Short Lecture “Integrated ¹Η-ΝΜR and LC-HRMS based metabolomics for the discovery of Pistacia lentiscus L. var. Chia leaves biomarkers”

Metabolomics by means of fingerprinting or metabolite profiling is an emerging field of study in natural products, where small molecules and especially secondary metabolites are observed and correlated with specific responses to different environmental stimuli, either natural or human made. Especially the untargeted metabolite profiling targets the whole metabolome of a biological system, e.g. a plant organism, towards the identification of relevant features and, finally, biomarkers. In this context, the present work focuses on the comparison of two widely used analytical platforms, ΝΜR and LC-HRMS, for the metabolite profiling of Pistacia lentiscus L. var. Chia leaves. The leaves are an underrated part of the mastic tree, an endemic plant of Greece widely known for its resin. More than ninety leaves samples were collected from four different areas of the “Mastichohoria” region, in different collection periods and growth stages. The two techniques were compared and combined using multivariate analysis (MVA) for the first time in Pistacia lentiscus var. Chia leaves. Novel statistical tools, Statistical Total Correlation Spectroscopy (STOCSY) [1] and Statistical HeterospectroscopY (SHY) [2] were also employed and correlated for dereplication processes. Advantages and pitfalls of each technique were underlined, making evident the complementarity of the two platforms. Lastly, certain biomarkers responsible for the classification of different subregions or branch age were identified.

Funding ERDF, “RESEARCH–CREATE–INNOVATE”, Hyper-Mastic (project code Τ2ΕΔΚ-00547)

 

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Conflict of Interest

The authors declare no conflict of interest.

• References

• 1 Beteinakis S, Papachristodoulou A, Kolb P. et al. NMR-Based Metabolite Profiling and the Application of STOCSY toward the Quality and Authentication Assessment of European EVOOs. Molecules 2023; 28(4)
• 2 Crockford DJ, Holmes E, Lindon JC. et al. Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: Application in metabonomic toxicology studies. Anal Chem 2006; 78: 363-371

Publication History

Article published online:
16 November 2023