|KU, KANG - University Of Illinois|
|YOUSEF, GAD - North Carolina State University|
|GUZMAN, IVETTE - North Carolina State University|
|JEFFERY, ELIZABETH - University Of Illinois|
|JUVIK, JOHN - University Of Illinois|
|JACKSON, ERIC - General Mills, Inc|
|BROWN, ALLAN - North Carolina State University|
Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 3/21/2013
Publication Date: 7/22/2013
Citation: Ku, K.M., Yousef, G.G., Guzman, I., Grusak, M.A., Jeffery, E., Juvik, J.A., Jackson, E.W., Brown, A.F. 2013. QTL mapping for quinone reductase activity in broccoli with Hepa1c1c7 cell lines [abstract]. American Society for Horticultural Science Annual Conference, July 22-25, 2013, Palm Desert, California. p. 118.
Technical Abstract: Floret tissue from 125 F2:3 broccoli families derived from the cross 'VI-158 x Brocolette Neri E. Cespuglio (BNC)' was harvested in 2009. Tissue was freeze-dried and stored in the dark at -80 until use. Distilled water was added to floret tissue (50 mg/mL) and auto-hydrolyzed for 24 hours in room temperature. Murine hepatoma cell lines (Hepa1c1c7, American Type Culture Collection) were used to measure quinone reductase (QR) activity. Broccoli extracts were incubated with Hepa1c1c7 cell in 96-well plates and then, after 24 hour incubation, QR activity measured. Triplicates of QR induction ratios were generated for each of 2 field replicates for a total of 6 QR data/line. The average of these scores was used to generate a phenotypic QR activity score for each line. A recently generated, highly saturated SNP-based map of this population was used to identify 4 significant QTL associated with QR activity. The most significant of these QTLs co-segregate with a major QTL for glucoraphanin variability in the population and maps to the GSL-ELONG locus on chromosome 2. The relationship between QR activity and other potential health promoting compounds in broccoli is further illustrated through the use of partial least square regression (PLS-R) model utilizing phytochemicals. Glucoraphanin, sulfur, gluconapin, and aliphatic glucosinolate were the most important variables to construct PLS-R model to predict QR activity. The results demonstrate the efficacy of utilizing plant populations segregating for multiple phytochemicals and nutrients for identifying factors that contribute to health related bioactivity.