1. Isonitrosoacetophenone induces metabolic perturbations in Nicotiana tabacum, Sorghum bicolor, and Arabidopsis thaliana. A holistic UPLC-ESI-HD-MS based metabolomics analysis.
Ntakadzeni Edwin Madala
Supervisor: Prof IA Dubery
Co-supervisor: Dr LA Piater
Dr PA Steenkamp

2. Background Information:
Plants have developed biochemical and molecular responses to defend themselves under different stress environments.

3. Plant defenses can be triggered by various stimuli Pathogens
Synthetic or naturally occurring molecules, especially those derived from pathogens
More recently, chemicals have also been employed to trigger SAR, the most widely used being benzothiadiazole (BTH) with trade name BION
Different promoters of genes with direct activity towards plant defense responses (such as the one of PR1a) have been shown to respond to different types of chemical inducers.
Other chemicals include :
β-aminobutyric acid (BABA)
Methyl-2,6-dichloroisonicotinic acid (INA)
Riboflavin
(Gorlachet al., 1996; Gatz, 1997 Gatz and Lenk, 1998; Oostendorpet al., 2001; Dao et al., 2009)

4. Oxime functional groups are rare in natural products.
Dubery et al. 1999, reported the accumulation of an oxime-containing stress metabolite/phytoalexin, (4-(3-methyl-2-butenoxy)-isonitrosoacetophenone or citaldoxime) in citrus peel undergoing oxidative stress due to gamma radiation treatment.
Oxime functional groups are rare in natural products.
In plants, oximes are intermediates of a range of metabolic pathways subject to controls that result in variation in both the type and amount of end product formed.
Aldoximes are intermediates during the biosynthesis of glucosinolates and cyanogenic glycosides
(Mahandevan, 1973; Møller, 2010)

5. Differences between INAP and Phenylacetaldehyde oxime
Unknown Function ?
Cyanogenic glycoside
precursor
Isonitrosoacetophenone (INAP)
Phenylacetaldehyde oxime

6. Differences between non-cyanogenic and cyanogenic plants.

7. Metabolomics.
“Identification and quantification of all metabolites in a specific issue/cell at given physiological status.”

8. Techniques and data analyses.
Techniques (NMR, GC-MS, LC-MS, IR).
High dimensional data.
In metabolomics, scientists spend most of the time analyzing data.
Chemometric/multivariate data analysis.
PCA
OPLS-DA (SUS)
PLS-DA
HCA
Metabolic trees

9. Extraction and sample preparation
Materials and Methods
Cells and treatments
Tobacco and sorghum cell suspensions were treated with INAP to final concentration of 1 mM at different time intervals (6 h, 12 h, 18 h and 24 h). Control cells were not treated.
Extraction and sample preparation
Wet cells (2 g) were homogenised in 100% methanol. Extracts were dried completely with the a speed vac at 42ºC. The dried pellet was suspended in 500 µL of 50% methanol. The extracts were filtered through 0.22 µm nylon filter membranes fitted on 1 mL syringe to remove any residual debris and transferred to UPLC vials fitted with 0.5 mL inserts and pre-slitted lids
UPLC-MS
Five (5) µL was injected on the Waters Acquity UPLC-MS instrument equipped with the BEH C18 column (100 mm × 2.1 mm with a particle size of 1.7 µm). The composition of mobile phase A consisted of 0.1% formic acid in deionized water and mobile phase B consisted of 0.1% formic acid in methanol. Data was acquired by both PDA and MS detectors, the MS detector was operated at both negative and positive ionization modes.

10. Materials and Methods Data Analysis Multivariate Data Analyses
Chromatograms were initially visually, compared to each other. MS raw data was further analyzed by MarkerLynx TM
Multivariate Data Analyses
MarkerLynx results containing peak information [Area under the peak, retention time (RT) and Mass (m/z)] were exported to SIMCA-P software for multivariate statistics analyses. Both PCA and OPLS-DA models were constructed from the exported data.
Biomarker Identification
Metabolites of which the levels were affected by INAP were identified from the PCA loadings plot and OPLS-DA loading S-plot. The mass of these biomarkers were used to predict the elemental compositions. These elemental compositions were used to search for chemical identity of these biomarkers from the dictionary of natural products (DNP) and ChemSpider databases.

11. INAP Induced metabolic changes in sorghum and tobacco cells.
Results and Discussion
INAP Induced metabolic changes in sorghum and tobacco cells.
Overlaid UPLC-PDA chromatogram (Tobacco).

12. Comparison of UPLC-MS data generated using different ionization polarity.
UPLC-MS (ESI-)
UPLC-MS (ESI+)

13. Overview of Quadrupole Time Of Flight (Q-TOF) MS

14. Changes in the collision energy affects the metabolomics data output.

15. PCA score plots for both Tobacco (A) and Sorghum(B).

16. Hierarchical Cluster Analysis (HCA) dendrograms
Tobacco
Sorghum

17. Metabolic trees visualization (For the first time the metabolic trees are used to decipher the time trend)
Sorghum
Tobacco

18. Summary of results up so far:
INAP induces metabolic changes in both sorghum and tobacco cells.
MS settings affects metabolic data output.
Different data visualization models are required for comprehensive understanding of the biological meaning underlying the exhibited response.
Metabolic trees and HCA offers an alternative to PCA score plots as they contains more statistically sounding results.
Question: Oxime response/metabolism:
Cyanogenic plants: known from literature
Non-cyanogenic plants: ????

19. Effect of INAP on tobacco cell suspensions
Effect of INAP on tobacco cell suspensions. (Identification of responsive metabolites)
Comparison of representative chromatograms [30 min, positive ionization UPLC-MS base peak intensity (BPI)] of extracts from tobacco cell suspension samples treated with INAP for different time intervals
24 h
18 h
12 h
6 h
Con

20. Representative OPLS-DA score plot, based on the UPLC-MS chromatograms, showing clustering of control vs. 6 h treatment of tobacco cell suspensions with INAP.

21. Loading S-plot showing bio-markers which are responsible for the different clustering observed in the OPLS-DA score plots, with those most significant contributing to the treatment response highlighted by red box.

22. Structures of Biomarkers of which the levels were found to increase after INAP treatment.
Gallic Acid 3 1 2
Sinapic acid 5 6 4
Biotransformed INAP
Vanillic acid
Chlorogenic acid

23. INAP was found to undergo biotransformation in tobacco cells
As seen from other INAP induced metabolites, they were also methoxylated and glycosylated, which justify the biotransformation steps proposed above.
Madala et al., Biotechnology Letters. DOI: /s

24. Conclusion
The induced metabolites have known antioxidant activities which in turn explain the initial accumulation of INAP in citrus peel undergoing oxidative stress.
INAP induced metabolic perturbations in tobacco cell suspensions. (i) It was metabolised through a series of hydroxylation and methoxylation steps and (ii) triggered the synthesis of benzoic acid derivatives that could create an enhanced defensive capacity
The use UPLC-MS based metabolomics and multivariate data analysis suffice the understanding of metabolic perturbations induced by chemical inducers.

25. Thanks to All! Ackwoledgements Prof IA Dubery Dr LA Piater
Dr PA Steenkamp
Mr MJ George
F Tugizimana
T Finnegan
The Plant Research Group.
Dr William Allwood and Andrew Vaughan.
NRF and University of Johnnesburg
Thanks to All!