The use of transgenic crops has generated concerns about transgene movement to unintended hosts and the associated ecological consequences. Moreover, the in-field monitoring of transgene expression is of practical concern (e.g., the underexpression of an herbicide tolerance gene in crop plants that are due to be sprayed with herbicide). A solution to these potential problems is to monitor the presence and expression of an agronomically important gene by linking it to a marker gene, such as GFP. Here we show that GFP fluorescence can indicate expression of the Bacillus thuringiensus cry1Ac gene when co-introduced into tobacco and oilseed rape, as demonstrated by insect bioassays and western blot analysis. Furthermore we conducted two seasons of field experiments to characterize the performance of three different GFP genes in transgenic tobacco. The best gene tested was mGFP5er, a mutagenized GFP gene that is targeted to the endoplasmic reticulum. We also demonstrated that host plants synthesizing GFP in the field suffered no fitness costs.

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This report introduces concepts, principles, and approaches for addressing uncertainty in decision making. The sources of uncertainty in decision making are discussed, emphasizing the distinction between uncertainty and risk, and the characterization of uncertainty and risk. The report provides a brief overview of decision theory and presents a practical method for modeling decisions under uncertainty and selecting decision alternatives that optimize the decision maker's objectives. The decision modeling methods introduced in this paper are suitable for both data-rich and data-poor decision environments. This report describes how to analyze the sensitivity of a decision model to improve understanding of the decision problem and build confidence in the conclusions of an analysis. Principles of adaptive management and adaptive engineering are discussed from a decision analysis perspective. Examples are provided to demonstrate how these methods could be applied within the U.S. Army Corps of Engineers. DISCLAIMER: The c ontents of t his r eport a re n ot t o b e u sed f or a dvertising, p ublication, or p romotional p urposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR.

Global regulatory agencies require bioinformatic sequence analysis as part of their safety evaluation for transgenic crops. Analysis typically focuses on encoded proteins and adjacent endogenous flanking sequences. Recently, regulatory expectations have expanded to include all reading frames of the inserted DNA. The intent is to provide biologically relevant results that can be used in the overall assessment of safety. This paper evaluates the relevance of assessing the allergenic potential of all DNA reading frames found in common food genes using methods considered for the analysis of T-DNA sequences used in transgenic crops. FASTA and BLASTX algorithms were used to compare genes from maize, rice, soybean, cucumber, melon, watermelon, and tomato using international regulatory guidance. Results show that BLASTX for maize yielded 7254 alignments that exceeded allergen similarity thresholds and 210,772 alignments that matched eight or more consecutive amino acids with an allergen; other crops produced similar results. This analysis suggests that each nontransgenic crop has a much greater potential for allergenic risk than what has been observed clinically. We demonstrate that a meaningful safety assessment is unlikely to be provided by using methods with inherently high frequencies of false positive alignments when broadly applied to all reading frames of DNA sequence.