In-silico prediction of dislodgeable foliar residues and regulatory implications for plant protection products.

BACKGROUND: When experimentally determined dislodgeable foliar residue (DFR) values are not available, regulatory agencies use conservative default DFR values as a first-tier approach to assess post-application dermal exposures to plant protection products (PPPs). These default values are based on a limited set of field studies, are very conservative, and potentially overestimate exposures from DFRs. OBJECTIVE: Use Random Forest to develop classification and regression-type ensemble models to predict DFR values after last application (DFR0) by considering experimentally-based variability due to differences in physical and chemical properties of PPPs, agronomic practices, crop type, and climatic conditions. METHODS: Random Forest algorithm was used to develop in-silico ensemble DFR0 prediction models using more than 100 DFR studies from Corteva AgriscienceTM. Several variables related to the active ingredient (a.i.) that was applied, crop, and climate conditions at the time of last application were considered as model parameters. RESULTS: The proposed ensemble models demonstrated 98% prediction accuracy that if a DFR0 is predicted to be less than the European Food Safety Authority (EFSA) default DFR0 value of 3 µg/cm2/kg a.i./ha, it is highly indicative that the measured DFR value will be less than the default if the study is conducted. If a value is predicted to be larger than or equal to the EFSA default, the model has an 83% prediction accuracy. IMPACT STATEMENT: This manuscript is expected to have significant impact globally as it provides: A framework for incorporating in silico DFR data into worker exposure assessment, A roadmap for a tiered approach for conducting re-entry exposure assessment, and A proof of concept for using existing DFR data to provide a read-across framework that can easily be harmonized across all regulatory agencies to provide more robust assessments for PPP exposures.

Complex Trait Loci in Maize Enabled by CRISPR-Cas9 Mediated Gene Insertion.

Modern maize hybrids often contain biotech and native traits. To-date all biotech traits have been randomly inserted in the genome. Consequently, developing hybrids with multiple traits is expensive, time-consuming, and complex. Here we report using CRISPR-Cas9 to generate a complex trait locus (CTL) to facilitate trait stacking. A CTL consists of multiple preselected sites positioned within a small well-characterized chromosomal region where trait genes are inserted. We generated individual lines, each carrying a site-specific insertion landing pad (SSILP) that was targeted to a preselected site and capable of efficiently receiving a transgene via recombinase-mediated cassette exchange. The selected sites supported consistent transgene expression and the SSILP insertion had no effect on grain yield. We demonstrated that two traits residing at different sites within a CTL can be combined via genetic recombination. CTL technology is a major step forward in the development of multi-trait maize hybrids.

Uniform Expression and Relatively Small Position Effects Characterize Sister Transformants in Maize and Soybean.

Development of transgenic cell lines or organisms for industrial, agricultural, or medicinal applications involves inserting DNA into the target genome in a way that achieves efficacious transgene expression without a deleterious impact on fitness. The genomic insertion site is widely recognized as an important determinant of success. However, the effect of chromosomal location on transgene expression and fitness has not been systematically investigated in plants. Here we evaluate the importance of transgene insertion site in maize and soybean using both random and site-specific transgene integration. We have compared the relative contribution of genomic location on transgene expression levels with other factors, including cis-regulatory elements, neighboring transgenes, genetic background, and zygosity. As expected, cis-regulatory elements and the presence/absence of nearby transgene neighbors can impact transgene expression. Surprisingly, we determined not only that genomic location had the least impact on transgene expression compared to the other factors that were investigated but that the majority of insertion sites recovered supported transgene expression levels that were statistically not distinguishable. All 68 genomic sites evaluated were capable of supporting high-level transgene expression, which was also consistent across generations. Furthermore, multilocation field evaluation detected no to little decrease in agronomic performance as a result of transgene insertion at the vast majority of sites we evaluated with a single construct in five maize hybrid backgrounds.