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A Metabolomic Approach to Target Compounds from the Asteraceae Family for Dual COX and LOX Inhibition.

Chagas-Paula DA, Zhang T, Da Costa FB, Edrada-Ebel R - Metabolites (2015)

Bottom Line: The O2PLS-DA results exhibited good validation values (cross-validation = Q2 > 0.7 and external validation = P2 > 0.6) with 0% of false positive predictions.The metabolomic approach determined biomarkers for the required biological activity and detected active compounds in the extracts displaying unique mechanisms of action.In addition, the PCA data also gave insights on the chemotaxonomy of the family Asteraceae across its diverse range of genera and tribes.

View Article: PubMed Central - PubMed

Affiliation: University of Strathclyde, the John Arbuthnott Building, 161 Cathedral Street, Glasgow G4 0RE, UK. febcosta@fcfrp.usp.br.

ABSTRACT
The application of metabolomics in phytochemical analysis is an innovative strategy for targeting active compounds from a complex plant extract. Species of the Asteraceae family are well-known to exhibit potent anti-inflammatory (AI) activity. Dual inhibition of the enzymes COX-1 and 5-LOX is essential for the treatment of several inflammatory diseases, but there is not much investigation reported in the literature for natural products. In this study, 57 leaf extracts (EtOH-H2O 7:3, v/v) from different genera and species of the Asteraceae family were tested against COX-1 and 5-LOX while HPLC-ESI-HRMS analysis of the extracts indicated high diversity in their chemical compositions. Using O2PLS-DA (R2 > 0.92; VIP > 1 and positive Y-correlation values), dual inhibition potential of low-abundance metabolites was determined. The O2PLS-DA results exhibited good validation values (cross-validation = Q2 > 0.7 and external validation = P2 > 0.6) with 0% of false positive predictions. The metabolomic approach determined biomarkers for the required biological activity and detected active compounds in the extracts displaying unique mechanisms of action. In addition, the PCA data also gave insights on the chemotaxonomy of the family Asteraceae across its diverse range of genera and tribes.

No MeSH data available.


Related in: MedlinePlus

Hierarchical Cluster Analysis (HCA) dendrogram for Asteraceae plant extracts analyzed in this study (n = 66, included root, stem and flower parts for some species). Highlighted (green and yellow) numbers represent sample codes of species-related extracts that clustered together in the PCA according to their similarity in the chemistry of their metabolomes. Red highlighted sample codes represent extracts with unique chemistry when compared with other extracts within their respective taxa.
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metabolites-05-00404-f003: Hierarchical Cluster Analysis (HCA) dendrogram for Asteraceae plant extracts analyzed in this study (n = 66, included root, stem and flower parts for some species). Highlighted (green and yellow) numbers represent sample codes of species-related extracts that clustered together in the PCA according to their similarity in the chemistry of their metabolomes. Red highlighted sample codes represent extracts with unique chemistry when compared with other extracts within their respective taxa.

Mentions: PCA of the HRMS data (Figure 2) that was pre-processed both by MZmine, SIMCA-P + 12.0® and Sieve®, disclosed the grouping of the samples according to their phylogenetic proximity [50,51,75]. Extracts prepared from the same genera or tribes were clustered together as illustrated by Hierarchical Cluster Analysis (HCA) of its PCA results (Figure 3). The HCA dendrogram exhibited clear groupings according to their phylogeny but not their anti-inflammatory properties. The phylogenetic grouping was not evident in the PCA scatter plot. Samples were clustered by PCA based on chemical similarity of the extracts. The PCA plots (Figure 2 and Figure S4) showed overlapping samples and groups were not well separated. Thus, it was not easy to define the difference between groups as well as the chemical similarity between respective samples. Interestingly, the HCA dendrogram exhibited significant separation between the Vernonieae and Heliantheae tribes which is comparable to the phylogenetic diagrams of the Asteraceae tribes according to Bremer [76] and Funk et al. [64], where both tribes belong to well-separated clades. The HCA results from Simca P+ was validated with Programme R (Figure S6) which even with few samples interfering on the phylogenetic tree, it was possible to see similar results. These results substantiated the use of metabolomics as a potential chemotaxonomical tool [50,51,75]. Nevertheless, unsupervised MSA by PCA did not contribute in the determination of biomarkers for dual inhibition because the samples were not grouped according to their AI properties (Figure 2). However, the trend bar graph of individual attributes (example shown in Figure 2) indicated significant intensity differences of distinct metabolites between AI property classes. This prompted us to apply a suitable supervised MSA to determine these unique biomarkers.


A Metabolomic Approach to Target Compounds from the Asteraceae Family for Dual COX and LOX Inhibition.

Chagas-Paula DA, Zhang T, Da Costa FB, Edrada-Ebel R - Metabolites (2015)

Hierarchical Cluster Analysis (HCA) dendrogram for Asteraceae plant extracts analyzed in this study (n = 66, included root, stem and flower parts for some species). Highlighted (green and yellow) numbers represent sample codes of species-related extracts that clustered together in the PCA according to their similarity in the chemistry of their metabolomes. Red highlighted sample codes represent extracts with unique chemistry when compared with other extracts within their respective taxa.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4588803&req=5

metabolites-05-00404-f003: Hierarchical Cluster Analysis (HCA) dendrogram for Asteraceae plant extracts analyzed in this study (n = 66, included root, stem and flower parts for some species). Highlighted (green and yellow) numbers represent sample codes of species-related extracts that clustered together in the PCA according to their similarity in the chemistry of their metabolomes. Red highlighted sample codes represent extracts with unique chemistry when compared with other extracts within their respective taxa.
Mentions: PCA of the HRMS data (Figure 2) that was pre-processed both by MZmine, SIMCA-P + 12.0® and Sieve®, disclosed the grouping of the samples according to their phylogenetic proximity [50,51,75]. Extracts prepared from the same genera or tribes were clustered together as illustrated by Hierarchical Cluster Analysis (HCA) of its PCA results (Figure 3). The HCA dendrogram exhibited clear groupings according to their phylogeny but not their anti-inflammatory properties. The phylogenetic grouping was not evident in the PCA scatter plot. Samples were clustered by PCA based on chemical similarity of the extracts. The PCA plots (Figure 2 and Figure S4) showed overlapping samples and groups were not well separated. Thus, it was not easy to define the difference between groups as well as the chemical similarity between respective samples. Interestingly, the HCA dendrogram exhibited significant separation between the Vernonieae and Heliantheae tribes which is comparable to the phylogenetic diagrams of the Asteraceae tribes according to Bremer [76] and Funk et al. [64], where both tribes belong to well-separated clades. The HCA results from Simca P+ was validated with Programme R (Figure S6) which even with few samples interfering on the phylogenetic tree, it was possible to see similar results. These results substantiated the use of metabolomics as a potential chemotaxonomical tool [50,51,75]. Nevertheless, unsupervised MSA by PCA did not contribute in the determination of biomarkers for dual inhibition because the samples were not grouped according to their AI properties (Figure 2). However, the trend bar graph of individual attributes (example shown in Figure 2) indicated significant intensity differences of distinct metabolites between AI property classes. This prompted us to apply a suitable supervised MSA to determine these unique biomarkers.

Bottom Line: The O2PLS-DA results exhibited good validation values (cross-validation = Q2 > 0.7 and external validation = P2 > 0.6) with 0% of false positive predictions.The metabolomic approach determined biomarkers for the required biological activity and detected active compounds in the extracts displaying unique mechanisms of action.In addition, the PCA data also gave insights on the chemotaxonomy of the family Asteraceae across its diverse range of genera and tribes.

View Article: PubMed Central - PubMed

Affiliation: University of Strathclyde, the John Arbuthnott Building, 161 Cathedral Street, Glasgow G4 0RE, UK. febcosta@fcfrp.usp.br.

ABSTRACT
The application of metabolomics in phytochemical analysis is an innovative strategy for targeting active compounds from a complex plant extract. Species of the Asteraceae family are well-known to exhibit potent anti-inflammatory (AI) activity. Dual inhibition of the enzymes COX-1 and 5-LOX is essential for the treatment of several inflammatory diseases, but there is not much investigation reported in the literature for natural products. In this study, 57 leaf extracts (EtOH-H2O 7:3, v/v) from different genera and species of the Asteraceae family were tested against COX-1 and 5-LOX while HPLC-ESI-HRMS analysis of the extracts indicated high diversity in their chemical compositions. Using O2PLS-DA (R2 > 0.92; VIP > 1 and positive Y-correlation values), dual inhibition potential of low-abundance metabolites was determined. The O2PLS-DA results exhibited good validation values (cross-validation = Q2 > 0.7 and external validation = P2 > 0.6) with 0% of false positive predictions. The metabolomic approach determined biomarkers for the required biological activity and detected active compounds in the extracts displaying unique mechanisms of action. In addition, the PCA data also gave insights on the chemotaxonomy of the family Asteraceae across its diverse range of genera and tribes.

No MeSH data available.


Related in: MedlinePlus