Nuclear magnetic resonance and liquid chromatography-mass spectrometry combined with an incompleted separation strategy for identifying the natural products in crude extract

Opening the Random Forest Black Box of 1H NMR Metabolomics Data by the Exploitation of Surrogate Variables

The untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.

Identification of metabolites from complex mixtures by 3D correlation of 1H NMR, MS and LC data using the SCORE-metabolite-ID approach

Not only in metabolomics studies, but also in natural product chemistry, reliable identification of metabolites usually requires laborious steps of isolation and purification and remains a bottleneck in many studies. Direct metabolite identification from a complex mixture without individual isolation is therefore a preferred approach, but due to the large number of metabolites present in natural products, this approach is often hampered by signal overlap in the respective 1H NMR spectra. This paper presents a method for the three-dimensional mathematical correlation of NMR with MS data over the third dimension of the time course of a chromatographic fractionation. The MATLAB application SCORE-metabolite-ID (Semi-automatic COrrelation analysis for REliable metabolite IDentification) provides semi-automatic detection of correlated NMR and MS data, allowing NMR signals to be related to associated mass-to-charge ratios from ESI mass spectra. This approach enables fast and reliable dereplication of known metabolites and facilitates the dynamic analysis for the identification of unknown compounds in any complex mixture. The strategy was validated using an artificial mixture and further tested on a polar extract of a pine nut sample. Straightforward identification of 40 metabolites could be shown, including the identification of β-D-glucopyranosyl-1-N-indole-3-acetyl-N-L-aspartic acid (1) and Nα-(2-hydroxy-2-carboxymethylsuccinyl)-L-arginine (2), the latter being identified in a food sample for the first time.

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.

Translational Metabolomics: Current Challenges and Future Opportunities

Metabolomics is one of the latest omics technologies that has been applied successfully in many areas of life sciences. Despite being relatively new, a plethora of publications over the years have exploited the opportunities provided through this data and question driven approach. Most importantly, metabolomics studies have produced great breakthroughs in biomarker discovery, identification of novel metabolites and more detailed characterisation of biological pathways in many organisms. However, translation of the research outcomes into clinical tests and user-friendly interfaces has been hindered due to many factors, some of which have been outlined hereafter. This position paper is the summary of discussion on translational metabolomics undertaken during a peer session of the Australian and New Zealand Metabolomics Conference (ANZMET 2018) held in Auckland, New Zealand. Here, we discuss some of the key areas in translational metabolomics including existing challenges and suggested solutions, as well as how to expand the clinical and industrial application of metabolomics. In addition, we share our perspective on how full translational capability of metabolomics research can be explored.

Targeted isolation and identification of bioactive compounds lowering cholesterol in the crude extracts of crabapples using UPLC-DAD-MS-SPE/NMR based on pharmacology-guided PLS-DA

The anti-hyperlipidemic effects of crude crabapple extracts derived from Malus 'Red jade', Malus hupehensis (Pamp.) Rehd. and Malus prunifolia (Willd.) Borkh. were evaluated on high-fat diet induced obese (HF DIO) mice. The results revealed that some of these extracts could lower serum cholesterol levels in HF DIO mice. The same extracts were also parallelly analyzed by LC-MS in both positive and negative ionization modes. Based on the pharmacological results, 22 LC-MS variables were identified to be correlated with the anti-hyperlipidemic effects using partial least square discriminant analysis (PLS-DA) and independent samples t-test. Further, under the guidance of the bioactivity-correlated LC-MS signals, 10 compounds were targetedly isolated and enriched using UPLC-DAD-MS-SPE and identified/elucidated by NMR together with MS/MS as citric acid(1), p-coumaric acid(2), hyperoside(3), myricetin(4), naringenin(5), quercetin(6), kaempferol(7), gentiopicroside(8), ursolic acid(9) and 8-epiloganic acid(10). Among these 10 compounds, 6 compounds, hyperoside(3), myricetin(4), naringenin(5), quercetin(6), kaempferol(7) and ursolic acid(9), were individually studied and reported to indeed have effects on lowering the serum lipid levels. These results demonstrated the efficiency of this strategy for drug discovery. In contrast to traditional routes to discover bioactive compounds in the plant extracts, targeted isolation and identification of bioactive compounds in the crude plant extracts using UPLC-DAD-MS-SPE/NMR based on pharmacology-guided PLS-DA of LC-MS data brings forward a new efficient dereplicated approach to natural products research for drug discovery.

Determination of monomeric composition in polyhydroxyalkanoates by liquid chromatography coupled with on-line mass spectrometry and off-line nuclear magnetic resonance

Polyhydroxyalkanoates (PHAs) are commercially-valuable biocompatible and biodegradable polymers with many potential medical, pharmaceutical and other industrial applications. The analysis of PHA monomeric composition is especially challenging due to the broad chemical diversity of PHA monomers and lack of analytical standards to represent the chemically-diverse PHA monomer constituents. In this study, a novel strategy based on on-line liquid chromatography-mass spectrometry (LC-MS) and off-line liquid chromatography-nuclear magnetic resonance (LC-NMR) was established to quantify seven PHA monomers with available standards and used to elucidate the structures of unknown PHA monomers. The strategy was successfully applied for the determination of monomeric composition in bacterial PHAs isolated from Pseudomonads cultivated on different carbon sources after hydrolysis. The results of this work demonstrated that the newly-developed strategy was efficient, repeatable, and could have good potential to be employed for detailed analysis of PHA monomeric composition.

LC-MS/MS Identification of a Bromelain Peptide Biomarker from Ananas comosus Merr

Bromelain (Br) is a cysteine peptidase (GenBank AEH26024.1) from pineapple, with over 40 years of clinical use. The constituents mediating its anti-inflammatory activity are not thoroughly characterized and no peptide biomarker exists. Our objective is to characterize Br raw material and identify peptides in the plasma of Br treated mice. After SDS-PAGE in-gel digestion, Br (VN#3507; Middletown, CT, USA) peptides were analyzed via LC/MS/MS using 95% protein probability, 95% peptide probability, and a minimum peptide number = 5. Br spiked mouse plasma (1 ug/ul) and plasma from i.p. treated mice (12 mg/kg) were assessed using SRM. In Br raw material, we identified seven proteins: four proteases, one jacalin-like lectin, and two protease inhibitors. In Br spiked mouse plasma, six proteins (ananain, bromelain inhibitor, cysteine proteinase AN11, FB1035 precursor, FBSB precursor, and jacalin-like lectin) were identified. Using LC/MS/MS, we identified the unique peptide, DYGAVNEVK, derived from FB1035, in the plasma of i.p. Br treated mice. The spectral count of this peptide peaked at 6 hrs and was undetectable by 24 hrs. In this study, a novel Br peptide was identified in the plasma of treated mice for the first time. This Br peptide could serve as a biomarker to standardize the therapeutic dose and maximize clinical utility.

Simultaneous structural identification of natural products in fractions of crude extract of the rare endangered plant Anoectochilus roxburghii using H NMR/RRLC-MS parallel dynamic spectroscopy

Nuclear magnetic resonance/liquid chromatography-mass spectroscopy parallel dynamic spectroscopy (NMR/LC-MS PDS) is a method aimed at the simultaneous structural identification of natural products in complex mixtures. In this study, the method is illustrated with respect to ¹H NMR and rapid resolution liquid chromatography-mass spectroscopy (RRLC-MS) data, acquired from the crude extract of Anoectochilus roxburghii, which was separated into a series of fractions with the concentration of constituent dynamic variation using reversed-phase preparative chromatography. Through fraction ranges and intensity changing profiles in ¹H NMR/RRLC–MS PDS spectrum, ¹H NMR and the extracted ion chromatogram (XIC) signals deriving from the same individual constituent, were correlated due to the signal amplitude co-variation resulting from the concentration variation of constituents in a series of incompletely separated fractions. 1H NMR/RRLC-MS PDS was then successfully used to identify three types of natural products, including eight flavonoids, four organic acids and p-hydroxybenzaldehyde, five of which have not previously been reported in Anoectochilus roxburghii. In addition, two groups of co-eluted compounds were successfully identified. The results prove that this approach should be of benefit in the unequivocal structural determination of a variety of classes of compounds from extremely complex mixtures, such as herbs and biological samples, which will lead to improved efficiency in the identification of new potential lead compounds.

Separation of patuletin-3-O-glucoside, astragalin, quercetin, kaempferol and isorhamnetin from Flaveria bidentis (L.) Kuntze by elution-pump-out high-performance counter-current chromatography

Flaveria bidentis (L.) Kuntze is an annual alien weed of Flaveria Juss. (Asteraceae) in China. Bioactive compounds, mainly flavonol glycosides and flavones from F. bidentis (L.) Kuntze, have been studied in order to utilize this invasive weed, Analytical high-performance counter-current chromatography (HPCCC) was successfully used to separate patuletin-3-O-glucoside, a mixture of hyperoside (quercetin-3-O-galactoside) and 6-methoxykaempferol-3-O-galactoside, astragalin, quercetin, kaempferol and isorhamnetin using two runs with different solvent system. Ethyl acetate-methanol-water (10:1:10, v/v) was selected by analytical HPCCC as the optimum phase system for the separation of patuletin-3-O-glucoside, a mixture of hyperoside and 6-methoxykaempferol-3-O-galactoside, and astragalin. A Dichloromethane-methanol-water (5:3:2, v/v) was used for the separation of quercetin, kaempferol and isorhamnetin. The separation was then scaled up: the crude extract (ca 1.5 g) was separated by preparative HPCCC, yielding 12 mg of patuletin-3-O-glucoside at a purity of 98.3%, yielding 9 mg of a mixture of hyperoside and 6-methoxykaempferol-3-O-galactoside constituting over 98% of the fraction, and 16 mg of astragalin (kaempferol-3-O-glucoside) at a purity of over 99%. The pump-out peaks are isorhanetin (98% purity), kaemferol (93% purity) and quercitin (99% purity). The chemical structure of patuletin-3-O-glucoside and astragalin were confirmed by MS and ¹H, ¹³C NMR.