Barking up the right tree: characterising Garcinia buchananii extracts with Progenesis QI

It’s often said that plants are a rich source of dietary supplements, medicines, and other usefully bioactive phytochemicals. Among these, there are many traditional remedies derived from plants, but these often derive from a specific part of a plant, or historical means of preparation. How, then, to know if this is the best method of obtaining the target compounds? Are they the ‘best’ compounds that plant has to offer? How do different parts of the plant differ from each other for providing bioactive metabolites? The answers to these questions could both help to obtain better yields of such compounds, and to assess whether there is real medical benefit on offer.

Progenesis QI, which we think is a versatile piece of software, is beginning to assist this process, and it turns out that two of its strengths are key to this. Firstly, the ability to rapidly quantify and effectively identify compounds in complex metabolomes; secondly, integrated statistics that allow rapid and robust discovery of biological changes between samples.

These strengths have been brought to bear on Garcinia buchananii, the source of a traditional sub-Saharan African remedy for diaorrhea that has also been claimed to represent a rich source of antioxidants – specifically in its stem bark. However, Dr Timo Stark at Technische Universität München decided to pose several questions – was bark extract truly providing the ‘best’ antioxidant activity; if not, which part of the tree would represent the best source of bioactive antioxidant compounds; and, how did leaf, root, and stem bark extracts differ from each other in their metabolite profiles.

To do this, he analysed G. buchananii extracts from those sources comprehensively, using an Acquity UPLC – Synapt G2-S – HD-MSE Waters technology workflow. This generated a vast array of metabolite data, carrying those twin challenges of identification – always a bottleneck in metabolomics – and accurate, quantitative statistical analysis. However, with Progenesis QI, these need not be intimidating. Our quantify-then-identify co-detection approach with no missing values, multivariate statistical visualisations which can reveal subtle co-ordinated trends in data, flexible and comprehensive range of identification approaches and user-friendly OPLS-DA (discriminant analysis) using an optional integrated analytical package (EZinfo 3.0, Umetrics) combine to make complex analyses much more straightforward. Dr Stark was able to rapidly determine the organs richest in known literature-corroborated antioxidants, differentiate the profile of antioxidants and other compounds associated with each organ, and identify several antioxidant species novel to G. buchananii. In the course of one study a great deal was revealed about the bioactive profile of the plant.

As Dr Stark put it:

“With Progenesis QI we were able to analyse data in a reasonably short time that had previously proved too difficult to analyse. Progenesis is summarizing and illustrating the data, there are direct links to online databases, fragmentation tools can help to verify/identify compounds. It is straightforward.

The power and speed of Progenesis analysis means we can not only get better results from existing experiments but can also analyse larger experiments with more biological replicates to further improve quality of results. Faster hints on compound identification.”

Figure 1

Figure 1. Progenesis QI allowed the detection of antioxidant compounds enriched in particular G. buchananii tissues; in this case, (2R,3S)-morelloflavone in leaf.*

I won’t reiterate the full details of his paper and results here, as there is a better option! Dr Stark himself is presenting a webinar where he will describe his work with Garcinia buchananii and Progenesis QI, and his discoveries, on December the 9th (08:00 PST / 11:00 EST / 16:00 GMT / 17:00 CET) and I would encourage you to register for what promises to be a very interesting presentation. In preparation for that, why not have a read of his paper yourself?

Enjoy the webinar, and if you would like to hear more about how Progenesis QI can assist and improve your own metabolomics studies, please do get in touch.

* Reprinted (adapted) with permission from Figure 5 (B), “UPLC-ESI-TOF MS-Based Metabolite Profiling of the Antioxidative Food Supplement Garcinia buchananii”, Timo D. Stark, Sofie Lösch, Junichiro Wakamatsu, et al. Journal of Agricultural and Food Chemistry 63:7169-79; DOI: 10.1021/acs.jafc.5b02544. Copyright 2015 American Chemical Society.

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