# An Integrated Metabolomics Study of Glucosinolate Metabolism in Different Brassicaceae Genera
## 2 Results  
### Profiles and Correlations among Glucosinolates and Their Precursors and Breakdown Products  
First, untargeted profiling of the non-volatile and volatile metabolites of the 12 Brassicaceae vegetables was performed. After detection frequency (>100%) and low relative standard deviation (RSD < 30%) filtration, the remaining metabolites were identified, with their relative abundances presented as a heat map in Supplementary Materials Figure S1. In total, 223 metabolites were detected and identified, including 32 glucosinolates. Seventeen of these glucosinolates were then quantified with available commercial standards. Those detected glucosinolates for which standards were not commercially available [21] were thus quantified using a semi-quantitative approach. These untargeted analyses, as well as the quantitative/semi-quantitative analyses of the glucosinolates in the 12 Brassicaceae vegetables (3 batches each), were performed as a discovery data set. The glucosinolate data obtained here show profiles and trends similar to various previous studies (Supplementary Materials Figure S1). For example, rocket salad was found to be high in glucoraphanin, which confirmed the previous report of up to 52% of its total glucosinolate content [27]. Similarly, dehydroroerucin and its hydrolysis product 4-methylthio-3-butenyl isothiamate were previously found in high abundances in radish and cherry radish [10]. Glucoerucin was detected in cabbage, cauliflower, rocket, and radishes, but not in the other Brassica genus plants in the present study. Interestingly, glucoerucin was reported to form part of the profile that acts as an attractant for the diamondback moth in some cabbage accessions [12]. These data demonstrate that different types of Brassica, albeit from the same family, have their unique content of glucosinolates and volatile secondary isothiamate metabolites. However, it must also be considered that the various Brassica samples were obtained commercially, and thus the observed variations in glucosinolates and isothiyanates may also be associated with differences in growth location and growth practices, as well as storage conditions and duration. Although it may be considered that vast changes in concentration and qualitative differences are likely most impacted upon by Brassica variety within this study, only by performing controlled field experiments where the different Brassica varieties are produced, harvested, and stored within identical conditions can all differences observed in glucosinolate and isothiyanate profiles be attributed to variety alone. Furthermore, Pearson correlation analysis revealed close correlations between some specific glucosinolates and their breakdown product isothiyanates (Supplementary Materials Figure S2). The allyl ITC, 3-Butenyl ITC, 3-Methylthionpropyl ITC, 4-Methyllpentyl ITC, and Phenethyl-ITC were found to be positively correlated with their respective parental glucosinolates across the 12 vegetables. On the other hand, there was no close correlation between amino acids at the beginning of the biosynthetic pathway and the secondary metabolites glucosinolates (Supplementary Materials Figure S1). For example, we would expect methionine to be crucial for the synthesis of some of the glucosinoltes such as sinigrin, glucoiberin, glucosativin, glucoerucin, and glucoraphanin in the radish, however, the data shows very different trends between methionine and these downstream glucosinolates (Supplementary Materials Figure S1). In contrast, while watercress contains high abundance of methionine (about 150 \(\upmu\)g/g dry weight, Supplementary Materials Table S1), the glucosionate metabolites such as sinigrin, glucoiberin, glucoerucin, and gluconapin that are involved in the rest of the pathway were not detected at high abundance. Similar results were obtained from the quantitative analysis of amino acids and glucosinolates (Supplementary Materials Table S1).