Peter N. Scherer

Binary Insecticidal Crystal Protein from Bacillus thuringiensis, Strain PS149B1: Effects of Individual Protein Components and Mixtures in Laboratory Bioassays

Abstract 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 (Cry34Ab1) and a 44-kDa protein (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.

Rapid Degradation of a Binary, PS149B1, δ-Endotoxin of Bacillus thuringiensis in Soil, and a Novel Mathematical Model for Fitting Curve-Linear Decay

Abstract A novel, binary δ-endotoxin from Bacillus thuringiensis Berliner (Bt) strain PS149B1 has been identified, and the two genes that code for the peptides that make up the binary insecticidal crystal protein (bICP) have been inserted into maize plants, Zea mays L. Transformed maize plants that express the proteins are resistant to western corn rootworm, Diabrotica virgifera virgifera LeConte, a major pest of maize. A laboratory study was conducted to better understand the degradation of the bICP in soil. Insect bioassays using southern corn rootworm, Diabrotica undecimpunctata howardi Barber, were used to track degradation. A first-order kinetic model using a truncated data set predicts a half-life of <4 d, indicating a rapid rate of decay in soil. The degradation pattern for the complete data set exhibits systematic departures from a first-order kinetic model. A novel 3-parameter degradation model was developed and validated with 23 additional degradation data sets representing both Bt proteins and synthetic organic molecules. This new model often fits degradation patterns better than a first-order model and a 3-parameter, biexponential (biphasic) model. The new model also retains an additional degree of freedom in the analyses compared with the biexponential model, making it especially useful when modeling small data sets. The time until 50% dissipation of the bICP was estimated at <2 d based on this new model.

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.

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.

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

Summary 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.