Chapter 6 Nutrient content and metabolite profiles of exotic and native host plants of porina
6.3.3 Sampling for metabolite profiling
Five plant samples per species were selected at random and a 7 cm section cut from the base of each plant (sampling was done at midday because most metabolites are subject to strong diurnal rhythms and are at their peak at midday (Lisec 2006). The samples were sealed in aluminium foil then snap frozen in liquid nitrogen, lyophilised in a freeze dryer (Thermo Savant) for 24 h, and stored at 80℃.
6.3.3.1 Plant metabolite and derivatization
About 40 mg each of freeze-dried sample was weighed (Sartorius CP 2245) into labelled FastPrep vials (MPBiomedicals, USA) containing 2.5 mm zirconia beads, and 1 mL of methanol (80% v/v) was added (without the plant material thawing). A blank was also prepared without the plant material.
To each sample, 60 μL ribitol (0.2 mg/ml) was added as internal standard and vortexed for 30 s.
Samples were homogenised in an MP FastPrep-24™ (MPBiomedicals, USA) at 4 m/s for 40 s, and centrifuged for 5 min at 20817 g. The resultant supernatant was transferred to Eppendorf tubes.
Samples were dried in the Speed Vac (Labconco CentriVap®, USA) at 30℃ for 360 min. Nitrogen gas was added to prevent oxidation and samples were stored in an airtight container at -80℃.
Trimethylsilylation is a commonly used method to derivatise a broad range of metabolites, including sugars, sugar alcohols, amines, amino acids and organic acids, in order for them to become volatile and thermally stable (Roessner et al. 2000). A total of 40 μL of methoxyamination
reagent (prepared by dissolving 20 mg/mL of methoxyamine hydrochloride in pure pyridine at 20- 25℃ in a glass vial) was added to samples, including a blank (empty tube) used as a control, using a glass syringe. The mixture was vortexed and put in a hot-water bath at 37℃for 90 min. A total of
40 μL of MSTFA reagent (N-methyl-N-(trimethylsilyl) trifluoroacetamide) was then added, centrifuged at 20817 g for 5 s and the supernatant was transferred into glass vials suitable for gas chromatograph-mass spectrometer (GC-MS) analysis.
6.3.3.2 GC-MS metabolite profiles
Prepared metabolite samples were analysed using a Shimadzu GCMS-QP2010 Ultra (Shimadzu, Japan) gas chromatograph-mass spectrometer fitted with a Restek Rtx-5ms fused silica capillary
column (30.0 m x 0.25 mm i.d. x 0.25 μm, Bellefonte, PA, USA) and supplied with a 5 m guard
column. A CTC-Combi PAL autosampler (PAL LHX-xt) was used to inject 1 µL of sample into the GC injection port, operating in split mode at 250℃ and 78.6 kPa pressure at a ratio of 20:1. After injection, the column oven was held at 80℃ for 3 min, then heated to 330℃ at 5℃/min, and held at this temperature for 13 min. Helium was used as the carrier gas with the constant linear velocity set at 34.1 cm/s in split mode (1.0 mL min-1). The mass spectrometer (MS) was operated in electron impact ionisation mode with 70 eV and mass range of 50 to 600 m/z. The temperature of the capillary interface was 250℃, with the source temperature set at 200℃.
6.3.3.3 Mass-spectral tags and their identification
The gas chromatography separates complex mixtures of metabolite derivatives into a series of compounds that enter the mass spectrometer and are subsequently ionized, fragmented and detected. Each metabolite represented by one or more ionic fragments of precise mass, referred to as mass-spectral tags (MST) (Desbrosses et al. 2005), with each MST having properties that facilitate unequivocal identification of the parent metabolite (using the linear retention index (IR)), following comparison to a pure reference compound (Wagner et al. 2003). MassFinder 4 software was used to visualise and interpret MST tags, by identifying peaks and assigning names to these peaks by matching MST and retention index to reference compounds in the NIST EPA/NIH Mass Spectral Library database (National Institute of Standards and Technology, NIST11) and Wiley
Registry of Mass Spectral Data 10th edition (John Wiley & Sons, Hoboken, New Jersey). Compounds were quantified by calculating their retention indices as follows;
(RtxെRtcv)
(RtcnെRtcv) × 100 + RIcv
Where Rtx = retention time of the compound, Rtcv = retention time of the n-alkane before x, Rtcn = retention time of n-alkane after x, RIcv = the retention index of the n-alkane before x.
Further identification of metabolites were done using the Golm Metabolome Database (GMD, http://gmd.mpimp-golm.mpg.de/). Compounds were quantified if they appeared in at least three of five plant samples.
6.3.3.4 Confirmation of compounds using pure samples
ĐŽŶĐĞŶƚƌĂƚŝŽŶ ŽĨ Ϭ͘Ϯ ŵŐ ʅ>-1 solution of commercially available reference compounds was
ƉƌĞƉĂƌĞĚďLJĚŝƐƐŽůǀŝŶŐƚŚĞŵŝŶŵĞƚŚĂŶŽů͘ϲϬʅ>ŽĨĞĂĐŚƐĂŵƉůĞǁĂƐƚƌĂŶƐĨĞƌƌĞĚŝŶƚŽƉƉĞŶĚŽƌĨ
tubes and dried in the Speed Vac at 30Ԩ ĨŽƌϵϬŵŝŶ͘ƚŽƚĂůŽĨϰϬʅ>ŽĨĨƌĞƐŚŵĞƚŚŽdžLJĂŵŝŶĞǁĂƐ
added to samples with a syringe. The mixture was vortexed and put in a hot-water bath at 37Ԩ for
ϵϬ ŵŝŶ͘ ƚŽƚĂů ŽĨ ϰϬ ʅ> ŽĨ ^d& ƌĞĂŐĞŶƚ ;E͘ K-Bis (trimethylsilyl) trifluoroacetamide with trimethylchlorosilane) was added, vortexed and put in the hot-water bath at 37Ԩ for 30 min, centrifuged at 20817 G for 5 sec, and the supernatant transferred into glass vials suitable for GC- MS analysis. Samples were run using the same conditions as above. Peaks of the chromatogram of pure samples were then compared with the peaks and mass spectra of identified plant compounds.
Statistical analyses
Differences between means for the variables Si, C, C/N ratio, ADF and NDF were determined by ANOVA, assuming normality of distribution and homogeneity of variance using Genstat 64-bit Release 18.1, VSN International Ltd. The significant differences between means was tested with Tukey’s Honestly Significant Difference test (HSD) at the significance level of P = 0.05.
Multivariate analyses using MVSP 3 (Kovach Computing Services, UK) of the data set was applied as a second, complementary approach to view how well the exotic and native host plants were separated. Hierarchical cluster analysis (HCA) was used to identify similarities in metabolite profiles among host’s plants, while PCA was used to explore hidden patterns among host plants where relationships between metabolite and grouping were still unclear. PCA uses a n-dimensional vector approach to separate samples by the cumulative correlation of all metabolite data. This then identifies the vector that yields the greatest separation between samples.
A one-way multivariate analysis of variance (MANOVA) was conducted to test for differences in the groups of chemical compounds in the host plants using Genstat. The significant differences between means was tested with Tukey’s Honestly Significant Difference test (HSD) at the significance level of P = 0.05.