DESCRIPCION DEL ESTUDIO

The advantage of HPLC over spectrophotometry was that the use of columns and anion exchange allowed for separation of the analyte of interest from background interference and separated a number of compounds from a mixture. A disadvantage was that HPLC took a longer time to set up and calibrate. Spectrophotometry would have been the preferred method as it was available in the lab, easy to use, was able to process multiple samples in short time and did not require too much expertise to run. However, the samples did not lend themselves to assessment by spectrophotometry due to interference by other compounds. The next stage was identification of compounds.

In HPLC the only way to be certain that the correct compounds have been identified is by having that pure compound to compare against and the only analytical pure glucosinolate available commercially was allyl glucosinolate. Alternatively, the individual glucosinolates in the BSP extract could be identified with HPLC equipped with mass spectrometry. This was only available at Christchurch and getting samples analysed from here, then trying to extrapolate the data was technically difficult. Fortunately, LCQTOF-HRMS became available after only a few months of working with the other systems.

The next issue to overcome was in the identification and quantitation of the metabolites from glucosinolate hydrolysis. As small volatile compounds they lend themselves well to analysis by GC-MS. However, even with the availability of mass spectrometry, the only way to be certain of identification was to match spectra with pure compounds and the only commercially available analytical standard was sulforaphane (S6317), BITC (Aldrich 242494) and butanenitrile (Fluka 08436).

For example, the first GC-MS chromatogram using the NIST12 and NIST62 database libraries identified every nitrile as butanenitrile. As butanenitrile was not on the list of possible products and could not be giving multiple peaks across the chromatogram, butanenitrile (Fluka 08436) was purchased and compared with the multiple butanenitrile peaks from my chromatogram results. This confirmed that none of the peaks were butanenitrile because the retention time for the real butanenitrile is ~5 minutes and the butanenitrile peaks in our chromatograms were at 6.8, 8.6 and 12.3 min. In fact,

what the software had annotated by automatic integration of the peaks as butanenitrile, were actually iberverin nitrile, erucin nitrile and sulforaphane nitrile.

To achieve the identification of the compounds, the following approach was undertaken. GC-MS solutions software (Shimadzu) processed the samples as batch files which detected and integrated the peaks against NIST12 and NIST62 commercial libraries. Next the author’s nitrile/ITC library was included and the samples reprocessed. Next manual integration was performed to separate any double peaks or rename incorrectly identified peaks and a manual visual scan was done over the whole chromatogram for any unidentified or interesting peaks.

The nitrile/ITC library was built from the commercially available analytical standards sulforaphane, butanenitrile and BITC (as described previously) and from others’ published mass spectral data on all of the compounds in Table 3.2. The published spectral signatures of compounds were not identical to the same compounds identified in BSP extract because of differences in the GC-MS equipment. For example, the slight variations between columns meant that ion fragments were produced at different abundances. Also, as the column ages, the retention time shifts. What did not change however, were the mass values for the ions so sulforaphane for example, had a 160 m/z ion, a 72 m/z ion, a 64 m/z ion and a 55 m/z ion although the retention times and ion abundances did vary between columns and also between batch runs. The nitriles, erucin nitrile and sulforaphane nitrile share some of the same fragmentation ions because they are the same compound except for their oxidation state. As shown in chapter 1 (Figure 1.6), erucin nitrile and iberverin nitrile differ only by one carbon on their alkane chain as do iberin nitrile and sulforaphane nitrile. Identification for nitriles required matching both the ions then checking the retention times. The spectra of the identified compounds are given at the Appendix A, Figure A14 – A15).

While there is an advantage of having pure standards for comparison enabling identification, a drawback is that this can be limiting to finding only the products that match the standards. By not having the benefit of these standards, every peak was monitored by checking every chromatogram manually.

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LCQTOF-HRMS enabled the profiling of individual glucosinolates and GC-MS allowed the fate of these glucosinolates to be monitored. Now experiments combining the bacteria and glucosinolates could proceed and if the glucosinolate composition of BSP extract was changing in any way, these analysis methods should capture these events.

3.6 References

(1) Kushad, M. M.; Brown, A. F.; Kurilich, A. C.; Juvik, J. A.; Klein, B. P.; Wallig, M. A.; Jeffery, E. H. Variation of glucosinolates in vegetable crops of

Brassica oleracea. J Agric Food Chem 1999,47, 1541-1548.

(2) Morra, M. J.; Borek, V. Glucosinolate preservation in stored Brassicaceae

seed meals. J Stored Prod Res 2010,46, 98-102.

(3) O'Hare, T. J.; Wong, L. S.; Force, L. E.; Irving, D. E. Glucosinolate composition and anti-cancer potential of seed-sprouts from horticultural members of the Brassicaceae. In Desjardins, Y., Ed. Acta Horticulturae: Quebec, Canada, 2005; Vol. 744, pp 181-188.

(4) Omirou, M. D.; Papadopoulou, K. K.; Papastylianou, I.; Constantinou, M.; Karpouzas, D. G.; Asimakopoulos, I.; Ehaliotis, C. Impact of nitrogen and sulfur fertilization on the composition of glucosinolates in relation to sulfur assimilation in different plant organs of broccoli. J Agric Food Chem

2009,57, 9408-9417.

(5) Angel Gabriel, A.-S.; Octavio, P.-L. Assessment of glucosinolates in broccoli by three different methodologies. J Food Biochem 1992,16, 265-275.

(6) Shapiro, T. A.; Fahey, J. W.; Wade, K. L.; Stephenson, K. K.; Talalay, P. Chemoprotective glucosinolates and isothiocyanates of broccoli sprouts.

Cancer Epidemiol Biomark Prev 2001,10, 501-508.

(7) Fahey, J. W.; Zhang, Y.; Talalay, P. Broccoli sprouts: An exceptionally rich source of inducers of enzymes that protect against chemical carcinogens. Proc Natl Acad Sci U S A 1997,94, 10367-10372.

(8) Palmieri, S.; Leoni, O.; Iori, R. A steady-state kineties study of myrosinase with direct ultraviolet spectrophotometric assay. Anal Biochem 1982, 123, 320-324.

(9) Matusheski, N. V.; Wallig, M. A.; Juvik, J. A.; Klein, B. P.; Kushad, M. M.; Jeffery, E. H. Preparative HPLC Method for the Purification of Sulforaphane and Sulforaphane Nitrile from Brassica oleracea. J Agric Food Chem 2001,49, 1867-1872.

(10) Prestera, T.; Fahey, J. W.; Holtzclaw, W. D.; Abeygunawardana, C.; Kachinski, J. L.; Talalay, P. Comprehensive chromatographic and

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spectroscopic methods for the separation and identification of intact glucosinolates. Anal Biochem 1996,239, 168-179.

(11) Jones, R. B.; Faragher, J. D.; Winkler, S. A review of the influence of postharvest treatments on quality and glucosinolate content in broccoli

FOUR

4.0 Selection of bacteria

Plant myrosinase (thioglucosidase EC 3.2.3.1) is a member of glycoside hydrolase family 1 (GH1) (www.cazy.org/), but while GH1 family enzymes with diverse substrate specificities are commonly found in bacteria, none have been functionally characterised as having thioglucosidase activity. Bacterial genes encoding similar GH1 family glycoside hydrolases were sought from bacterial protein data (NCBI BlastP) with the criteria for bacterial selection being that the bacteria should be beneficial food grade organisms and be able to survive transit through the gut or at least remain metabolically active during transit.

By cloning the putative myrosinase-encoding genes from bacteria it may be possible to discover a novel bacterial myrosinase. Also from finding this, it may be possible to narrow down the number of bacteria that are screened for being the best at metabolising glucosinolates (GSLs).

The aim of this study was to identify myrosinase-producing bacteria and the first approach was to employ database mining to identify putative myrosinase-encoding bacterial genes and from this, select potential candidates, clone these genes and express them in recombinant Escherichia coli. It was hoped that pure recombinant protein could then be collected and

assessed for myrosinase activity. In the event that this protein possessed myrosinase activity, this would be the first bacterial myrosinase identified since 19741,2 and the first to be cloned.

Another way to find GSL metabolising bacteria is to test bacteria and to this end, some preliminary assays for myrosinase activity were also undertaken.