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Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures.

ABSTRACT: Compounds derived from natural sources represent the majority of small-molecule drugs utilized today. Plants, owing to their complex biosynthetic pathways, are poised to synthesize diverse secondary metabolites that selectively target biological macromolecules. Despite the vast chemical landscape of botanicals, drug discovery programs from these sources have diminished due to the costly and time-consuming nature of standard practices and high rates of compound rediscovery. Untargeted metabolomics approaches that integrate biological and chemical data sets potentially enable the prediction of active constituents early in the fractionation process. However, data acquisition and data processing parameters may have major impacts on the success of models produced. Using an inactive botanical mixture spiked with known antimicrobial compounds, untargeted mass spectrometry-based metabolomics data were combined with bioactivity data to produce selectivity ratio models subjected to a variety of data acquisition and data processing parameters. Selectivity ratio models were used to identify active constituents that were intentionally added to the mixture, along with an additional antimicrobial compound, randainal (5), which was masked by the presence of antagonists in the mixture. These studies found that data-processing approaches, particularly data transformation and model simplification tools using a variance cutoff, had significant impacts on the models produced, either masking or enhancing the ability to detect active constituents in samples. The current study highlights the importance of the data processing step for obtaining reliable information from metabolomics models and demonstrates the strengths and limitations of selectivity ratio analysis to comprehensively assess complex botanical mixtures.

SUBMITTER: Caesar LK

PROVIDER: S-EPMC6837904 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Project description:Mass spectral data sets often contain experimental artefacts, and data filtering prior to statistical analysis is crucial to extract reliable information. This is particularly true in untargeted metabolomics analyses, where the analyte(s) of interest are not known a priori. It is often assumed that chemical interferents (i.e. solvent contaminants such as plasticizers) are consistent across samples, and can be removed by background subtraction from blank injections. On the contrary, it is shown here that chemical contaminants may vary in abundance across each injection, potentially leading to their misidentification as relevant sample components. With this metabolomics study, we demonstrate the effectiveness of hierarchical cluster analysis (HCA) of replicate injections (technical replicates) as a methodology to identify chemical interferents and reduce their contaminating contribution to metabolomics models. Pools of metabolites with varying complexity were prepared from the botanical Angelica keiskei Koidzumi and spiked with known metabolites. Each set of pools was analyzed in triplicate and at multiple concentrations using ultraperformance liquid chromatography coupled to mass spectrometry (UPLC-MS). Before filtering, HCA failed to cluster replicates in the data sets. To identify contaminant peaks, we developed a filtering process that evaluated the relative peak area variance of each variable within triplicate injections. These interferent peaks were found across all samples, but did not show consistent peak area from injection to injection, even when evaluating the same chemical sample. This filtering process identified 128 ions that appear to originate from the UPLC-MS system. Data sets collected for a high number of pools with comparatively simple chemical composition were highly influenced by these chemical interferents, as were samples that were analyzed at a low concentration. When chemical interferent masses were removed, technical replicates clustered in all data sets. This work highlights the importance of technical replication in mass spectrometry-based studies, and presents a new application of HCA as a tool for evaluating the effectiveness of data filtering prior to statistical analysis.

Project description:BackgroundPyridoxine-dependent epilepsy (PDE-ALDH7A1) is an inborn error of lysine catabolism that presents with refractory epilepsy in newborns. Biallelic ALDH7A1 variants lead to deficiency of α-aminoadipic semialdehyde dehydrogenase/antiquitin, resulting in accumulation of piperideine-6-carboxylate (P6C), and secondary deficiency of the important cofactor pyridoxal-5'-phosphate (PLP, active vitamin B6) through its complexation with P6C. Vitamin B6 supplementation resolves epilepsy in patients, but intellectual disability may still develop. Early diagnosis and treatment, preferably based on newborn screening, could optimize long-term clinical outcome. However, no suitable PDE-ALDH7A1 newborn screening biomarkers are currently available.MethodsWe combined the innovative analytical methods untargeted metabolomics and infrared ion spectroscopy to discover and identify biomarkers in plasma that would allow for PDE-ALDH7A1 diagnosis in newborn screening.ResultsWe identified 2S,6S-/2S,6R-oxopropylpiperidine-2-carboxylic acid (2-OPP) as a PDE-ALDH7A1 biomarker, and confirmed 6-oxopiperidine-2-carboxylic acid (6-oxoPIP) as a biomarker. The suitability of 2-OPP as a potential PDE-ALDH7A1 newborn screening biomarker in dried bloodspots was shown. Additionally, we found that 2-OPP accumulates in brain tissue of patients and Aldh7a1-knockout mice, and induced epilepsy-like behavior in a zebrafish model system.ConclusionThis study has opened the way to newborn screening for PDE-ALDH7A1. We speculate that 2-OPP may contribute to ongoing neurotoxicity, also in treated PDE-ALDH7A1 patients. As 2-OPP formation appears to increase upon ketosis, we emphasize the importance of avoiding catabolism in PDE-ALDH7A1 patients.FundingSociety for Inborn Errors of Metabolism for Netherlands and Belgium (ESN), United for Metabolic Diseases (UMD), Stofwisselkracht, Radboud University, Canadian Institutes of Health Research, Dutch Research Council (NWO), and the European Research Council (ERC).

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Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures.

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