Identification of Unknown Metabolomics Mixture Compounds by Combining NMR, MS, and Cheminformatics

Leggett, Abigail; Wang, Cheng; Li, Dawei; Somogyi, Árpád; Bruschweiler‐Li, Lei; Brüschweiler, Rafael

doi:10.1016/bs.mie.2018.09.003

Cited by 26 publications

(25 citation statements)

References 28 publications

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“…Interestingly, corresponding outputs only partially overlap in terms of detected metabolites, and both technologies that offer complementary advantages and disadvantages [ 37 ] have proven to deliver robust results for metabolomics studies. Recent advances tend to associate MS and NMR through integrated studies to maximize the sensitivity and precision of metabolomics studies [ 34 , 38 , 39 ]. The information-rich datasets generated from NMR or MS are subjected to computational and statistical analyses to identify event-related metabolite changes and potential biomarkers [ 40 ].…”

Section: Metabolic Phenotyping Of Cell Culture Metabolism: Analytimentioning

confidence: 99%

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Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

Balcerczyk

Damblon

Elena-Herrmann

et al. 2020

IJMS

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Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.

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Section: Metabolic Phenotyping Of Cell Culture Metabolism: Analytimentioning

confidence: 99%

“…Yet, a large proportion of MS signals remain unidentified. Recent strategies combining NMR and MS show a strong potential for the identification of the unknowns [ 38 , 98 ].…”

Section: Metabolic Phenotyping Of Cell Culture Metabolism: Analytimentioning

confidence: 99%

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

Balcerczyk

Damblon

Elena-Herrmann

et al. 2020

IJMS

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show abstract

“…The second category is related to the research of new unknown biomarkers. In this latter case, untargeted metabolomics would require multiple complementary analytical approaches to characterize the unknown molecule(s) 97,98 . Another emerging field in life sciences and biomedical research is the characterization of the relationship between gut microbiota metabolome and health/disease 99–101 .…”

Section: Main Applications Of Steady‐state Metabolomicsmentioning

confidence: 99%

“…In this latter case, untargeted metabolomics would require multiple complementary analytical approaches to characterize the unknown molecule(s). 97,98 Another emerging field in life sciences and biomedical research is the characterization of the relationship between gut microbiota metabolome and health/disease. [99][100][101] For instance, it has been also demonstrated that human gut microbiota homeostasis has a direct impact on host serum metabolomic profile, insulin sensitivity, and neurotransmitters abundance.…”

Section: Main Applications Of Steady-state Metabolomicsmentioning

confidence: 99%

Investigating metabolism by mass spectrometry: From steady state to dynamic view

et al. 2020

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Metabolism is the set of life‐sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so‐called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady‐state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

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“…However, these methods are not effective for unknown molecules whose spectra are not registered in the database. In addition, even for prediction methods based on computational chemistry [ 17 ] and machine learning [ 18 – 20 ], NMR spectra cannot be calculated without the structures of the compounds; thus, these methods are also not useful for unknown molecules [ 21 , 22 ]. In this work, we try to identify unknown molecules from its NMR spectrum with a de novo molecule generator.…”

Section: Introductionmentioning

confidence: 99%

NMR-TS: de novo molecule identification from NMR spectra

Zhang

Terayama

Sumita

et al. 2020

Science and Technology of Advanced Materials

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Nuclear magnetic resonance (NMR) spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proofof-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures.

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Identification of Unknown Metabolomics Mixture Compounds by Combining NMR, MS, and Cheminformatics

Cited by 26 publications

References 28 publications

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

Investigating metabolism by mass spectrometry: From steady state to dynamic view

NMR-TS: de novo molecule identification from NMR spectra

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