Evaluation of Ion Mobility Spectrometry for Improving Constitutional Assignment in Natural Product Mixtures.

The comprehensive chemical characterization of biological samples remains a central challenge in the field of natural products. Conventional workflows using liquid chromatography (LC)-coupled high-resolution tandem mass spectrometry (MS/MS or MS2) allow the detection of relevant small molecules while providing diagnostic fragment ions for their structural assignment. Still, many natural product extracts are of a molecular complexity that challenges the resolving power of modern LC-MS2 pipelines. In this study, we examined the effect of integrating ion mobility spectrometry (IMS) to our LC-MS2 platform for the characterization of natural product mixtures. IMS provides an additional axis of separation in the gas phase as well as experimental collision cross-sectional (CCS) values. We analyzed a mixture of 20 commercial standards at 2 concentration ranges, either solubilized in solvent or spiked into an actinobacterial extract. Data were acquired in positive ion mode using both data-dependent acquisition (DDA) and data-independent acquisition (DIA) MS2 fragmentation approaches and assessed for both chemical coverage and spectral quality. IMS-DIA identified the largest number of standards in the spiked extract at the lower concentration of standards (17), followed by IMS-DDA (10), DDA (8), and DIA (6). In addition, we examined how these data sets performed in the Global Natural Products Social Molecular Networking (GNPS) platform. Overall, integrating IMS increased both metabolite detection and the quality of MS2 spectra, particularly for samples analyzed in DIA mode.

Effect of a Flaxseed Lignan Intervention on Circulating Bile Acids in a Placebo-Controlled Randomized, Crossover Trial.

Plant lignans and their microbial metabolites, e.g., enterolactone (ENL), may affect bile acid (BA) metabolism through interaction with hepatic receptors. We evaluated the effects of a flaxseed lignan extract (50 mg/day secoisolariciresinol diglucoside) compared to a placebo for 60 days each on plasma BA concentrations in 46 healthy men and women (20-45 years) using samples from a completed randomized, crossover intervention. Twenty BA species were measured in fasting plasma using LC-MS. ENL was measured in 24-h urines by GC-MS. We tested for (a) effects of the intervention on BA concentrations overall and stratified by ENL excretion; and (b) cross-sectional associations between plasma BA and ENL. We also explored the overlap in bacterial metabolism at the genus level and conducted in vitro anaerobic incubations of stool with lignan substrate to identify genes that are enriched in response to lignan metabolism. There were no intervention effects, overall or stratified by ENL at FDR < 0.05. In the cross-sectional analysis, irrespective of treatment, five secondary BAs were associated with ENL excretion (FDR < 0.05). In vitro analyses showed positive associations between ENL production and bacterial gene expression of the bile acid-inducible gene cluster and hydroxysteroid dehydrogenases. These data suggest overlap in community bacterial metabolism of secondary BA and ENL.

Characterization of aporphine alkaloids by electrospray ionization tandem mass spectrometry and density functional theory calculations.

RATIONALE: Aporphine alkaloids represent a large group of isoquinoline natural products with important roles in biological and biomedical areas. Their characterization by electrospray ionization tandem mass spectrometry (ESI-MS/MS) can contribute to their rapid identification in complex biological matrices. METHODS: We report the fragmentation of protonated 7,7-dimethylaporphine alkaloids by ESI-MS/MS, and the putative annotation of aporphine alkaloids in plant extracts. We used low- and high-resolution MS/MS analyses to rationalize the fragmentation pathways, and employed the B3LYP/6-31 + G(d,p) density functional theory (DFT) model to provide thermochemical parameters and to obtain the reactive sites. RESULTS: DFT calculations of a set of 7,7-dimethylaporphine alkaloids suggested the heterocyclic amino group as the most basic site due to the proton affinity of the nitrogen atom. Collision-induced dissociation experiments promoted • OCH3 elimination instead of the expected neutral loss of the heterocyclic amino group, pointing to the [M - 15 + H]•+ ion as the diagnostic fragment for 7,7-dimethylaporphine alkaloids. The analysis of plant extracts led to the annotation of 25 aporphine alkaloids. Their fragmentation initiated with the loss of the amino group followed by formation of a cyclic carbocation. Further reactions derived from consecutive charge-remote and/or charge-induced fragmentations of the substituents attached to the aromatic system. The mechanisms were re-examined based on plausible gas-phase ion chemistry reactions. CONCLUSIONS: Taken together, the diagnostic product ions and the series of radical and neutral eliminations provided information about the location of methylenedioxy, aromatic methoxy, and vicinal methoxy and hydroxy groups in aporphine alkaloids, assisting their characterization via MS/MS.

Plant Metabolomics Using NMR Spectroscopy.

The major goal in plant metabolomics is to study complex extracts for the purposes of metabolic exploration and natural products discovery. To achieve this goal, plant metabolomics relies on accurate and selective acquisition of all possible chemical information, which includes maximization of the number of detected metabolites and their correct molecular assignment. Nuclear magnetic resonance (NMR) spectroscopy has been recognized as a powerful platform for obtaining the metabolite profiles of plant extracts. In this chapter, we provide a workflow for targeted and untargeted metabolite profiling of plant extracts using both 1D and 2D NMR methods. The protocol includes sample preparation, instrument operation, data processing, multivariate analysis, biomarker elucidation, and metabolite quantitation. It also addresses the annotation of plant metabolite peaks considering NMR's capabilities to cover a broad range of metabolites and elucidate structures for unknown compounds.

Dereplication of Flavonoid Glycoconjugates from Adenocalymma imperatoris-maximilianii by Untargeted Tandem Mass Spectrometry-Based Molecular Networking.

The interpretation of large datasets acquired using high performance liquid chromatography coupled with tandem mass spectrometry represents one of the major challenges in natural products research. Here we propose the use of molecular networking to rapid identify the known secondary metabolites from untargeted MS/MS analysis of Adenocalymma imperatoris-maximilianii plant extracts. The leaves, stems and roots of A. imperatoris-maximilianii were extracted using different solvents according to Snyder selectivity triangle. The samples were analyzed by HPLC coupled with ion trap mass spectrometer in a collision-induced dissociation MS/MS configuration in both positive and negative electrospray ionization modes. Molecular networking simultaneously organized the spectra by cosine similarity. The chemical identification was performed based on the systematic study of the main fragmentation pathways observed for the resulting network. The untargeted tandem mass spectrometry-based molecular networking allowed for the identification of 63 metabolites, mainly mono-, di- and tri-, C- and/or O-glycosyl flavones. Molecular networking was capable not only to dereplicate known flavonoids, but also to point out related prenyl derivatives, described for the first time in Adenocalymma species. The gas-phase reaction route to form the characteristic [M-H2O-(30/60/90)]+ fragments in C-glycosyl flavones was suggested as sequential sugar ring opening followed by retro-aldol elimination involving aldose-ketose isomerization. The use of molecular networking with LC-CID-MS/MS assisted the identification of various isomeric and isobaric flavonoid glycoconjugates by establishing clusters according to the fragmentation similarities. Additionally, the proposed cross-ring sugar cleavages can contribute to the identification of C-glycosides by MS/MS analysis.

Dereplication of Natural Products Using GC-TOF Mass Spectrometry: Improved Metabolite Identification by Spectral Deconvolution Ratio Analysis.

Dereplication based on hyphenated techniques has been extensively applied in plant metabolomics, thereby avoiding re-isolation of known natural products. However, due to the complex nature of biological samples and their large concentration range, dereplication requires the use of chemometric tools to comprehensively extract information from the acquired data. In this work we developed a reliable GC-MS-based method for the identification of non-targeted plant metabolites by combining the Ratio Analysis of Mass Spectrometry deconvolution tool (RAMSY) with Automated Mass Spectral Deconvolution and Identification System software (AMDIS). Plants species from Solanaceae, Chrysobalanaceae and Euphorbiaceae were selected as model systems due to their molecular diversity, ethnopharmacological potential, and economical value. The samples were analyzed by GC-MS after methoximation and silylation reactions. Dereplication was initiated with the use of a factorial design of experiments to determine the best AMDIS configuration for each sample, considering linear retention indices and mass spectral data. A heuristic factor (CDF, compound detection factor) was developed and applied to the AMDIS results in order to decrease the false-positive rates. Despite the enhancement in deconvolution and peak identification, the empirical AMDIS method was not able to fully deconvolute all GC-peaks, leading to low MF values and/or missing metabolites. RAMSY was applied as a complementary deconvolution method to AMDIS to peaks exhibiting substantial overlap, resulting in recovery of low-intensity co-eluted ions. The results from this combination of optimized AMDIS with RAMSY attested to the ability of this approach as an improved dereplication method for complex biological samples such as plant extracts.

Partial least squares model and design of experiments toward the analysis of the metabolome of Jatropha gossypifolia leaves: Extraction and chromatographic fingerprint optimization.

A major challenge in metabolomic studies is how to extract and analyze an entire metabolome. So far, no single method was able to clearly complete this task in an efficient and reproducible way. In this work we proposed a sequential strategy for the extraction and chromatographic separation of metabolites from leaves Jatropha gossypifolia using a design of experiments and partial least square model. The effect of 14 different solvents on extraction process was evaluated and an optimized separation condition on liquid chromatography was estimated considering mobile phase composition and analysis time. The initial conditions of extraction using methanol and separation in 30 min between 5 and 100% water/methanol (1:1 v/v) with 0.1% of acetic acid, 20 µL sample volume, 3.0 mL min(-1) flow rate and 25°C column temperature led to 107 chromatographic peaks. After the optimization strategy using i-propanol/chloroform (1:1 v/v) for extraction, linear gradient elution of 60 min between 5 and 100% water/(acetonitrile/methanol 68:32 v/v with 0.1% of acetic acid), 30 µL sample volume, 2.0 mL min(-1) flow rate, and 30°C column temperature, we detected 140 chromatographic peaks, 30.84% more peaks compared to initial method. This is a reliable strategy using a limited number of experiments for metabolomics protocols.