Heterocovariance Based Metabolomics as a Powerful Tool Accelerating Bioactive Natural Product Identification

Abstract

The discovery of new active natural products is hampered by laborious purification processes that often end up to the re-isolation of known compounds. We demonstrate here that, spectral data reflecting concentration fluctuations of components can correlate statistically with measurable dose-dependent properties on the basis of a Heterocovariance approach deconvoluting the active components structure. Variance of extract constituents was achieved through statistically meaningful collections of plants from different families, genus, and species. This fluctuation was also obtained through the fractionation of a single plant extract by separation techniques. The NMR and HRMS spectra of the extracts and fractions were recorded, as well as their ability to inhibit tyrosinase or 5-lipoxygenase enzymes. Biological activity was statistically correlated with spectral data deciphering the active compounds through the Heterocovariance approach prior to any purification. Natural products (NPs) possess significant and well-documented biological properties representing a bioactivity hotspot in the chemical space. [1] However, the great majority of existing chemodiversity remains still unexplored as only a minute fraction of biodiversity has been chemically characterised. [2] The complexity of plant extracts in terms of number, concentration levels and nature of contained metabolites renders this goal rather challenging. Traditional isolation and scale-up proce-dures are inefficient and often become a bottleneck resulting to the constant re-isolation and re-identification of known compounds. Thus, dereplication methods have been emerged aiming to accelerate and rationalize the entire discovery process. [3] Recently, there is a considerable interest by the NP scientific community to adopt metabolomics approaches introducing a more holistic concept in the analysis of complex mixtures. [4][5][6] Metabolomics have found several applications in the area of NPs by means of metabolic profiling and fingerprinting. [7][8][9] They have been successfully employed for chemotaxonomic studies exploring the proximity of different plant species, origins or organs in respect to their metabolite profile. [10] Interestingly, different metabolomics studies have contributed significantly in the quality assessment of botanical preparations enabling the monitoring of the metabolome composition. [11,12] Metabolomics studies are based on the combined application of spectroscopic techniques and multivariate statistical approaches. [13] Among these lines, specific statistical methods for the analysis of the complex spectroscopic data were developed in order to identify components of a complex mixture. The statistical total correlation spectroscopy (STOCSY) [14] can detect multiple NMR peaks from the same molecule based on their intensities multi-colinearity in a set of NMR spectra. Moreover, correlation can be achieved between MS and NMR data in statistical heterospectroscopy (SHY). [15] It has been reported recently a new approach based on MS and activit...

Innovative omics-based approaches for prioritisation and targeted isolation of natural products – new strategies for drug discovery

“…52 In this respect, heterocovariance (HetCA)-based metabolomics have been found to serve as a powerful tool. 53 In a proof of concept study, the variance of extract constituents was rst assessed in plants of different families and correlations with tyrosinase or 5-lipoxygenase enzymes inhibition were calculated. 53 Correlations between spectral features and activity levels were, for example, examined by tracking the efficiency of a single species to identify the structural characteristics of bioactive components.…”

“…53 In a proof of concept study, the variance of extract constituents was rst assessed in plants of different families and correlations with tyrosinase or 5-lipoxygenase enzymes inhibition were calculated. 53 Correlations between spectral features and activity levels were, for example, examined by tracking the efficiency of a single species to identify the structural characteristics of bioactive components. In this case, the covariance between NMR and activity data was plotted for the most active plant species.…”

“…54 The integration of HetCA STOCSY and SHY allows for the identication of stilbene derivatives as active tyrosinase inhibitors in Morus alba without the need for full purication. 53 Based on a similar premise, SHY MS-NMR correlations have been established between features of micro-fractions obtained by preparative-HPLC capillary 1 H-NMR (CapNMR) and LC-MS proles associated with a cancer chemo-preventive assay successfully deconvoluting bioactive compounds as their reconstituted pseudo 1 H NMR spectra. 55 Several approaches also rely on LC-MS-based proling for plant extract prioritisation.…”

1H NMR-MS-based heterocovariance as a drug discovery tool for fishing bioactive compounds out of a complex mixture of structural analogues

“…However recently, a paradigm shift for discovery-oriented NP chemistry has been introduced with an approach based on statistical heterocovariance analysis (HetCA) 22 . This method combines chemical and biological data of microfractions obtained after a single fractionation step performed with a bioactive crude extract.…”

Mass spectrometry-based metabolomics approach in the isolation of bioactive natural products

“…Patridge and co-workers 4 analyzed FDA-approved drugs from natural sources and observed a declining trend in the contribution of natural products to new molecular entities, especially from plant sources. As the biodiversity remains largely unexplored, the classical NP isolation process often involves the isolation of known compounds 5,6 . To overcome this discovery bottleneck, the association of analytical tools with computational and statistical treatments, known as "omics" tools, constitutes a powerful ally for natural product chemists.…”