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Proposed Method for Estimating Health-Promoting Glucosinolate and Hydrolysis Products in Broccoli (Brassica oleracea var. italica) Using Relative Transcript Abundance Talon M. Becker, Elizabeth H. Jeffery, and John A. Juvik J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b04668 • Publication Date (Web): 19 Dec 2016 Downloaded from http://pubs.acs.org on December 20, 2016

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Journal of Agricultural and Food Chemistry

1 Proposed Method for Estimating Health-Promoting Glucosinolate and Hydrolysis Products in Broccoli (Brassica oleracea var. italica) Using Relative Transcript Abundance

Talon M. Becker1, Elizabeth H. Jeffery2, and John A. Juvik1*

1

Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801-

3838 2

Department of Food Science and Human Nutrition, University of Illinois at Urbana-Champaign,

Urbana, IL 61801-3838

*

To whom correspondence should be addressed

John A. Juvik Department of Crop Science, University of Illinois at Urbana-Champaign, 307 ERML, 1201 West Gregory Drive, Urbana, University of Illinois, Urbana, Illinois 61801. Tel.: 217-3331966, email: [email protected]

ACS Paragon Plus Environment


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2 Proposed Method for Estimating Health-Promoting Glucosinolates and Their Hydrolysis Products in Broccoli (Brassica oleracea var. italica) Using Relative Transcript Abundance ABSTRACT Due to the importance of glucosinolates and their hydrolysis products in human nutrition and plant defense, optimizing the content of these compounds is a frequent breeding objective for Brassica crops. Towards this goal, we investigated the feasibility of using models built from these data for the prediction of glucosinolate and hydrolysis product concentrations in broccoli. We report that predictive models explaining at least 50% of the variation for a number of glucosinolates and their hydrolysis products can be built when predicting within the same season, but prediction accuracy decreased when using models built from one season’s data for prediction of the opposing season. This method of phytochemical profile prediction could potentially allow for lower phytochemical phenotyping costs and larger breeding populations. This, in turn, could improve selection efficiency for phase II induction potential, a type of chemopreventive bioactivity, by allowing for the quick and relatively cheap content estimation of phytochemicals known to influence the trait.


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3 INTRODUCTION Due to their apparent importance in human health and well-established importance in plant defense, the study of secondary metabolites in crop plants has been growing in popularity, as new technologies have allowed for their accurate quantification. Among these, glucosinolates (GSs) are a class of sulfur-rich secondary metabolites derived from amino acids and found in the order Brassicales1,2. This diverse group of secondary metabolites is known to have at least 132 naturally occurring members, with probably more than another dozen to be elucidated3,4. There are three major classes of GSs in the Brassica genus; aliphatic, indole, and aromatic derived from methionine, tryptophan, and phenylalanine/tyrosine, respectively5,6. Glucosinolates are largely considered inert and are generally separated in intact tissue from the enzymes that act upon them called myrosinases7,8. Upon tissue disruption, the hydrolysis reaction mediated by myrosinases result in the production of GS hydrolysis products (GSHPs). The primary function of these compounds is in plant defense against herbivory and pathogens9–13. However, beyond plant protection, these compounds have also been shown to exhibit cancer chemopreventive qualities and other health benefits through a number of mechanisms14–16. If breeding for increased chemopreventive bioactivity in Brassica vegetables through the manipulation of GS and GSHP profiles, the ability to induce phase II enzymes in mammals is considered one of the most important contributing factors17. The genes encoding cytoprotective phase II enzymes contain an antioxidant response element (ARE) in the upstream promoter region, activated when electrophilic GSHPs trigger nuclear factor (erythroid-derived 2)-like 2 (NFE2L2 or Nrf2) passage into the nucleus18. Studies on action of Nrf2 suggest that this transcription factor can inhibit nuclear factor κB (NF-κB)-mediated inflammatory responses, upregulate antioxidant enzymes, and promote a number of chemopreventive actions, including



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4 phase II enzyme induction for detoxifying and clearing xenobiotics16. It is hypothesized that increasing Nrf2 activity, and subsequently phase II cytoprotective enzyme activity, will reduce the body’s exposure to xenobiotic carcinogens due to an increased rate of detoxification and excretion15,19–21. Increasing production of these enzymes can allow the human body, possibly depending on polymorphisms in genes such as glutathione S-transferases22, to more efficiently clear carcinogens before initiation of carcinogenesis23. Because of this, chemopreventive potential of a given compound or mixture of compounds is often quantified as induction of quinone reductase (QR), a phase II enzyme24,25. The utility of QR as a biomarker for phase II enzyme induction has been established by research showing a strong correlation between QR induction and that of other phase II enzymes26–28. Breeding directly for QR induction potential (QRIP) seems to be impractical due to the fact that measurement of QRIP is very costly and time consuming, thereby not allowing for accurate and high-throughput assaying of the large populations required in a breeding program. Due to these reasons, a better strategy for increasing QRIP of a given vegetable may be to manipulate specific phytochemicals, such as GSs and GSHPs, which are known to affect QRIP. However, while less time consuming than QRIP, collection of GS and GSHP data is still an expensive and laborious exercise. One method for increasing the throughput of a breeding program for increased chemopreventive bioactivity would be the use of transcriptomic data to predict levels of individual GSs or GSHPs associated with QRIP. In order to do this, predictive models must be built that can incorporate transcript abundance data as independent variables in the models. A problem that exists with this approach is that the number of genes associated with GS metabolism for which transcript abundance could be quantified (predictors/independent variables) is very large, and ideally, the number of biological samples needed to build said


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5 models (response/dependent variables) would be as small as possible in order to minimize resources expended on phytochemical profiling. This n

Proposed Method for Estimating Health-Promoting ... - ACS Publications

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