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Human untargeted metabolomics studies annotate only ~10% of molecular features. We introduce reference-data-driven analysis to match metabolomics tandem mass spectrometry (MS/MS) data against metadata-annotated source data as a pseudo-MS/MS reference library. Applying this approach to food source data, we show that it increases MS/MS spectral usage 5.1-fold over conventional structural MS/MS library matches and allows empirical assessment of dietary patterns from untargeted data.

STRUCTURED ABSTRACTINTRODUCTIONThe ketogenic diet (KD) is an intriguing candidate for neuroprotection in Alzheimer’s disease (AD) given its protective effects against metabolic dysregulation and seizures. The diet’s neuroprotective effects have been shown to be gut microbiome-dependent in mice; thus we examined KD-induced changes in the gut microbiome and metabolome in patients at-risk for AD.METHODSWe compared the low-carbohydrate modified Mediterranean Ketogenic Diet (MMKD) to the low-fat American Heart Association Diet (AHAD) in adults with mild cognitive impairment (MCI) and control participants. We collected stool samples for shotgun metagenomics and untargeted metabolomics at five timepoints to interrogate the microbiome and metabolome.RESULTSIndividuals with MCI on the MMKD had lower levels of GABA-producing microbes Alistipes sp. CAG:514 and GABA, and higher levels of GABA-regulating microbes Akkermansia muciniphila.DISCUSSIONWe hypothesize that MMKD protects individuals with MCI in part via influencing on GABA levels and gut-transit time.

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of public MS/MS spectra. Annotations were propagated based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer’s brain phenotype. The nearest neighbor suspect spectral library is openly available through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Clinical testing typically relies on invasive blood draws and biopsies. Alternative methods of sample collection are continually being developed to improve patient experience; swabbing the skin is one of the least invasive sampling methods possible. To show that skin swabs in combination with untargeted mass spectrometry (metabolomics) can be used for non-invasive monitoring of an oral drug, we report the kinetics and metabolism of diphenhydramine in healthy volunteers (n=10) over the course of 24 hours in blood and three regions of the skin. Diphenhydramine and its metabolites were observed on the skin after peak plasma levels, varying by compound and skin location, and is an illustrative example of how systemically administered molecules can be detected on the skin surface. The observation of diphenhydramine directly from the skin supports the hypothesis that both parent drug and metabolites can be qualitatively measured from a simple non-invasive swab of the skin surface. The mechanism of the drug and metabolites pathway to the skin’s surface remains unknown.

Identification of metabolites in humans remains challenging. Here, we present synthesis-based reverse metabolomics as a strategy for structure elucidation that aims to also find phenotypic associations. In this approach, MS/MS spectra are acquired from newly synthesized compounds, and searched against public metabolomics data to uncover their phenotypic associations. To demonstrate the concept, we used combinatorial amide coupling reactions to synthesize an array of amino acid conjugated bile acids. A total of 16,587 spectral matches were found for 145 amidates synthesized, representing the single largest expansion of structures in the 170+ year history of bile acids. Furthermore, some new bile acids were associated with health-related phenotypes such as inflammatory bowel diseases and obesity. Using independent human cohorts for validation revealed that some cholic and chenodeoxycholic acid conjugates were elevated in Crohn’s disease, of which some were also potent PXR agonists. Bacteria belonging to bifidobacterium, clostridium, and enterococci genera were the main producers of these new conjugated bile acids. Because searching repositories with MS/MS spectra has only recently become possible, this synthesis-based reverse metabolomics approach can now be employed as a general strategy to elucidate structures and discover other new molecules from human and animal ecosystems.

Human untargeted metabolomics studies succeed in annotating only ~10% of molecular features. We, therefore, introduce reference data-driven analysis that uses the source data as a pseudo-MS/MS reference library to match against human metabolomics MS/MS data. We demonstrate this approach with food source data, allowing an empirical assessment of dietary patterns from untargeted data but is broadly applicable and provides an additional layer of interpretability to metabolomics data.

Non-Ribosomal Peptides (NRPs) represent a biomedically important class of natural products that include a multitude of antibiotics and other clinically used drugs. NRPs are not directly encoded in the genome but are instead produced by metabolic pathways encoded by biosynthetic gene clusters (BGCs). Since the existing genome mining tools predict many putative NRPs synthesized by a given BGC, it remains unclear which of these putative NRPs are correct and how to identify post-assembly modifications of amino acids in these NRPs in a blind mode, without knowing which modifications exist in the sample. To address this challenge, here we report NRPminer, a modification-tolerant tool for NRP discovery from large (meta)genomic and mass spectrometry datasets. We show that NRPminer is able to identify many NRPs from different environments, including four previously unreported NRP families from soil-associated microbes and NRPs from human microbiota. Furthermore, in this work we demonstrate the anti-parasitic activities and the structure of two of these NRP families using direct bioactivity screening and nuclear magnetic resonance spectrometry, illustrating the power of NRPminer for discovering bioactive NRPs., Current genome mining methods predict many putative non-ribosomal peptides (NRPs) from their corresponding biosynthetic gene clusters, but it remains unclear which of those exist in nature and how to identify their post-assembly modifications. Here, the authors develop NRPminer, a modification-tolerant tool for the discovery of NRPs from large genomic and mass spectrometry datasets, and use it to find 180 NRPs from different environments.

Author(s): Petras, Daniel; Phelan, Vanessa V; Acharya, Deepa; Allen, Andrew E; Aron, Allegra T; Bandeira, Nuno; Bowen, Benjamin P; Belle-Oudry, Deirdre; Boecker, Simon; Cummings, Dale A; Deutsch, Jessica M; Fahy, Eoin; Garg, Neha; Gregor, Rachel; Handelsman, Jo; Navarro-Hoyos, Mirtha; Jarmusch, Alan K; Jarmusch, Scott A; Louie, Katherine; Maloney, Katherine N; Marty, Michael T; Meijler, Michael M; Mizrahi, Itzhak; Neve, Rachel L; Northen, Trent R; Molina-Santiago, Carlos; Panitchpakdi, Morgan; Pullman, Benjamin; Puri, Aaron W; Schmid, Robin; Subramaniam, Shankar; Thukral, Monica; Vasquez-Castro, Felipe; Dorrestein, Pieter C; Wang, Mingxun | Abstract: AbstractAccess to web-based platforms has enabled scientists to perform research remotely. A critical aspect of mass spectrometry data analysis is the inspection, analysis, and visualization of the raw data to validate data quality and confirm statistical observations. We developed the GNPS Dashboard, a web-based data visualization tool, to facilitate synchronous collaborative inspection, visualization, and analysis of private and public mass spectrometry data remotely.

Author(s): Fang, Xin; Vazquez-Baeza, Yoshiki; Elijah, Emmanuel; Vargas, Fernando; Ackermann, Gail; Humphrey, Gregory; Lau, Rebecca; Weldon, Kelly C; Sanders, Jon G; Panitchpakdi, Morgan; Carpenter, Carolina; Jarmusch, Alan K; Neill, Jennifer; Miralles, Ara; Dulai, Parambir; Singh, Siddharth; Tsai, Matthew; Swafford, Austin D; Smarr, Larry; Boyle, David L; Palsson, Bernhard O; Chang, John T; Dorrestein, Pieter C; Sandborn, William J; Knight, Rob; Boland, Brigid S | Abstract: BackgroundMany studies have investigated the role of the microbiome in inflammatory bowel disease (IBD), but few have focused on surgery specifically or its consequences on the metabolome that may differ by surgery type and require longitudinal sampling. Our objective was to characterize and contrast microbiome and metabolome changes after different surgeries for IBD, including ileocolonic resection and colectomy.MethodsThe UC San Diego IBD Biobank was used to prospectively collect 332 stool samples from 129 subjects (50 ulcerative colitis; 79 Crohn's disease). Of these, 21 with Crohn's disease had ileocolonic resections, and 17 had colectomies. We used shotgun metagenomics and untargeted liquid chromatography followed by tandem mass spectrometry metabolomics to characterize the microbiomes and metabolomes of these patients up to 24 months after the initial sampling.ResultsThe species diversity and metabolite diversity both differed significantly among groups (species diversity: Mann-Whitney U test P value = 7.8e-17; metabolomics, P-value = 0.0043). Escherichia coli in particular expanded dramatically in relative abundance in subjects undergoing surgery. The species profile was better able to classify subjects according to surgery status than the metabolite profile (average precision 0.80 vs 0.68).ConclusionsIntestinal surgeries seem to reduce the diversity of the gut microbiome and metabolome in IBD patients, and these changes may persist. Surgery also further destabilizes the microbiome (but not the metabolome) over time, even relative to the previously established instability in the microbiome of IBD patients. These long-term effects and their consequences for health outcomes need to be studied in prospective longitudinal trials linked to microbiome-involved phenotypes.

Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a “balance score” that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.

We engineered a machine learning approach, MSHub, to enable auto-deconvolution of gas chromatography–mass spectrometry (GC–MS) data. We then designed workflows to enable the community to store, process, share, annotate, compare and perform molecular networking of GC–MS data within the Global Natural Product Social (GNPS) Molecular Networking analysis platform. MSHub/GNPS performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization and quantifies the reproducibility of fragmentation patterns across samples., Fil: Aksenov, Alexander A. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Aksenov, Alexander A. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Laponogov, Ivan. South Kensington Campus. Imperial College London. Department of Surgery and Cancer; Reino Unido, Fil: Zhang, Zheng. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Doran, Sophie L. F. South Kensington Campus. Imperial College London. Department of Surgery and Cancer; Reino Unido, Fil: Belluomo, Ilaria. South Kensington Campus. Imperial College London. Department of Surgery and Cancer; Reino Unido, Fil: Veselkov, Dennis. Intelligify Limited. Reino Unido. South Kensington Campus. Imperial College. Department of Computing; Reino Unido, Fil: Bittremieux, Wout. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Bittremieux, Wout. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Bittremieux, Wout. University of Antwerp. Department of Computer Science; Belgica, Fil: Nothias, Louis Felix. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Nothias, Louis Felix. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Nothias-Esposito, Mélissa. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Nothias-Esposito, Mélissa. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Maloney, Katherine N. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Maloney, Katherine N. Point Loma Nazarene University. Department of Chemistry; Estados Unidos, Fil: Misra, Biswapriya B. Wake Forest School of Medicine. Section of Molecular Medicine. Department of Internal Medicine. Center for Precision Medicine; Estados Unidos, Fil: Melnik, Alexey V. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Smirnov, Aleksandr. University of North Carolina at Charlotte. Department of Bioinformatics and Genomics; Estados Unidos, Fil: Du, Xiuxia. University of North Carolina at Charlotte. Department of Bioinformatics and Genomics; Estados Unidos, Fil: Jones, Kenneth L. University of North Carolina at Charlotte. Department of Bioinformatics and Genomics; Estados Unidos, Fil: Dorrestein, Kathleen. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Dorrestein, Kathleen. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Panitchpakdi, Morgan. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Ernst, Madeleine. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Ernst, Madeleine. Statens Serum Institut. Danish Center for Neonatal Screening. Department of Congenital Disorders. Section for Clinical Mass Spectrometry; Dinamarca, Fil: van der Hooft, Justin J. J. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: van der Hooft, Justin J. J. Wageningen University. Bioinformatics Group; Países Bajos, Fil: Gonzalez, Mabel. Universidad de los Andes. Department of Chemistry; Colombia, Fil: Carazzone, Chiara. Universidad de los Andes. Department of Chemistry; Colombia, Fil: Amézquita, Adolfo. Universidad de los Andes. Department of Biological Sciences; Colombia, Fil: Callewaert, Chris. Center for Microbial Ecology and Technology; Belgica, Fil: Callewaert, Chris. University of California. Department of Pediatrics; Estados Unidos, Fil: Morton, James T. University of California. Department of Pediatrics; Estados Unidos, Fil: Morton, James T. Simons Foundation. Flatiron Institute. Center for Computational Biology; Estados Unidos, Fil: Quinn, Robert A. Michigan State University. Department of Biochemistry and Molecular Biology; Estados Unidos, Fil: Bouslimani, Amina. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Bouslimani, Amina. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Orio, Andrea Albarracín. Universidad Católica de Córdoba. IRNASUS. CONICET. Facultad de Ciencias Agropecuarias; Argentina, Fil: Petras, Daniel. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Petras, Daniel. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Smania, Andrea M. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Departamento de Química Biológica Ranwel Caputto; Argentina, Fil: Smania, Andrea M. Centro de Investigaciones en Química Biológica de Córdoba. Universidad Nacional de Córdoba. CONICET; Argentina, Fil: Couvillion, Sneha P. Pacific Northwest National Laboratory. Biological Sciences Division; Estados Unidos, Fil: Burnet, Meagan C. Pacific Northwest National Laboratory. Biological Sciences Division; Estados Unidos, Fil: Nicora, Carrie D. Pacific Northwest National Laboratory. Biological Sciences Division; Estados Unidos, Fil: Zink, Erika. Pacific Northwest National Laboratory. Biological Sciences Division; Estados Unidos, Fil: Metz, Thomas O. Pacific Northwest National Laboratory. Biological Sciences Division; Estados Unidos, Fil: Artaev, Viatcheslav. LECO Corporation; Estados Unidos, Fil: Humston-Fulmer, Elizabeth. LECO Corporation; Estados Unidos, Fil: Gregor, Rachel. Ben-Gurion University of the Negev. Department of Chemistry and the National Institute for Biotechnology in the Negev; Israel, Fil: Meijler, Michael M. Ben-Gurion University of the Negev. Department of Chemistry and the National Institute for Biotechnology in the Negev; Israel, Fil: Mizrahi, Itzhak. Ben-Gurion University of the Negev. Department of Life Sciences and the National Institute for Biotechnology in the Negev; Israel, Fil: Eyal, Stav. Ben-Gurion University of the Negev. Department of Life Sciences and the National Institute for Biotechnology in the Negev; Israel, Fil: Anderson, Brooke. University of California. Division of Biological Sciences; Estados Unidos, Fil: Dutton, Rachel. University of California. Division of Biological Sciences; Estados Unidos, Fil: Lugan, Raphaël. Université d’Avignon et des Pays du Vaucluse, Agrosciences. UMR Qualisud; Francia, Fil: Boulch, Pauline Le. Université d’Avignon et des Pays du Vaucluse, Agrosciences. UMR Qualisud; Francia, Fil: Guitton, Yann. INRAe. Oniris. Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA); Francia, Fil: Prevost, Stephanie. INRAe. Oniris. Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA); Francia, Fil: Poirier, Audrey. INRAe. Oniris. Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA); Francia, Fil: Dervilly, Gaud. INRAe. Oniris. Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA); Francia, Fil: Le Bizec, Bruno. Oniris. Laboratoire d’Etude des Résidus et Contaminants dans les Aliments (LABERCA); Francia, Fil: Fait, Aaron. Ben Gurion University of the Negev. Sede Boqer Campus. The Jacob Blaustein Institutes for Desert Research. The French Associates Institute for Agriculture and Biotechnology of Dryland; Israel, Fil: Persi, Noga Sikron. Ben Gurion University of the Negev. Sede Boqer Campus. The Jacob Blaustein Institutes for Desert Research. The French Associates Institute for Agriculture and Biotechnology of Dryland; Israel, Fil: Song, Chao. Ben Gurion University of the Negev. Sede Boqer Campus. The Jacob Blaustein Institutes for Desert Research. The French Associates Institute for Agriculture and Biotechnology of Dryland; Israel, Fil: Gashu, Kelem. Ben Gurion University of the Negev. Sede Boqer Campus. The Jacob Blaustein Institutes for Desert Research. The French Associates Institute for Agriculture and Biotechnology of Dryland; Israel, Fil: Coras, Roxana. University of California. Department of Medicine. Division of Rheumatology; Estados Unidos, Fil: Guma, Monica. University of California. Department of Medicine. Division of Rheumatology; Estados Unidos, Fil: Manasson, Julia. New York University School of Medicine. Department of Medicine. Division of Rheumatology; Estados Unidos, Fil: Scher, Jose U. New York University School of Medicine. Department of Medicine. Division of Rheumatology; Estados Unidos, Fil: Barupal, Dinesh Kumar. Icahn School of Medicine at Mount Sinai. Department of Environmental Medicine and Public Health; Estados Unidos, Fil: Alseekh, Saleh. Max Planck Institute for Molecular Plant Physiology; Alemania, Fil: Alseekh, Saleh. Center of Plant Systems Biology and Biotechnology (CPSBB); Bulgaria, Fil: Fernie, Alisdair R. Max Planck Institute for Molecular Plant Physiology; Alemania, Fil: Fernie, Alisdair R. Center of Plant Systems Biology and Biotechnology (CPSBB); Bulgaria, Fil: Mirnezami, Reza. Royal Free Hospital NHS Foundation Trust. Department of Colorectal Surgery; Reino Unido, Fil: Vasiliou, Vasilis. Yale University. Yale School of Public Health. Department of Environmental Health Sciences; Estados Unidos, Fil: Schmid, Robin. University of Münster. Institute of Inorganic and Analytical Chemistry; Alemania, Fil: Borisov, Roman S. A.V. Topchiev Institute of Petrochemical Synthesis RAS; Rusia, Fil: Kulikova, Larisa N. Рeoples’ Friendship University of Russia (RUDN University); Rusia, Fil: Knight, Rob. University of California. Department of Pediatrics; Estados Unidos, Fil: Knight, Rob. University of California. UCSD Center for Microbiome Innovation; Estados Unidos, Fil: Knight, Rob. University of California. Department of Bioengineering; Estados Unidos, Fil: Wang, Mingxun. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Wang, Mingxun. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Hanna, George B. South Kensington Campus. Imperial College London. Department of Surgery and Cancer; Reino Unido, Fil: Dorrestein, Pieter C. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences; Estados Unidos, Fil: Dorrestein, Pieter C. University of California. Skaggs School of Pharmacy and Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos, Fil: Dorrestein, Pieter C. University of California. Department of Pediatrics; Estados Unidos, Fil: Veselkov, Kirill. South Kensington Campus. Imperial College London. Department of Surgery and Cancer; Reino Unido

SummaryThe human metabolome has remained largely unknown, with most studies annotating ∼10% of features. In nucleic acid sequencing, annotating transcripts by source has proven essential for understanding gene function. Here we generalize this concept to stool, plasma, urine and other human metabolomes, discovering that food-based annotations increase the interpreted fraction of molecular features 7-fold, providing a general framework for expanding the interpretability of human metabolomic “dark matter.”

Molecular networking connects tandem mass spectra of molecules based on the similarity of their fragmentation patterns. However, during ionization, molecules commonly form multiple ion species with different fragmentation behavior. To connect ion species of the same molecule, we developed Ion Identity Molecular Networking. These new relationships improve network connectivity, are shown to reveal novel ion-ligand complexes, enhance annotation within molecular networks, and facilitate the expansion of spectral libraries. Fil: Schmid, Robin. University Of Munster; Alemania Fil: Petras, Daniel. University Of California At San Diego. Skaggs School Of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center.; Estados Unidos Fil: Nothias, Louis Félix. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Wang, Mingxun. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Aron, Allegra T.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Jagels, Annika. University of Munster; Alemania Fil: Tsugawa, Hiroshi. RIKEN Center for Sustainable Resource Science; Japón Fil: Rainer, Johannes. University of Lübeck; Italia Fil: Garcia-Aloy, Mar. University of Lübeck; Italia Fil: Dührkop, Kai. Universitat Jena; Alemania Fil: Korf, Ansgar. University of Munster; Alemania Fil: Pluskal, Tomáš. Czech Academy of Sciences; República Checa Fil: Kameník, Zdeněk. Czech Academy of Sciences; República Checa Fil: Jarmusch, Alan K.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Caraballo Rodríguez, Andrés Mauricio. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Weldon, Kelly. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Nothias Esposito, Melissa. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Aksenov, Alexander A.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Bauermeister, Anelize. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Albarracín Orio, Andrea Georgina. Universidad Católica de Córdoba. Instituto de Investigaciones en Recursos Naturales y Sustentabilidad José Sanchez Labrador S. J. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Recursos Naturales y Sustentabilidad José Sanchez Labrador S. J.; Argentina Fil: Grundmann, Carlismari O.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Vargas, Fernando. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Koester, Irina. University of California at San Diego; Estados Unidos Fil: Gauglitz, Julia M.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Gentry, Emily C.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Hövelmann, Yannick. University of Munster; Alemania Fil: Kalinina, Svetlana A.. University of Munster; Alemania Fil: Pendergraft, Matthew A.. University of California at San Diego; Estados Unidos Fil: Panitchpakdi, Morgan W.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Tehan, Richard. Oregon State University; Estados Unidos Fil: Le Gouellec, Audrey. Universite Grenoble Alpes; Francia. Centre National de la Recherche Scientifique; Francia Fil: Aleti, Gajender. University of California at San Diego; Estados Unidos Fil: Mannochio Russo, Helena. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos Fil: Arndt, Birgit. University of Munster; Alemania Fil: Hübner, Florian. University of Munster; Alemania Fil: Hayen, Heiko. University of Munster; Alemania Fil: Zhi, Hui. University of California at San Diego; Estados Unidos Fil: Raffatellu, Manuela. University of California at San Diego; Estados Unidos Fil: Prather, Kimberly A.. University of California at San Diego; Estados Unidos Fil: Aluwihare, Lihini I.. University of California at San Diego; Estados Unidos Fil: Böcker, Sebastian. Friedrich-Schiller-University, Jena; Alemania Fil: McPhail, Kerry L.. State University of Oregon; Estados Unidos Fil: Humpf, Hans-Ulrich. University of Munster; Alemania Fil: Karst, Uwe. University of Munster; Francia Fil: Dorrestein, Pieter. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unidos

A mosaic of cross-phylum chemical interactions occurs between all metazoans and their microbiomes. A number of molecular families that are known to be produced by the microbiome have a marked effect on the balance between health and disease1–9. Considering the diversity of the human microbiome (which numbers over 40,000 operational taxonomic units10), the effect of the microbiome on the chemistry of an entire animal remains underexplored. Here we use mass spectrometry informatics and data visualization approaches11–13 to provide an assessment of the effects of the microbiome on the chemistry of an entire mammal by comparing metabolomics data from germ-free and specific-pathogen-free mice. We found that the microbiota affects the chemistry of all organs. This included the amino acid conjugations of host bile acids that were used to produce phenylalanocholic acid, tyrosocholic acid and leucocholic acid, which have not previously been characterized despite extensive research on bile-acid chemistry14. These bile-acid conjugates were also found in humans, and were enriched in patients with inflammatory bowel disease or cystic fibrosis. These compounds agonized the farnesoid X receptor in vitro, and mice gavaged with the compounds showed reduced expression of bile-acid synthesis genes in vivo. Further studies are required to confirm whether these compounds have a physiological role in the host, and whether they contribute to gut diseases that are associated with microbiome dysbiosis. Metabolomics data from germ-free and specific-pathogen-free mice reveal effects of the microbiome on host chemistry, identifying conjugations of bile acids that are also enriched in patients with inflammatory bowel disease or cystic fibrosis.

Rationale A major hurdle in identifying chemicals in mass spectrometry experiments is the availability of tandem mass spectrometry (MS/MS) reference spectra in public databases. Currently, scientists purchase databases or use public databases such as Global Natural Products Social Molecular Networking (GNPS). The MSMS-Chooser workflow is an open-source protocol for the creation of MS/MS reference spectra directly in the GNPS infrastructure. Methods An MSMS-Chooser Sample Template is provided and completed manually. The MSMS-Chooser Submission File and Sequence Table for data acquisition were programmatically generated. Standards from the Mass Spectrometry Metabolite Library (MSMLS) suspended in a methanol-water (1:1) solution were analyzed. Flow injection on an LC/MS/MS system was used to generate negative and positive mode data using data-dependent acquisition. The MS/MS spectra and Submission File were uploaded to MSMS-Chooser workflow in GNPS for automatic selection of MS/MS spectra. Results Data acquisition and processing required ~2 h and ~2 min, respectively, per 96-well plate using MSMS-Chooser. Analysis of the MSMLS, over 600 small molecules, using MSMS-Chooser added 889 spectra (including multiple adducts) to the public library in GNPS. Manual validation of one plate indicated accurate selection of MS/MS scans (true positive rate of 0.96 and a true negative rate of 0.99). The MSMS-Chooser output includes a table formatted for inclusion in the GNPS library as well as the ability to directly launch searches via MASST. Conclusions MSMS-Chooser enables rapid data acquisition, data analysis (selection of MS/MS spectra), and a formatted table for inspection and upload to GNPS. Open file-format data (.mzML or.mzXML) from most mass spectrometry platforms containing MS/MS spectra can be processed using MSMS-Chooser. MSMS-Chooser democratizes the creation of MS/MS reference spectra in GNPS which will improve annotation and strengthen the tools which use the annotation information.

We present ReDU ( https://redu.ucsd.edu/ ), a system for metadata capture of public mass spectrometry-based metabolomics data, with validated controlled vocabularies. Systematic capture of knowledge enables the reanalysis of public data and/or co-analysis of one’s own data. ReDU enables multiple types of analyses, including finding chemicals and associated metadata, comparing the shared and different chemicals between groups of samples, and metadata-filtered, repository-scale molecular networking. Repository-scale reanalysis of public mass spectrometry-based metabolomics data is facilitated by the Reanalysis of Data User (ReDU) interface, a system that uses consistent formatting and controlled vocabularies for metadata capture.

In our daily lives, we consume foods that have been transported, stored, prepared, cooked, or otherwise processed by ourselves or others. Food storage and preparation have drastic effects on the chemical composition of foods. Untargeted mass spectrometry analysis of food samples has the potential to increase our chemical understanding of these processes by detecting a broad spectrum of chemicals. We performed a time-based analysis of the chemical changes in foods during common preparations, such as fermentation, brewing, and ripening, using untargeted mass spectrometry and molecular networking. The data analysis workflow presented implements an approach to study changes in food chemistry that can reveal global alterations in chemical profiles, identify changes in abundance, as well as identify specific chemicals and their transformation products. The data generated in this study are publicly available, enabling the replication and re-analysis of these data in isolation, and serve as a baseline dataset for future investigations.

The human metabolome has remained largely unknown, with most studies annotating ~10% of features. In nucleic acid sequencing, annotating transcripts by source has proven essential for understanding gene function. Here we generalize this concept to stool, plasma, urine and other human metabolomes, discovering that food-based annotations increase the interpreted fraction of molecular features 7-fold, providing a general framework for expanding the interpretability of human metabolomic “dark matter.”

RationaleA major hurdle in identifying chemicals in mass spectrometry experiments is the availability of MS/MS reference spectra in public databases. Currently, scientists purchase databases or use public databases such as GNPS. The MSMS-Chooser workflow empowers the creation of MS/MS reference spectra directly in the GNPS infrastructure.MethodsAn MSMS-Chooser sample template was completed with the required information and sequence tables were generated programmatically. Standards in methanol-water (1:1) solution (1 μM) were placed into wells individually. An LC-MS/MS system using data-dependent acquisition in positive and negative modes was used. Species that may be generated under typical ESI conditions are chosen. The MS/MS spectra and MSMS-Chooser sample template were subsequently uploaded to MSMS-Chooser in GNPS for automatic MS/MS spectral annotation.ResultsData acquisition quickly and effectively collected MS/MS spectra. MSMS-Chooser was able to accurately annotate 99.2% of the manually validated MS/MS scans that were generated from the chemical standards. The output of MSMS-Chooser includes a table ready for inclusion in the GNPS library (after inspection) as well as the ability to directly launch searches via MASST. Altogether, the data acquisition, processing, and upload to GNPS took ~2 hours for our proof-of-concept results.ConclusionsThe MSMS-Chooser workflow enables the rapid data acquisition, analysis, and annotation of chemical standards, and uploads the MS/MS spectra to community-driven GNPS. MSMS-Chooser democratizes the creation of MS/MS reference spectra in GNPS which will improve annotation and strengthen the tools which use the annotation information.

CorrespondenceWe introduce a web-enabled small-molecule mass spectrometry (MS) search engine. To date, no tool can query all the public small-molecule tandem MS data in metabolomics repositories, greatly limiting the utility of these resources in clinical, environmental and natural product applications. Therefore, we introduce aMassSpectrometrySearchTool (MASST) (https://proteosafe-extensions.ucsd.edu/masst/), that enables the discovery of molecular relationships among accessible public metabolomics and natural product tandem mass spectrometry data (MS/MS).

Non-Ribosomal Peptides (NRPs) represent a biomedically important class of natural products that include a multitude of antibiotics and other clinically used drugs. NRPs are not directly encoded in the genome but are instead produced by metabolic pathways encoded by biosynthetic gene clusters (BGCs). Since the existing genome mining tools predict many putative NRPs synthesized by a given BGC, it remains unclear which of these putative NRPs are correct and how to identify post-assembly modifications of amino acids in these NRPs in a blind mode, without knowing which modifications exist in the sample. To address this challenge, here we report NRPminer, a modification-tolerant tool for NRP discovery from large (meta)genomic and mass spectrometry datasets. We show that NRPminer is able to identify many NRPs from different environments, including four previously unreported NRP families from soil-associated microbes and NRPs from human microbiota. Furthermore, in this work we demonstrate the anti-parasitic activities and the structure of two of these NRP families using direct bioactivity screening and nuclear magnetic resonance spectrometry, illustrating the power of NRPminer for discovering bioactive NRPs.

The aim of this study was to investigate if the declared benefits associated with superfoods are related to a specific molecular composition. For this purpose, untargeted metabolomics and molecular networking were used to obtain an overview of all features, focusing on compounds with anti-inflammatory, antioxidant or antimicrobial properties. 565 plant-based food samples were analyzed using UHPLC-HRMS and advanced data analysis tools. The molecular networking of the whole dataset allowed identification of a greater diversity of molecules, in particular, prenol lipids, isoflavonoids and isoquinolines in superfoods, when compared with non-superfood species belonging to the same botanical family. Furthermore, in silico tools were used to expand our chemical knowledge of compounds observed in superfood samples.

A major aspect of our daily lives is the need to acquire, store and prepare our food. Storage and preparation can have drastic effects on the compositional chemistry of our foods, but we have a limited understanding of the temporal nature of processes such as storage, spoilage, fermentation and brewing on the chemistry of the foods we eat. Here, we performed a temporal analysis of the chemical changes in foods during common household preparations using untargeted mass spectrometry and novel data analysis approaches. Common treatments of foods such as home fermentation of yogurt, brewing of tea, spoilage of meats and ripening of tomatoes altered the chemical makeup through time, through both chemical and biological processes. For example, brewing tea altered its composition by increasing the diversity of molecules, but this change was halted after 4 min of brewing. The results indicate that this is largely due to differential extraction of the material from the tea and not modification of the molecules during the brewing process. This is in contrast to the preparation of yogurt from milk, spoilage of meat and the ripening of tomatoes where biological transformations directly altered the foods molecular composition. Comprehensive assessment of chemical changes using multivariate statistics showed the varied impacts of the different food treatments, while analysis of individual chemical changes show specific alterations of chemical families in the different food types. The methods developed here represent novel approaches to studying the changes in food chemistry that can reveal global alterations in chemical profiles and specific transformations at the chemical level.