Untargeted mass spectrometry-based metabolomics approach unveils molecular changes in raw and processed foods and beverages

Gauglitz, Julia M.; Aceves, Christine M.; Aksenov, Alexander A.; Aleti, Gajender; Almaliti, Jehad; Bouslimani, Amina; Brown, Élizabeth; Campeau, Anaamika; Caraballo‐Rodríguez, Andrés Mauricio; Chaar, Rama; Silva, Ricardo R. da; Demko, Alyssa M.; Ottavio, Francesca Di; Elijah, Emmanuel O.; Ernst, Madeleine; Ferguson, L. Paige; Holmes, Xavier; Jarmusch, Alan K.; Jiang, Lingjing; Kang, Kyo Bin; Koester, Irina; Kwan, Brian; Li, Jie; Li, Yueying; Melnik, Alexey V.; Molina-Santiago, Carlos; Ni, Bohan; Oom, Aaron L.; Panitchpakdi, Morgan; Petras, Daniel; Quinn, Robert A.; Sikora, Nicole; Spengler, Katharina; Teke, Bahar; Tripathi, Anupriya; Ul-Hasan, Sabah; Hooft, Justin J. J. van der; Vargas, Fernando; Vrbanac, Alison; Vu, Anthony Q.; Wang, Steven C; Weldon, Kelly C.; Wilson, Kayla; Wozniak, Jacob M.; Yoon, Michael C.; Bandeira, Nuno; Dorrestein, Pieter C.

doi:10.1016/j.foodchem.2019.125290

Cited by 55 publications

(33 citation statements)

References 41 publications

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“…In recent years, non-targeted as well as suspect screening approaches have increasingly attracted the interest for the tentative identification of unknown or suspect components in foods [22,23]. Accurate mass measurement and characteristic fragmentation are powerful tools for the elucidation of the molecular structure of unknown and suspect compounds.…”

Section: Suspect Analysismentioning

confidence: 99%

Study of the Royal Jelly Free Fatty Acids by Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS)

et al. 2020

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The lipidome of royal jelly (RJ) consists of medium-chained (8–12 carbon atoms) free fatty acids. We present herein a liquid chromatography-high resolution mass spectrometry (HRMS) method that permits the determination of RJ fatty acids and at the same time the detection of suspect fatty acids. The method allows for the direct quantification of seven free fatty acids of RJ, avoiding any derivatization step. It was validated and applied in seven RJ samples, where the major RJ fatty acid trans-10-hydroxy-2-decenoic acid (10-HDA) was found to vary from 0.771 ± 0.08 to 0.928 ± 0.04 g/100 g fresh RJ. Four additional suspect fatty acids were simultaneously detected taking advantage of the HRMS detection.

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Section: Suspect Analysismentioning

confidence: 99%

Study of the Royal Jelly Free Fatty Acids by Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS)

et al. 2020

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“…Recent advances in high-throughput mass spectrometry have enabled collection of billions of mass spectra from hundreds of thousands of host-oriented/environmental samples [8, 9, 10, 11]. A mass spectrum is the fingerprint of a small molecule, which can be represented by a set of mass peaks (Figure 1AB).…”

Section: Introductionmentioning

confidence: 99%

MolDiscovery: Learning Mass Spectrometry Fragmentation of Small Molecules

Cao

Guler

Tagirdzhanov

et al. 2020

Preprint

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Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. This is a challenging task as currently it is not clear how small molecules are fragmented in mass spectrometry. The existing approaches use the domain knowledge from chemistry to predict fragmentation of molecules. However, these rule-based methods fail to explain many of the peaks in mass spectra of small molecules. Recently, spectral libraries with tens of thousands of labelled mass spectra of small molecules have emerged, paving the path for learning more accurate fragmentation models for mass spectral database search. We present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by (i) utilizing an efficient algorithm to generate mass spectrometry fragmentations, and (ii) learning a probabilistic model to match small molecules with their mass spectra. We show our database search is an order of magnitude more efficient than the state-of-the-art methods, which enables searching against databases with millions of molecules. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that our probabilistic model can correctly identify nearly six times more unique small molecules than previous methods. Moreover, by applying molDiscovery on microbial datasets with both mass spectral and genomics data we successfully discovered the novel biosynthetic gene clusters of three families of small molecules.AvailabilityThe command-line version of molDiscovery and its online web service through the GNPS infrastructure are available at https://github.com/mohimanilab/molDiscovery.

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“…Importantly, these resources are expected to become increasingly comprehensive, especially for food compounds. For example, efforts are underway by the USDA to expand their food composition databases [7], and recent investigations have identified additional compounds produced during food processing [21] and by human microbiota [22], which may promote certain health effects. While QSAR models are susceptible to false positives due to activity cliffs (key discontinuities in the structure-activity landscape), outputs from PhyteByte are intended to be only putative structure-activity relationships to be explored further through complementary computational and laboratory methods [23].…”

Section: Resultsmentioning

confidence: 99%

PhyteByte: identification of foods containing compounds with specific pharmacological properties

et al. 2020

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Background: Phytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could enable more effective and more personalized dietary planning. We have created a machine learning-based tool (PhyteByte) to leverage existing pharmacological data to predict bioactivity across a comprehensive molecular database of foods and food compounds. Results: PhyteByte uses a cheminformatic approach to structure-based activity prediction and applies it to uncover the putative bioactivity of food compounds. The tool takes an input protein target and develops a random forest classifier to predict the effect of an input molecule based on its molecular fingerprint, using structure and activity data available from the ChEMBL database. It then predicts the relevant bioactivity of a library of food compounds with known molecular structures from the FooDB database. The output is a list of food compounds with high confidence of eliciting relevant biological effects, along with their source foods and associated quantities in those foods, where available. Applying PhyteByte to the human PPARG gene, we identified irigenin, sesamin, fargesin, and delta-sanshool as putative agonists of PPARG, along with previously identified agonists of this important metabolic regulator. Conclusions: PhyteByte identifies food-based compounds that are predicted to interact with specific protein targets. The identified relationships can be used to prioritize food compounds for experimental or epidemiological follow-up and can contribute to the rapid development of precision approaches to new nutraceuticals as well as personalized dietary planning.

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Untargeted mass spectrometry-based metabolomics approach unveils molecular changes in raw and processed foods and beverages

Cited by 55 publications

References 41 publications

Study of the Royal Jelly Free Fatty Acids by Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS)

Study of the Royal Jelly Free Fatty Acids by Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS)

MolDiscovery: Learning Mass Spectrometry Fragmentation of Small Molecules

PhyteByte: identification of foods containing compounds with specific pharmacological properties

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