biochemical composition was compared with the impact of generating nonGM hybrids. The compositional similarity of seven GM stacks containing event DAS-Ø15Ø7-1, and their matched nonGM near-isogenic hybrids (iso-hybrids) was compared with the compositional similarity of concurrently grown nonGM hybrids and these same iso-hybrids. Scatter plots were used to visualize comparisons among hybrids and a coefficient of identity (per cent of variation explained by line of identity) was calculated to quantify the relationships within analyte profiles. The composition of GM breeding stacks was more similar to the composition of iso-hybrids than was the composition of nonGM hybrids. NonGM breeding more strongly influenced crop composition than did transgenesis or stacking of GM events. These findings call into question the value of uniquely requiring composition studies for GM crops, especially for breeding stacks composed of GM events previously found to be compositionally normal.

Safe composition levels of transgenic crops assessed via a clinical medicine model.

Substantial equivalence has become established as a foundation concept in the safety evaluation of transgenic crops. In the case of a food and feed crop, no single variety is considered the standard for safety or nutrition, so the substantial equivalence of transgenic crops is investigated relative to the array of commercial crop varieties with a history of safe consumption. Although used extensively in clinical medicine to compare new generic drugs with brand-name drugs, equivalence limits are shown to be a poor model for comparing transgenic crops with an array of reference crop varieties. We suggest an alternate model, also analogous to that used in clinical medicine, where reference intervals are constructed for a healthy heterogeneous population. Specifically, we advocate the use of distribution-free tolerance intervals calculated across a large amount of publicly available compositional data such as is found in the International Life Sciences Institute Crop Composition Database.

Evaluation of logistic and polynomial models for fitting sandwich-ELISA calibration curves.

Appropriately modeled calibration curves are important for accurately estimating the concentrations of proteins in samples evaluated in sandwich-format enzyme-linked immunosorbent assay (ELISA). Calibration curves are commonly fit using polynomial or logistic models. We compared the fit of a quadratic, cubic and 4-parameter logistic model for highly-replicated calibration curves across seven assays used for quantifying transgenic proteins in commercial crops. Results indicate that it is typically undesirable to include zero-concentration data when modeling these curves over the quantitative range, and simple polynomial models are typically preferable to the commonly recommended 4-parameter logistic model. These results are applicable to assays where precision constraints preclude interpolating results from the flat portions of the calibration curve, and it is under these conditions that the moderate improvements in accuracy described here will have impact.

Fit of four curve-linear models to decay profiles for pest control substances in soil.

Experiments that investigate the pattern of degradation of pest control substances in soil are often undertaken to estimate the persistence of compounds in the environment. Mathematical models are typically fit to decay data to facilitate the interpretation of the results and make predictions concerning the environmental fate of xenobiotics in soil. Four mathematical models were fit to 61 data sets to compare their performance in conforming to empirical patterns of degradation of pest control substances in soil. The use of composite residual plots allowed comparisons of the performance of the different models over many data sets. While an exponential model, estimated using nonlinear regression, fit many data sets very well, a shift-log, biexponential, and Monod equation appears superior in many cases, and systematic deviations from data sets are often less evident with the latter models. A knowledge of the patterns of bias typically exhibited by each model across many data sets may be useful for selecting models with reduced bias when fitting individual data sets.

Comparison of linear and nonlinear regression for modeling the first-order degradation of pest-control substances in soil.

First-order kinetic models are often used to profile the degradation of pest-control compounds in soil. This approach is based on enzyme theory and is often favored due to its simplicity and its requirement by regulatory agencies. Here, linear and nonlinear regression approaches to modeling first-order decay are compared. Composite residual plots of many soil degradation data sets are presented on a normalized scale. These plots illustrate the general error structure for the data and are useful for detecting common mis-specifications of the models. Results indicate that a nonlinear regression approach to modeling first-order decay of compounds in soil more accurately describes most data sets when compared with a linear approach. Specifically, the observed error structure does not support the broad use of a logarithmic transformation to stabilize the variance. In addition, models generated using the linear approach generally exhibit more dramatic systematic deviations from the observations as compared with models generated using the nonlinear approach. The analysis methods described here may be useful for comparing alternative models in this and other research areas.

Binary insecticidal crystal protein from Bacillus thuringiensis, strain PS149B1: effects of individual protein components and mixtures in laboratory bioassays.

A family of novel binary insecticidal crystal proteins, with activity against western corn rootworm, Diabrotica virgifera virgifera LeConte, was identified from Bacillus thuringiensis Berliner. A binary insecticidal crystal protein (bICP) from B. thuringiensis strain PS149B1 is composed of a 14-kDa protein (Cry34Abl) and a 44-kDaprotein (Cry35Ab1). These proteins have been co-expressed in transgenic maize plants, Zea mays L., and effectively control western corn rootworm larvae under field conditions. Laboratory experiments were conducted to better understand the contribution of each component protein to the in vivo activity of the bICP. The 14-kDa protein is active alone against southern corn rootworm, Diabrotica undecimpunctata howardi Barber, and was synergized by the 44-kDa protein. In mixtures, the concentration of the 14-kDa protein had a greater impact on efficacy than the 44-kDa component. Although both proteins are clearly required for maximal insecticidal activity, laboratory results did not support the formation of a stable, fixed-ratio complex of the two component proteins.