Statistical Correlations between HPLC Activity-Based Profiling Results and NMR/MS Microfraction Data to Deconvolute Bioactive Compounds in Mixtures

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

NMR-Based Chromatography Readouts: Indispensable Tools to “Translate” Analytical Features into Molecular Structures

Gaining structural information is a must to allow the unequivocal structural characterization of analytes from natural sources. In liquid state, NMR spectroscopy is almost the only possible alternative to HPLC-MS and hyphenating the effluent of an analyte separation device to the probe head of an NMR spectrometer has therefore been pursued for more than three decades. The purpose of this review article was to demonstrate that, while it is possible to use mass spectrometry and similar methods to differentiate, group, and often assign the differentiating variables to entities that can be recognized as single molecules, the structural characterization of these putative biomarkers usually requires the use of NMR spectroscopy.

LC-MS/MS metabolomics-facilitated identification of the active compounds responsible for anti-allergic activity of the ethanol extract of Xenostegia tridentata

In vivo and in vitro anti-allergic activities of ethanol extract of Xenostegia tridentata (L.) D.F. Austin & Staples were investigated using passive cutaneous anaphylaxis reaction assay and RBL-2H3 cell degranulation assay, respectively. The crude ethanol extract exhibited promising activities when compared with the known anti-allergic agents, namely dexamethasone and ketotifen fumarate. The ethyl acetate subfraction showed the highest anti-allergic activity among various sub-partitions and showed better activity than the crude extract, consistent with the high abundance of total phenolic and flavonoid contents in this subfraction. LC-MS/MS metabolomics analysis and bioassay-guided isolation were then used to identify chemical constituents responsible for the anti-allergic activity. The results showed that major components of the ethyl acetate subfraction consist of 3,5-dicaffeoylquinic acid, quercetin-3-O-rhamnoside, kaempferol-3-O-rhamnoside and luteolin-7-O-glucoside. The inhibitory activity of the isolated compounds against mast cell degranulation was validated, ensuring their important roles in the anti-allergic activity of the plant. Notably, besides showing the anti-allergic activity of X. tridentata, this work highlights the role of metabolomic analysis in identifying and selectively isolating active metabolites from plants.

Thai Native Chicken as a Potential Functional Meat Source Rich in Anserine, Anserine/Carnosine, and Antioxidant Substances

This study identified anserine and anserine/carnosine in chicken breast of Thai native chicken (TNC; 100% Thai native), Thai synthetic chicken (TSC; 50% Thai native), and Thai native crossbred chicken (TNC crossbred; 25% Thai native) compared with commercial broiler chicken (BR; 0% Thai native) using nuclear magnetic resonance (NMR) spectroscopy and the effect on antioxidant activity using 2,2-diphenyl-1-picrylhydrazyl assay (DPPH). We conducted experiments with a completely randomized design and explored principal components analysis (PCA) and orthogonal projection to latent structure-discriminant analysis (OPLS-DA) to identify the distinguishing metabolites and relative concentrations from 1H NMR spectra among the groups. The relative concentrations and antioxidant properties among the groups were analyzed by analysis of variance (ANOVA) using the general linear model (GLM). This study revealed seven metabolites alanine, inositol monophosphate (IMP), inosine, and anserine/carnosine, lactate, anserine, and creatine. Lactate, anserine, and creatine were major components. In terms of PCA, the plots can distinguish BR from other groups. OPLS-DA revealed that anserine and anserine/carnosine in the chicken breast were significantly higher in TNC, TSC, and TNC crossbred than BR according to their relative concentrations and antioxidant properties (p < 0.01). Therefore, TNCs and their crossbreeds might have the potential to be functional meat sources.

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

Covering: 2013 to 2019 The exploration of the chemical diversity of extracts from various biological sources has led to major drug discoveries. Over the past two decades, despite the introduction of advanced methodologies for natural product (NP) research (e.g., dereplication and high content screening), successful accounts of the validation of NPs as lead therapeutic candidates have been limited. In this context, one of the main challenges faced is related to working with crude natural extracts because of their complex composition and the inadequacies of classical bioguided isolation studies given the pace of high-throughput screening campaigns. In line with the development of metabolomics, genomics and chemometrics, significant advances in metabolite profiling have been achieved and have generated high-quality massive genome and metabolome data on natural extracts. The unambiguous identification of each individual NP in an extract using generic methods remains challenging. However, the establishment of structural links among NPs via molecular network analysis and the determination of common features of extract composition have provided invaluable information to the scientific community. In this context, new multi-informational-based profiling approaches integrating taxonomic and/or bioactivity data can hold promise for the discovery and development of new bioactive compounds and return NPs back to an exciting era of development. In this article, we examine recent studies that have the potential to improve the efficiency of NP prioritisation and to accelerate the targeted isolation of key NPs. Perspectives on the field's evolution are discussed.

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

Chemometric methods and correlation of spectroscopic or spectrometric data with bioactivity results are known to improve dereplication in classical bio-guided isolation approaches. However, in drug discovery from natural sources the isolation of bioactive constituents from a crude extract containing close structural analogues remains a significant challenge. This study is a ¹H NMR-MS workflow named ELINA (Eliciting Nature’s Activities) which is based on statistical heterocovariance analysis (HetCA) of ¹H NMR spectra detecting chemical features that are positively (“hot”) or negatively (“cold”) correlated with bioactivity prior to any isolation. ELINA is exemplified in the discovery of steroid sulfatase (STS) inhibiting lanostane triterpenes (LTTs) from a complex extract of the polypore fungus Fomitopsis pinicola.

Bioactivity-Based Molecular Networking for the Discovery of Drug Leads in Natural Product Bioassay-Guided Fractionation

It is a common problem in natural product therapeutic lead discovery programs that despite good bioassay results in the initial extract, the active compound(s) may not be isolated during subsequent bioassay-guided purification. Herein, we present the concept of bioactive molecular networking to find candidate active molecules directly from fractionated bioactive extracts. By employing tandem mass spectrometry, it is possible to accelerate the dereplication of molecules using molecular networking prior to subsequent isolation of the compounds, and it is also possible to expose potentially bioactive molecules using bioactivity score prediction. Indeed, bioactivity score prediction can be calculated with the relative abundance of a molecule in fractions and the bioactivity level of each fraction. For that reason, we have developed a bioinformatic workflow able to map bioactivity score in molecular networks and applied it for discovery of antiviral compounds from a previously investigated extract of Euphorbia dendroides where the bioactive candidate molecules were not discovered following a classical bioassay-guided fractionation procedure. It can be expected that this approach will be implemented as a systematic strategy, not only in current and future bioactive lead discovery from natural extract collections but also for the reinvestigation of the untapped reservoir of bioactive analogues in previous bioassay-guided fractionation efforts.