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Coding sequences for major trichome regulatory genes, including the positive regulators GLABRA 1(GL1), GLABRA 2 (GL2), ENHANCER OF GLABRA 3 (EGL3), and TRANSPARENT TESTA GLABRA 1 (TTG1) and the negative regulator TRIPTYCHON (TRY), were cloned from wild Brassica villosa, which is characterized by dense trichome coverage over most of the plant. Transcript (FPKM) levels from RNA sequencing indicated much higher expression of the GL2 and TTG1 regulatory genes in B. villosa leaves compared with expression levels of GL1 and EGL3 genes in either B. villosa or the reference genome species, glabrous B. oleracea; however, cotyledon TTG1 expression was high in both species. RNA sequencing and Q-PCR also revealed an unusual expression pattern for the negative regulators TRY and CPC, which were much more highly expressed in trichome-rich B. villosa leaves than in glabrous B. oleracea leaves and in glabrous cotyledons from both species. The B. villosa TRY expression pattern also contrasted with TRY expression patterns in two diploid Brassica species, and with the Arabidopsis model for expression of negative regulators of trichome development. Further unique sequence polymorphisms, protein characteristics, and gene evolution studies highlighted specific amino acids in GL1 and GL2 coding sequences that distinguished glabrous species from hairy species and several variants that were specific for each B. villosa gene. Positive selection was observed for GL1 between hairy and non-hairy plants, and as expected the origin of the four expressed positive trichome regulatory genes in B. villosa was predicted to be from B. oleracea. In particular the unpredicted expression patterns for TRY and CPC in B. villosa suggest additional characterization is needed to determine the function of the expanded families of trichome regulatory genes in more complex polyploid species within the Brassicaceae.

A new method to improve the efficiency of flanking sequence identification by genome walking was developed based on an expanded, sequential list of criteria for selecting candidate enzymes, plus several other optimization steps. These criteria include: step (1) initially choosing the most appropriate restriction enzyme according to the average fragment size produced by each enzyme determined using in silico digestion of genomic DNA, step (2) evaluating the in silico frequency of fragment size distribution between individual chromosomes, step (3) selecting those enzymes that generate fragments with the majority between 100 bp and 3,000 bp, step (4) weighing the advantages and disadvantages of blunt-end sites vs. cohesive-end sites, step (5) elimination of methylation sensitive enzymes with methylation-insensitive isoschizomers, and step (6) elimination of enzymes with recognition sites within the binary vector sequence (T-DNA and plasmid backbone). Step (7) includes the selection of a second restriction enzyme with highest number of recognition sites within regions not covered by the first restriction enzyme. Step (8) considers primer and adapter sequence optimization, selecting the best adapter-primer pairs according to their hairpin/dimers and secondary structure. In step (9), the efficiency of genomic library development was improved by column-filtration of digested DNA to remove restriction enzyme and phosphatase enzyme, and most important, to remove small genomic fragments (