A new approach to graphical and numerical analysis of links between plant chemotaxonomy and secondary metabolism from HPLC data smoothed by a simplex mixture design

Abstract

HPLC analysis of secondary metabolites represents an efficient tool for the studying of plant chemical diversity under different aspects: chemotaxonomy, metabolomics, adaptative responses to ecological factors, etc. Statistical analyses of HPLC databases, e.g. correlation analysis between HPLC peaks, can reliably provide information on the similarity/dissimilarity degrees between the chemical compounds. The similarities, corresponding to positive correlations, can be interpreted in terms of analogies between chemical structures, synchronic metabolisms or co-evolution of two compounds under certain environment conditions, etc. . In terms of metabolism, positive correlations can translate precursor-product relationships between compounds; negative correlations can be indicative of competitive processes between two compounds for a common precursor(s), enzyme(s) or substrate(s). Furthermore, the correlation analysis under a metabolic aspect can help to understand the biochemical origins of an observed polymorphism in a plant species. With the aim of showing this, we present a new approach based on a simplex mixture design, Scheffé matrix, which provides a correlation network making it possible to graphically visualise and to numerically model the metabolic trends between HPLC peaks. The principle of the approach consisted in mixing individual HPLC profiles representative of different phenotypes, then from a complete mixture set, a series of average profiles were calculated to provide a new database with a small variability. Several iterations of the mixture design provided a smoothed final database from which the relationships between the secondary metabolites were graphically and numerically analysed. These relationships were scale-dependent, namely either deterministic or systematic: the first consisted of a monotonic global trend covering the whole variation field of each metabolites' pair; the second consisted of repetitive monotonic variations which gradually attenuated or intensified along a global trend. This new metabolomic approach was illustrated from 404 individual plants of Astragalus caprinus (Leguminoseae), belonging to four chemical phenotypes (chemotypes) on the basis of flavonoids analysed in their leaves. After smoothing, the relationships between flavonoids were numerically fitted using linear or polynomial models; therefore the co-response coefficients were easily interpreted in terms of metabolic affinities or competitions between flavonoids which would be responsible of the observed chemical polymorphism (the four chemotypes). The statistical validation of the approach was carried out by comparing Pearson correlations to Spearman correlations calculated from the smoothed and the crude HPLC database, respectively. Moreover, the signs of the smoothed relationships were finely supported by analogies and differences between the chemical structures of flavonoids, leading to fluent interpretation in relation to the pathway architecture.

Metabotype Concept: Flexibility, Usefulness and Meaning in Different Biological Populations

“…Correlation analyses between the relative levels of the 14 flavonoids showed positive trends between flavonoids based on a same aglycone and negative trends between flavonoids having different aglycones (kaempferol, quercetin and methylated aglycones) (Semmar et al, 2007). This compatibility between statistical correlations and chemical structures helped to analyse the network of metabolic links between flavonol glycosides (Fig.…”

Mathematical Methods to Analysis of Topology, Functional Variability and Evolution of Metabolic Systems Based on Different Decomposition Concepts

“…-Two structurally neighbour metabolites benefiting from the over-expression of a same regulation enzyme. Negative correlation between regulation ratios of two metabolites can be indicative of : -Two metabolites belonging to two competitive metabolic pathways [22,24]; -Two metabolites strongly shifted in time (e.g. the one with short and the other with long turnover) [24].…”

“…It results in a large metabolomic dataset (n profiles p metabolites) the variability of which is not restricted to a pair of metabolites but due to mutual interactions between all the metabolites at once. In addition, a relationship between two given metabolites can be more complex than a single monotonous trend: it can include both positive and negative correlations which are observed separately in different subsets of profiles representing different genetic, physiologic or environmentdepending sub-populations (states) of the metabolic system [22,24,26]. Also, two metabolites can be indirectly positively linked through their shared opposition (negative correlation) to a same third metabolite.…”

“…Apart from quantitative and qualitative variations in metabolites, analysis of the complexity of metabolic system requires determination of metabolic pathways, their interconnections, resulting metabolic profiles, kinetic analysis of metabolic processes, etc [15][16][17][18][19]. Statistical analysis of these different components helps to understand the organization, functional processes and evolution of metabolic systems [11,[20][21][22][23][24][25]. The decomposition of a metabolic system into different components can be undertaken by reference to four concepts, while other system conceptions are possible ( Fig.…”

“…Such MbTrs can be considered as chemical phenotypes (or chemotypes) resulting from different expression degrees of metabolic pathways in the metabolic network [21,22,[35][36][37][38][39][40][41][42][43]. Their identification in a biological population can be highlighted by ordination and cluster analyses of the metabolomic dataset.…”