Eric R. Ward

Inducible herbicide resistance

The invention relates to DNA constructs which are capable of conferring on a plant inducible resistance to a herbicide. The inducible effect may be achieved by using a gene switch such as the alcA/alcR switch derived from A. nidulans. The invention relates in particular to inducible resistance to the herbicide N-phosphonomethyl glycine (glyphosate) and its salts.

Systemic Acquired Resistance in Tobacco: Use of Transgenic Expression to Study the Functions of Pathogenesis-Related Proteins

Systemic Acquired Resistance (SAR) is the resistance to a variety of fungal, bacterial, and viral pathogens induced in many plant species by prior inoculation with a necrotizing pathogen. While the mechanism of resistance is unclear, there is a strong correlation in tobacco between the resistant state and the presence of the so-called “Pathogenesis-Related proteins” (PR-proteins). To study the involvement of the PR-proteins in SAR, we have engaged in a comprehensive program to clone and express constitutively in tobacco the cDNAs for all the PR-proteins. In addition to cloning the well-described PR 1–5 gene families, we have used differential cDNA screening to clone several new gene families which are induced by TMV inoculation. Homozygous transgenic seed lines expressing the various genes (or their anti-sense transcripts) are tested against a battery of pathogens for altered disease phenotypes. We have determined that transgenic plants expressing constitutively tobacco PR1 protein show significant resistance to blue mold (Peronosopra tabacino). The resistance, exhibited as a delay in symptom development, is observed in several independent transgenic PR1 lines, as well as in F1 crosses with a PR1 transgenic line as parent. Lines expressing the eDNA of a newly defined induced gene, denoted SAR8.2d, exhibit resistance to another oomycete pathogen, Phytophthora parasitica. As with the PR1 resistance, multiple transgenic events show the resistance, which is observed as much delayed symptom development.

Target-based discovery of crop protection chemicals

likely be driven heavily by an inversion of the current strategy. More rapid understanding of gene–phenotype relationships will come from physiological array data on libraries of genetically diverse cell constructs studied in environmental grids, then dissecting the gene–environment interactions that generate significant changes in phenotype, rather than the current method of searching for some change in phenotype associated with modification in a particular gene. Genome, transcriptome, and proteome data, and the bioinformatics tools to classify and mine them, are powerful tools. However, many current realizations and concepts in these fields have almost nothing to do with kinetic aspects of metabolism and physiology that are fundamental to the working of cells and higher organisms (see, e.g., ref. 18). The answers that science and technology seek from contemporary bioinformatics can only come from new computational tools that capture the essence of the complex networks of interactions and rate processes that connect genes to phenotype.