Maximizing efficiency in sunflower breeding through historical data optimization.

Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.

Identification and recombinant expression of a novel chymotrypsin from Spodoptera exigua.

A novel chymotrypsin which is expressed in the midgut of the lepidopteran insect Spodoptera exigua is described. This enzyme, referred to as SeCT34, represents a novel class of chymotrypsins. Its amino-acid sequence shares common features of gut chymotrpysins, but can be clearly distinguished from other serine proteinases that are expressed in the insect gut. Most notable, SeCT34 contains a chymotrypsin activation site and the highly conserved motive DSGGP in the catalytic domain around the active-site serine is changed to DSGSA. Recombinant expression of SeCT34 was achieved in Sf21 insect cells using a special baculovirus vector, which has been engineered for optimized protein production. This is the first example of recombinant expression of an active serine proteinase which functions in the lepidopteran digestive tract. Purified recombinant SeCT34 enzyme was characterized by its ability to hydrolyze various synthetic substrates and its susceptibility to proteinase inhibitors. It appeared to be highly selective for substrates carrying a phenylalanine residue at the cleavage site. SeCT34 showed a pH-dependence and sensitivity to inhibitors, which is characteristic for semi-purified lepidopteran gut proteinases. Expression analysis revealed that SeCT34 was only expressed in the midgut of larvae at the end of their last instar, just before the onset of pupation. This suggests a possible role of this protein in the proteolytic remodelling that occurs in the gut during the larval to pupal molt.