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2. Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics.

Author

Huan, Tao, Jarmusch, Alan, Kaddurah-Daouk, Rima, Kang, Kyo, Kim, Hyun, Kondić, Todor, Mannochio-Russo, Helena, Meehan, Michael, Melnik, Alexey, Nothias, Louis-Felix, ODonovan, Claire, Panitchpakdi, Morgan, Petras, Daniel, Schmid, Robin, Schymanski, Emma, van der Hooft, Justin, Weldon, Kelly, Yang, Heejung, Xing, Shipei, Zemlin, Jasmine, Wang, Mingxun, Dorrestein, Pieter, Thomas, Sydney, Kakhkhorov, Sarvar, Aksenov, Alexander, Gomes, Paulo, Aceves, Christine, Caraballo-Rodríguez, Andrés, Gauglitz, Julia, Bittremieux, Wout, Gerwick, William, and Avalon, Nicole

Subjects
Tandem Mass Spectrometry, Access to Information, Metabolomics, Gene Library, Cluster Analysis
Abstract

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 MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or suspects, were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns 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 Alzheimers brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Published
2023

3. Illuminating the Dark Metabolome of Pseudo-nitzschia-microbiome Associations

Author

Koester, Irina, Quinlan, Zachary A., Nothias, Louis-Felix, White, Margot E., Rabines, Ariel, Petras, Daniel, Brunson, John K., Duhrkop, Kai, Ludwig, Marcus, Bocker, Sebastian, Azam, Farooq, Allen, Andrew E., Dorrestein, Pieter C., and Aluwihare, Lihini I.

Abstract

The exchange of metabolites mediates algal and bacterial interactions that maintain ecosystem function. Yet, while 1000s of metabolites are produced, only a few molecules have been identifiedin these associations. Using the ubiquitous microalgae Pseudo-nitzschia sp., as a model, we employed an untargeted metabolomics strategy to assign structural characteristics to themetabolites that distinguished specific diatom-microbiome associations. We cultured five species of Pseudo-nitzschia, including two species that produced the toxin domoic acid, and examinedtheir microbiomes and metabolomes. A total of 4826 molecular features were detected by tandem mass spectrometry. Only 229 of these could be annotated using available mass spectral libraries,but by applying new in-silico annotation tools, characterization was expanded to 2710 features. The metabolomes of the Pseudo-nitzschia-microbiome associations were distinct and distinguished by structurally diverse nitrogen compounds, ranging from simple amines andamides to cyclic compounds such as imidazoles, pyrrolidines, and lactams. By illuminating the dark metabolomes, this study expands our capacity to discover new chemical targets that facilitatemicrobial partnerships and uncovers the chemical diversity that underpins algae-bacteria interactions.

Published
2022

4. Distinguishing the molecular diversity, nutrient content, and energetic potential of exometabolomes produced by macroalgae and reef-building corals.

Author

Wegley Kelly, Linda, Nelson, Craig, Petras, Daniel, Koester, Irina, Quinlan, Zachary, Arts, Milou, Nothias, Louis-Felix, Comstock, Jacqueline, White, Brandie, Hopmans, Ellen, van Duyl, Fleur, Dorrestein, Pieter, Haas, Andreas, Carlson, Craig, and Aluwihare, Lihini

Subjects
coral reefs, metabolomics, molecular networking, Animals, Anthozoa, Carbon, Coral Reefs, Dissolved Organic Matter, Ecosystem, Marine Biology, Metabolomics, Nitrogen, Nutrients, Phosphorus, Polynesia, Seawater, Seaweed
Abstract

Metabolites exuded by primary producers comprise a significant fraction of marine dissolved organic matter, a poorly characterized, heterogenous mixture that dictates microbial metabolism and biogeochemical cycling. We present a foundational untargeted molecular analysis of exudates released by coral reef primary producers using liquid chromatography-tandem mass spectrometry to examine compounds produced by two coral species and three types of algae (macroalgae, turfing microalgae, and crustose coralline algae [CCA]) from Moorea, French Polynesia. Of 10,568 distinct ion features recovered from reef and mesocosm waters, 1,667 were exuded by producers; the majority (86%) were organism specific, reflecting a clear divide between coral and algal exometabolomes. These data allowed us to examine two tenets of coral reef ecology at the molecular level. First, stoichiometric analyses show a significantly reduced nominal carbon oxidation state of algal exometabolites than coral exometabolites, illustrating one ecological mechanism by which algal phase shifts engender fundamental changes in the biogeochemistry of reef biomes. Second, coral and algal exometabolomes were differentially enriched in organic macronutrients, revealing a mechanism for reef nutrient-recycling. Coral exometabolomes were enriched in diverse sources of nitrogen and phosphorus, including tyrosine derivatives, oleoyl-taurines, and acyl carnitines. Exometabolites of CCA and turf algae were significantly enriched in nitrogen with distinct signals from polyketide macrolactams and alkaloids, respectively. Macroalgal exometabolomes were dominated by nonnitrogenous compounds, including diverse prenol lipids and steroids. This study provides molecular-level insights into biogeochemical cycling on coral reefs and illustrates how changing benthic cover on reefs influences reef water chemistry with implications for microbial metabolism.

Published
2022

5. Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics

Author

Bittremieux, Wout, Avalon, Nicole E., Thomas, Sydney P., Kakhkhorov, Sarvar A., Aksenov, Alexander A., Gomes, Paulo Wender P., Aceves, Christine M., Caraballo-Rodríguez, Andrés Mauricio, Gauglitz, Julia M., Gerwick, William H., Huan, Tao, Jarmusch, Alan K., Kaddurah-Daouk, Rima F., Kang, Kyo Bin, Kim, Hyun Woo, Kondić, Todor, Mannochio-Russo, Helena, Meehan, Michael J., Melnik, Alexey V., Nothias, Louis-Felix, O’Donovan, Claire, Panitchpakdi, Morgan, Petras, Daniel, Schmid, Robin, Schymanski, Emma L., van der Hooft, Justin J. J., Weldon, Kelly C., Yang, Heejung, Xing, Shipei, Zemlin, Jasmine, Wang, Mingxun, and Dorrestein, Pieter C.

Published
2023
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6. Auto-deconvolution and molecular networking of gas chromatography-mass spectrometry data.

Author

Aksenov, Alexander A, Laponogov, Ivan, Zhang, Zheng, Doran, Sophie LF, Belluomo, Ilaria, Veselkov, Dennis, Bittremieux, Wout, Nothias, Louis Felix, Nothias-Esposito, Mélissa, Maloney, Katherine N, Misra, Biswapriya B, Melnik, Alexey V, Smirnov, Aleksandr, Du, Xiuxia, Jones, Kenneth L, Dorrestein, Kathleen, Panitchpakdi, Morgan, Ernst, Madeleine, van der Hooft, Justin JJ, Gonzalez, Mabel, Carazzone, Chiara, Amézquita, Adolfo, Callewaert, Chris, Morton, James T, Quinn, Robert A, Bouslimani, Amina, Orio, Andrea Albarracín, Petras, Daniel, Smania, Andrea M, Couvillion, Sneha P, Burnet, Meagan C, Nicora, Carrie D, Zink, Erika, Metz, Thomas O, Artaev, Viatcheslav, Humston-Fulmer, Elizabeth, Gregor, Rachel, Meijler, Michael M, Mizrahi, Itzhak, Eyal, Stav, Anderson, Brooke, Dutton, Rachel, Lugan, Raphaël, Boulch, Pauline Le, Guitton, Yann, Prevost, Stephanie, Poirier, Audrey, Dervilly, Gaud, Le Bizec, Bruno, Fait, Aaron, Persi, Noga Sikron, Song, Chao, Gashu, Kelem, Coras, Roxana, Guma, Monica, Manasson, Julia, Scher, Jose U, Barupal, Dinesh Kumar, Alseekh, Saleh, Fernie, Alisdair R, Mirnezami, Reza, Vasiliou, Vasilis, Schmid, Robin, Borisov, Roman S, Kulikova, Larisa N, Knight, Rob, Wang, Mingxun, Hanna, George B, Dorrestein, Pieter C, and Veselkov, Kirill

Subjects
Animals, Anura, Humans, Algorithms, Gas Chromatography-Mass Spectrometry, Metabolomics
Abstract

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.

Published
2021

7. Reply to: Examining microbe-metabolite correlations by linear methods.

Author

Morton, James T, McDonald, Daniel, Aksenov, Alexander A, Nothias, Louis Felix, Foulds, James R, Quinn, Robert A, Badri, Michelle H, Swenson, Tami L, Van Goethem, Marc W, Northen, Trent R, Vazquez-Baeza, Yoshiki, Wang, Mingxun, Bokulich, Nicholas A, Watters, Aaron, Song, Se Jin, Bonneau, Richard, Dorrestein, Pieter C, and Knight, Rob

Subjects
Microbial Interactions, Developmental Biology, Biological Sciences, Technology, Medical and Health Sciences
Published
2021

8. ReDU: a framework to find and reanalyze public mass spectrometry data

Author

Jarmusch, Alan K, Wang, Mingxun, Aceves, Christine M, Advani, Rohit S, Aguirre, Shaden, Aksenov, Alexander A, Aleti, Gajender, Aron, Allegra T, Bauermeister, Anelize, Bolleddu, Sanjana, Bouslimani, Amina, Caraballo Rodriguez, Andres Mauricio, Chaar, Rama, Coras, Roxana, Elijah, Emmanuel O, Ernst, Madeleine, Gauglitz, Julia M, Gentry, Emily C, Husband, Makhai, Jarmusch, Scott A, Jones, Kenneth L, Kamenik, Zdenek, Le Gouellec, Audrey, Lu, Aileen, McCall, Laura-Isobel, McPhail, Kerry L, Meehan, Michael J, Melnik, Alexey V, Menezes, Riya C, Montoya Giraldo, Yessica Alejandra, Nguyen, Ngoc Hung, Nothias, Louis Felix, Nothias-Esposito, Mélissa, Panitchpakdi, Morgan, Petras, Daniel, Quinn, Robert A, Sikora, Nicole, van der Hooft, Justin JJ, Vargas, Fernando, Vrbanac, Alison, Weldon, Kelly C, Knight, Rob, Bandeira, Nuno, and Dorrestein, Pieter C

Subjects
Biological Sciences, Databases, Chemical, Mass Spectrometry, Metabolomics, Metadata, Models, Chemical, Software, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

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.

Published
2020

10. Identification of Four Amoebicidal Nontoxic Compounds by a Molecular Docking Screen of Naegleria fowleri Sterol Δ8−Δ7-Isomerase and Phenotypic Assays

Author

Shi, Da, Chahal, Kirti Kandhwal, Oto, Patricia, Nothias, Louis-Felix, Debnath, Anjan, McKerrow, James H, Podust, Larissa M, and Abagyan, Ruben

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Orphan Drug, Infectious Diseases, Rare Diseases, Good Health and Well Being, Amebicides, Dose-Response Relationship, Drug, HEK293 Cells, Humans, Isomerases, Models, Molecular, Molecular Docking Simulation, Naegleria fowleri, Phenotype, Protein Conformation, Sequence Homology, Small Molecule Libraries, drug discovery, homology modeling, primary amoebic meningoencephalitis, virtual ligand screen, Medical microbiology
Abstract

Naegleria fowleri is a free-living amoeba causing primary amoebic meningoencephalitis, a rapid-onset brain infection in humans with over 97% mortality rate. Despite some progress in the treatment of the disease, there is no single, proven, evidence-based treatment with a high probability of cure. Here we report the chemical library screening and experimental identification of four new compounds with amoebicidal effects against N. fowleri. The chemical library was screened by molecular docking against a homology model of sterol Δ8-Δ7 isomerase (NfERG2). Thirty top-ranking hits were then tested in a cell-based assay for antiproliferative/amoebicidal activities. Eight chemicals exhibited nearly 100% inhibition of N. fowleri at 50 μM, with the EC50 values ranging from 6 to 25 μM. A cell toxicity assay using human HEK-293 cells was also performed. Four of the compounds preferentially kill amoeba cells with no apparent human cell toxicities. These compounds fall into two distinct chemical scaffolds with druglike properties.

Published
2019

11. MetaMiner: A Scalable Peptidogenomics Approach for Discovery of Ribosomal Peptide Natural Products with Blind Modifications from Microbial Communities

Author

Cao, Liu, Gurevich, Alexey, Alexander, Kelsey L, Naman, C Benjamin, Leão, Tiago, Glukhov, Evgenia, Luzzatto-Knaan, Tal, Vargas, Fernando, Quinn, Robby, Bouslimani, Amina, Nothias, Louis Felix, Singh, Nitin K, Sanders, Jon G, Benitez, Rodolfo AS, Thompson, Luke R, Hamid, Md-Nafiz, Morton, James T, Mikheenko, Alla, Shlemov, Alexander, Korobeynikov, Anton, Friedberg, Iddo, Knight, Rob, Venkateswaran, Kasthuri, Gerwick, William H, Gerwick, Lena, Dorrestein, Pieter C, Pevzner, Pavel A, and Mohimani, Hosein

Subjects
Microbiology, Biological Sciences, Complementary and Integrative Health, Biotechnology, Human Genome, Microbiome, Genetics, Generic health relevance, Computational Biology, Genomics, Humans, Microbiota, Peptides, Protein Processing, Post-Translational, Ribosomes, Software, computational mass spectrometry, metagenomics, microbial metabolites, natural products discovery, ribosomally synthesized and post-translationally modified peptides, Biochemistry and Cell Biology, Biochemistry and cell biology
Abstract

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are an important class of natural products that contain antibiotics and a variety of other bioactive compounds. The existing methods for discovery of RiPPs by combining genome mining and computational mass spectrometry are limited to discovering specific classes of RiPPs from small datasets, and these methods fail to handle unknown post-translational modifications. Here, we present MetaMiner, a software tool for addressing these challenges that is compatible with large-scale screening platforms for natural product discovery. After searching millions of spectra in the Global Natural Products Social (GNPS) molecular networking infrastructure against just eight genomic and metagenomic datasets, MetaMiner discovered 31 known and seven unknown RiPPs from diverse microbial communities, including human microbiome and lichen microbiome, and microorganisms isolated from the International Space Station.

Published
2019

12. Learning representations of microbe–metabolite interactions

Author

Morton, James T, Aksenov, Alexander A, Nothias, Louis Felix, Foulds, James R, Quinn, Robert A, Badri, Michelle H, Swenson, Tami L, Van Goethem, Marc W, Northen, Trent R, Vazquez-Baeza, Yoshiki, Wang, Mingxun, Bokulich, Nicholas A, Watters, Aaron, Song, Se Jin, Bonneau, Richard, Dorrestein, Pieter C, and Knight, Rob

Subjects
Biological Sciences, Lung, Microbiome, 2.1 Biological and endogenous factors, Animals, Bacteria, Benchmarking, Cyanobacteria, Cystic Fibrosis, Inflammatory Bowel Diseases, Mice, Microbiota, Neural Networks, Computer, Pseudomonas aeruginosa, Technology, Medical and Health Sciences, Developmental Biology, Biological sciences
Abstract

Integrating multiomics datasets is critical for microbiome research; however, inferring interactions across omics datasets has multiple statistical challenges. We solve this problem by using neural networks (https://github.com/biocore/mmvec) to estimate the conditional probability that each molecule is present given the presence of a specific microorganism. We show with known environmental (desert soil biocrust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially produced metabolites and inflammatory bowel disease.

Published
2019

13. Niche partitioning of a pathogenic microbiome driven by chemical gradients.

Author

Quinn, Robert A, Comstock, William, Zhang, Tianyu, Morton, James T, da Silva, Ricardo, Tran, Alda, Aksenov, Alexander, Nothias, Louis-Felix, Wangpraseurt, Daniel, Melnik, Alexey V, Ackermann, Gail, Conrad, Douglas, Klapper, Isaac, Knight, Rob, and Dorrestein, Pieter C

Subjects
Lung, Sputum, Humans, Bacterial Infections, Cystic Fibrosis, Virulence Factors, Anti-Bacterial Agents, Chemotaxis, Models, Theoretical, Adult, Metabolic Networks and Pathways, High-Throughput Nucleotide Sequencing, Transcriptome, Microbiota
Abstract

Environmental microbial communities are stratified by chemical gradients that shape the structure and function of these systems. Similar chemical gradients exist in the human body, but how they influence these microbial systems is more poorly understood. Understanding these effects can be particularly important for dysbiotic shifts in microbiome structure that are often associated with disease. We show that pH and oxygen strongly partition the microbial community from a diseased human lung into two mutually exclusive communities of pathogens and anaerobes. Antimicrobial treatment disrupted this chemical partitioning, causing complex death, survival, and resistance outcomes that were highly dependent on the individual microorganism and on community stratification. These effects were mathematically modeled, enabling a predictive understanding of this complex polymicrobial system. Harnessing the power of these chemical gradients could be a drug-free method of shaping microbial communities in the human body from undesirable dysbiotic states.

Published
2018

14. Before platelets: the production of platelet-activating factor during growth and stress in a basal marine organism

Author

d'Auriac, Ines Galtier, Quinn, Robert A, Maughan, Heather, Nothias, Louis-Felix, Little, Mark, Kapono, Clifford A, Cobian, Ana, Reyes, Brandon T, Green, Kevin, Quistad, Steven D, Leray, Matthieu, Smith, Jennifer E, Dorrestein, Pieter C, Rohwer, Forest, Deheyn, Dimitri D, and Hartmann, Aaron C

Subjects
Aggression, Animals, Anthozoa, Phospholipases A2, Platelet Activating Factor, Stress, Physiological, Ultraviolet Rays, coral reef ecology, phospholipids, metabolomics, platelet-activating factor, Biological Sciences, Agricultural and Veterinary Sciences, Medical and Health Sciences
Abstract

Corals and humans represent two extremely disparate metazoan lineages and are therefore useful for comparative evolutionary studies. Two lipid-based molecules that are central to human immunity, platelet-activating factor (PAF) and Lyso-PAF were recently identified in scleractinian corals. To identify processes in corals that involve these molecules, PAF and Lyso-PAF biosynthesis was quantified in conditions known to stimulate PAF production in mammals (tissue growth and exposure to elevated levels of ultraviolet light) and in conditions unique to corals (competing with neighbouring colonies over benthic space). Similar to observations in mammals, PAF production was higher in regions of active tissue growth and increased when corals were exposed to elevated levels of ultraviolet light. PAF production also increased when corals were attacked by the stinging cells of a neighbouring colony, though only the attacked coral exhibited an increase in PAF. This reaction was observed in adjacent areas of the colony, indicating that this response is coordinated across multiple polyps including those not directly subject to the stress. PAF and Lyso-PAF are involved in coral stress responses that are both shared with mammals and unique to the ecology of cnidarians.

Published
2018

15. Before platelets: the production of platelet-activating factor during growth and stress in a basal marine organism.

Author

Galtier d'Auriac, Ines, Quinn, Robert A, Maughan, Heather, Nothias, Louis-Felix, Little, Mark, Kapono, Clifford A, Cobian, Ana, Reyes, Brandon T, Green, Kevin, Quistad, Steven D, Leray, Matthieu, Smith, Jennifer E, Dorrestein, Pieter C, Rohwer, Forest, Deheyn, Dimitri D, and Hartmann, Aaron C

Subjects
Animals, Anthozoa, Platelet Activating Factor, Aggression, Ultraviolet Rays, Phospholipases A2, Stress, Physiological, coral reef ecology, metabolomics, phospholipids, platelet-activating factor, Biological Sciences, Agricultural and Veterinary Sciences, Medical and Health Sciences
Abstract

Corals and humans represent two extremely disparate metazoan lineages and are therefore useful for comparative evolutionary studies. Two lipid-based molecules that are central to human immunity, platelet-activating factor (PAF) and Lyso-PAF were recently identified in scleractinian corals. To identify processes in corals that involve these molecules, PAF and Lyso-PAF biosynthesis was quantified in conditions known to stimulate PAF production in mammals (tissue growth and exposure to elevated levels of ultraviolet light) and in conditions unique to corals (competing with neighbouring colonies over benthic space). Similar to observations in mammals, PAF production was higher in regions of active tissue growth and increased when corals were exposed to elevated levels of ultraviolet light. PAF production also increased when corals were attacked by the stinging cells of a neighbouring colony, though only the attacked coral exhibited an increase in PAF. This reaction was observed in adjacent areas of the colony, indicating that this response is coordinated across multiple polyps including those not directly subject to the stress. PAF and Lyso-PAF are involved in coral stress responses that are both shared with mammals and unique to the ecology of cnidarians.

Published
2018

17. Mass spectrometry searches using MASST

Author

Wang, Mingxun, Jarmusch, Alan K., Vargas, Fernando, Aksenov, Alexander A., Gauglitz, Julia M., Weldon, Kelly, Petras, Daniel, da Silva, Ricardo, Quinn, Robert, Melnik, Alexey V., van der Hooft, Justin J. J., Caraballo-Rodríguez, Andrés Mauricio, Nothias, Louis Felix, Aceves, Christine M., Panitchpakdi, Morgan, Brown, Elizabeth, Di Ottavio, Francesca, Sikora, Nicole, Elijah, Emmanuel O., Labarta-Bajo, Lara, Gentry, Emily C., Shalapour, Shabnam, Kyle, Kathleen E., Puckett, Sara P., Watrous, Jeramie D., Carpenter, Carolina S., Bouslimani, Amina, Ernst, Madeleine, Swafford, Austin D., Zúñiga, Elina I., Balunas, Marcy J., Klassen, Jonathan L., Loomba, Rohit, Knight, Rob, Bandeira, Nuno, and Dorrestein, Pieter C.

Published
2020
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18. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

Author

Bolyen, Evan, Rideout, Jai Ram, Dillon, Matthew R., Bokulich, Nicholas A., Abnet, Christian C., Al-Ghalith, Gabriel A., Alexander, Harriet, Alm, Eric J., Arumugam, Manimozhiyan, Asnicar, Francesco, Bai, Yang, Bisanz, Jordan E., Bittinger, Kyle, Brejnrod, Asker, Brislawn, Colin J., Brown, C. Titus, Callahan, Benjamin J., Caraballo-Rodríguez, Andrés Mauricio, Chase, John, Cope, Emily K., Da Silva, Ricardo, Diener, Christian, Dorrestein, Pieter C., Douglas, Gavin M., Durall, Daniel M., Duvallet, Claire, Edwardson, Christian F., Ernst, Madeleine, Estaki, Mehrbod, Fouquier, Jennifer, Gauglitz, Julia M., Gibbons, Sean M., Gibson, Deanna L., Gonzalez, Antonio, Gorlick, Kestrel, Guo, Jiarong, Hillmann, Benjamin, Holmes, Susan, Holste, Hannes, Huttenhower, Curtis, Huttley, Gavin A., Janssen, Stefan, Jarmusch, Alan K., Jiang, Lingjing, Kaehler, Benjamin D., Kang, Kyo Bin, Keefe, Christopher R., Keim, Paul, Kelley, Scott T., Knights, Dan, Koester, Irina, Kosciolek, Tomasz, Kreps, Jorden, Langille, Morgan G. I., Lee, Joslynn, Ley, Ruth, Liu, Yong-Xin, Loftfield, Erikka, Lozupone, Catherine, Maher, Massoud, Marotz, Clarisse, Martin, Bryan D., McDonald, Daniel, McIver, Lauren J., Melnik, Alexey V., Metcalf, Jessica L., Morgan, Sydney C., Morton, Jamie T., Naimey, Ahmad Turan, Navas-Molina, Jose A., Nothias, Louis Felix, Orchanian, Stephanie B., Pearson, Talima, Peoples, Samuel L., Petras, Daniel, Preuss, Mary Lai, Pruesse, Elmar, Rasmussen, Lasse Buur, Rivers, Adam, Robeson, II, Michael S., Rosenthal, Patrick, Segata, Nicola, Shaffer, Michael, Shiffer, Arron, Sinha, Rashmi, Song, Se Jin, Spear, John R., Swafford, Austin D., Thompson, Luke R., Torres, Pedro J., Trinh, Pauline, Tripathi, Anupriya, Turnbaugh, Peter J., Ul-Hasan, Sabah, van der Hooft, Justin J. J., Vargas, Fernando, Vázquez-Baeza, Yoshiki, Vogtmann, Emily, von Hippel, Max, Walters, William, Wan, Yunhu, Wang, Mingxun, Warren, Jonathan, Weber, Kyle C., Williamson, Charles H. D., Willis, Amy D., Xu, Zhenjiang Zech, Zaneveld, Jesse R., Zhang, Yilong, Zhu, Qiyun, Knight, Rob, and Caporaso, J. Gregory

Published
2019
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20. Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity

Author

Shafer, Justin P., Nothias, Louis-Felix, Thompson, Luke R., Sanders, Jon G., Salido, Rodolfo A., Couvillion, Sneha P., Brejnrod, Asker D., Lejzerowicz, Franck, Haiminen, Niina, Huang, Shi, Lutz, Holly L., Zhu, Qiyun, Martino, Cameron, Morton, James T., Karthikeyan, Smruthi, Nothias-Esposito, Melissa, Duhrkop, Kai, Bocker, Sebastian, Kim, Hyun Woo, Aksenov, Alexander A., Bittremieux, Wout, Minich, Jeremiah J., Marotz, Clarisse, Bryant, MacKenzie M., Sanders, Karenina, Schwartz, Tara, Humphrey, Greg, Vasquez-Baeza, Yoshiki, Tripathi, Anupriya, Parida, Laxmi, Carrieri, Anna Paola, Beck, Kristen L., Das, Promi, Gonzalez, Antonio, McDonald, Daniel, Ladau, Joshua, Karst, Soren M., Albertsen, Mads, Ackermann, Gail, DeReus, Jeff, Thomas, Torsten, Petras, Daniel, Shade, Ashley, Stegen, James, Song, Se Jin, Metz, Thomas O., Swafford, Austin D., Dorrestein, Pieter C., Jansson, Janet K., Gilbert, Jack A., Knight, Rob, Earth Microbiome Project 500 (EMP500) Consortium, Shaffer, Justin P., Nothias, Louis-Félix, Thompson, Luke R., Sanders, Jon G., Couvillion, Sneha P., Haiminen, Niina, Lutz, Holly L., and Zhu, Qiyun

Subjects
Microbiology (medical), Earth, Planet, Pharmacology. Therapy, Immunology, Cell Biology, Applied Microbiology and Biotechnology, Microbiology, Soil, Genetics, Animals, Metagenome, Microbiota/genetics, Metagenomics, Biology
Abstract

Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth's environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment.

Published
2022
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22. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

Author

Wang, Mingxun, Carver, Jeremy J, Phelan, Vanessa V, Sanchez, Laura M, Garg, Neha, Peng, Yao, Nguyen, Don Duy, Watrous, Jeramie, Kapono, Clifford A, Luzzatto-Knaan, Tal, Porto, Carla, Bouslimani, Amina, Melnik, Alexey V, Meehan, Michael J, Liu, Wei-Ting, Crüsemann, Max, Boudreau, Paul D, Esquenazi, Eduardo, Sandoval-Calderón, Mario, Kersten, Roland D, Pace, Laura A, Quinn, Robert A, Duncan, Katherine R, Hsu, Cheng-Chih, Floros, Dimitrios J, Gavilan, Ronnie G, Kleigrewe, Karin, Northen, Trent, Dutton, Rachel J, Parrot, Delphine, Carlson, Erin E, Aigle, Bertrand, Michelsen, Charlotte F, Jelsbak, Lars, Sohlenkamp, Christian, Pevzner, Pavel, Edlund, Anna, McLean, Jeffrey, Piel, Jörn, Murphy, Brian T, Gerwick, Lena, Liaw, Chih-Chuang, Yang, Yu-Liang, Humpf, Hans-Ulrich, Maansson, Maria, Keyzers, Robert A, Sims, Amy C, Johnson, Andrew R, Sidebottom, Ashley M, Sedio, Brian E, Klitgaard, Andreas, Larson, Charles B, Boya P, Cristopher A, Torres-Mendoza, Daniel, Gonzalez, David J, Silva, Denise B, Marques, Lucas M, Demarque, Daniel P, Pociute, Egle, O'Neill, Ellis C, Briand, Enora, Helfrich, Eric J N, Granatosky, Eve A, Glukhov, Evgenia, Ryffel, Florian, Houson, Hailey, Mohimani, Hosein, Kharbush, Jenan J, Zeng, Yi, Vorholt, Julia A, Kurita, Kenji L, Charusanti, Pep, McPhail, Kerry L, Nielsen, Kristian Fog, Vuong, Lisa, Elfeki, Maryam, Traxler, Matthew F, Engene, Niclas, Koyama, Nobuhiro, Vining, Oliver B, Baric, Ralph, Silva, Ricardo R, Mascuch, Samantha J, Tomasi, Sophie, Jenkins, Stefan, Macherla, Venkat, Hoffman, Thomas, Agarwal, Vinayak, Williams, Philip G, Dai, Jingqui, Neupane, Ram, Gurr, Joshua, Rodríguez, Andrés M C, Lamsa, Anne, Zhang, Chen, Dorrestein, Kathleen, Duggan, Brendan M, Almaliti, Jehad, Allard, Pierre-Marie, Phapale, Prasad, Nothias, Louis-Felix, Alexandrov, Theodore, Litaudon, Marc, Wolfender, Jean-Luc, Kyle, Jennifer E, Metz, Thomas O, Peryea, Tyler, Nguyen, Dac-Trung, VanLeer, Danielle, Shinn, Paul, Jadhav, Ajit, Müller, Rolf, Waters, Katrina M, Shi, Wenyuan, Liu, Xueting, Zhang, Lixin, Knight, Rob, Jensen, Paul R, Palsson, Bernhard Ø, Pogliano, Kit, Linington, Roger G, Gutiérrez, Marcelino, Lopes, Norberto P, Gerwick, William H, Moore, Bradley S, Dorrestein, Pieter C, and Bandeira, Nuno

Published
2016
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23. Author Correction: Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

Author

Bolyen, Evan, Rideout, Jai Ram, Dillon, Matthew R., Bokulich, Nicholas A., Abnet, Christian C., Al-Ghalith, Gabriel A., Alexander, Harriet, Alm, Eric J., Arumugam, Manimozhiyan, Asnicar, Francesco, Bai, Yang, Bisanz, Jordan E., Bittinger, Kyle, Brejnrod, Asker, Brislawn, Colin J., Brown, C. Titus, Callahan, Benjamin J., Caraballo-Rodríguez, Andrés Mauricio, Chase, John, Cope, Emily K., Da Silva, Ricardo, Diener, Christian, Dorrestein, Pieter C., Douglas, Gavin M., Durall, Daniel M., Duvallet, Claire, Edwardson, Christian F., Ernst, Madeleine, Estaki, Mehrbod, Fouquier, Jennifer, Gauglitz, Julia M., Gibbons, Sean M., Gibson, Deanna L., Gonzalez, Antonio, Gorlick, Kestrel, Guo, Jiarong, Hillmann, Benjamin, Holmes, Susan, Holste, Hannes, Huttenhower, Curtis, Huttley, Gavin A., Janssen, Stefan, Jarmusch, Alan K., Jiang, Lingjing, Kaehler, Benjamin D., Kang, Kyo Bin, Keefe, Christopher R., Keim, Paul, Kelley, Scott T., Knights, Dan, Koester, Irina, Kosciolek, Tomasz, Kreps, Jorden, Langille, Morgan G. I., Lee, Joslynn, Ley, Ruth, Liu, Yong-Xin, Loftfield, Erikka, Lozupone, Catherine, Maher, Massoud, Marotz, Clarisse, Martin, Bryan D., McDonald, Daniel, McIver, Lauren J., Melnik, Alexey V., Metcalf, Jessica L., Morgan, Sydney C., Morton, Jamie T., Naimey, Ahmad Turan, Navas-Molina, Jose A., Nothias, Louis Felix, Orchanian, Stephanie B., Pearson, Talima, Peoples, Samuel L., Petras, Daniel, Preuss, Mary Lai, Pruesse, Elmar, Rasmussen, Lasse Buur, Rivers, Adam, Robeson, II, Michael S., Rosenthal, Patrick, Segata, Nicola, Shaffer, Michael, Shiffer, Arron, Sinha, Rashmi, Song, Se Jin, Spear, John R., Swafford, Austin D., Thompson, Luke R., Torres, Pedro J., Trinh, Pauline, Tripathi, Anupriya, Turnbaugh, Peter J., Ul-Hasan, Sabah, van der Hooft, Justin J. J., Vargas, Fernando, Vázquez-Baeza, Yoshiki, Vogtmann, Emily, von Hippel, Max, Walters, William, Wan, Yunhu, Wang, Mingxun, Warren, Jonathan, Weber, Kyle C., Williamson, Charles H. D., Willis, Amy D., Xu, Zhenjiang Zech, Zaneveld, Jesse R., Zhang, Yilong, Zhu, Qiyun, Knight, Rob, and Caporaso, J. Gregory

Published
2019
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24. Open Access Repository-Scale Propagated Nearest Neighbor Suspect Spectral Library for Untargeted Metabolomics

Author

Bittremieux, Wout, Avalon, Nicole E., Thomas, Sydney P., Kakhkhorov, Sarvar A., Aksenov, Alexander A., Gomes, Paulo Wender P., Aceves, Christine M., Caraballo Rodríguez, Andrés Mauricio, Gauglitz, Julia M., Gerwick, William H., Jarmusch, Alan K., Kaddurah-Daouk, Rima F., Kang, Kyo Bin, Kim, Hyun Woo, Kondic, Todor, Mannochio-Russo, Helena, Meehan, Michael J., Melnik, Alexey V., Nothias, Louis-Felix, O’Donovan, Claire, Panitchpakdi, Morgan, Petras, Daniel, Schmid, Robin, Schymanski, Emma, van der Hooft, Justin J. J., Weldon, Kelly C., Yang, Heejung, Zemlin, Jasmine, Wang, Mingxun, Dorrestein, Pieter C., Bittremieux, Wout, Avalon, Nicole E., Thomas, Sydney P., Kakhkhorov, Sarvar A., Aksenov, Alexander A., Gomes, Paulo Wender P., Aceves, Christine M., Caraballo Rodríguez, Andrés Mauricio, Gauglitz, Julia M., Gerwick, William H., Jarmusch, Alan K., Kaddurah-Daouk, Rima F., Kang, Kyo Bin, Kim, Hyun Woo, Kondic, Todor, Mannochio-Russo, Helena, Meehan, Michael J., Melnik, Alexey V., Nothias, Louis-Felix, O’Donovan, Claire, Panitchpakdi, Morgan, Petras, Daniel, Schmid, Robin, Schymanski, Emma, van der Hooft, Justin J. J., Weldon, Kelly C., Yang, Heejung, Zemlin, Jasmine, Wang, Mingxun, and Dorrestein, Pieter C.

Abstract

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.

Published
2022

25. Learning accurate representations of microbe-metabolite interactions

Author

Morton, James T., Aksenov, Alexander A., Nothias, Louis Felix, Foulds, James R., Quinn, Robert A., Badri, Michelle H., Swenson, Tami L., Van Goethem, Marc W., Northen, Trent R., Vazquez-Baeza, Yoshiki, Wang, Mingxun, Bokulich, Nicholas A., Watters, Aaron, Song, Se Jin, Bonneau, Richard, Dorrestein, Pieter C., and Knight, Rob

Subjects
food and beverages, Article
Abstract

Integrating multi-omics datasets is critical for microbiome research, but multiple statistical challenges can confound traditional correlation techniques. We solve this problem by using neural networks to estimate the conditional probability that each molecule is present given the presence of each specific microbe. We show with known environmental (desert biological soil crust wetting) and clinical (cystic fibrosis lung) examples, our ability to recover microbe-metabolite relationships, and demonstrate how the method can discover relationships between microbially-produced metabolites and inflammatory bowel disease.

Published
2019

26. A community resource for paired genomic and metabolomic data mining

Author

Schorn, Michelle A., Verhoeven, Stefan, Ridder, Lars, Huber, Florian, Acharya, Deepa D., Aksenov, Alexander A., Aleti, Gajender, Moghaddam, Jamshid Amiri, Aron, Allegra T., Aziz, Saefuddin, Bauermeister, Anelize, Bauman, Katherine D., Baunach, Martin, Beemelmanns, Christine, Beman, J. Michael, Berlanga-Clavero, María Victoria, Blacutt, Alex A., Bode, Helge B., Boullie, Anne, Brejnrod, Asker, Bugni, Tim S., Calteau, Alexandra, Cao, Liu, Carrión, Víctor J., Castelo-Branco, Raquel, Chanana, Shaurya, Chase, Alexander B., Chevrette, Marc G., Costa-Lotufo, Leticia V., Crawford, Jason M., Currie, Cameron R., Cuypers, Bart, Dang, Tam, de Rond, Tristan, Demko, Alyssa M., Dittmann, Elke, Du, Chao, Drozd, Christopher, Dujardin, Jean-Claude, Dutton, Rachel J., Edlund, Anna, Fewer, David P., Garg, Neha, Gauglitz, Julia M., Gentry, Emily C., Gerwick, Lena, Glukhov, Evgenia, Gross, Harald, Gugger, Muriel, Guillén Matus, Dulce G., Helfrich, Eric J.N., Hempel, Benjamin-Florian, Hur, Jae-Seoun, Iorio, Marianna, Jensen, Paul R., Kang, Kyo Bin, Kaysser, Leonhard, Kelleher, Neil L., Kim, Chung Sub, Kim, Hyun-Ki, Koester, Irina, König, Gabriele M., Leao, Tiago, Lee, Seoung Rak, Lee, Yi-Yuan, Li, Xuanji, Little, Jessica C., Maloney, Katherine N., Männle, Daniel, Martin H, Christian, McAvoy, Andrew C., Metcalf, Willam W., Mohimani, Hosein, Molina-Santiago, Carlos, Moore, Bradley S., Mullowney, Michael W., Muskat, Mitchell, Nothias, Louis Felix, O’Neill, Ellis C., Parkinson, Elizabeth I., Petras, Daniel, Piel, Jörn, Pierce, Emily C., Pires, Karine, Reher, Raphael, Romero, Diego, Roper, M. Caroline, Rust, Michael, Saad, Hamada, Saenz, Carmen, Sanchez, Laura M., Sørensen, Søren J., Sosio, Margherita, Süssmuth, Roderich D., Sweeney, Douglas, Tahlan, Kapil, Thomson, Regan J., Tobias, Nicholas J., Trindade-Silva, Amaro E., van Wezel, Gilles P., Wang, Mingxun, Weldon, Kelly C., Zhang, Fan, Ziemert, Nadine, Duncan, Katherine R., Crüsemann, Max, Rogers, Simon, Dorrestein, Pieter C., Medema, Marnix H., and van der Hooft, Justin J.J.

Subjects
Databases, Metabolomics, Computational biology and bioinformatics, DNA, Systems biology
Abstract

The structural complexity and bioactivity of natural products often depend on enzymatic redox tailoring steps. This is exemplified by the generation of the bisbenzannulated [5,6]-spiroketal pharmacophore in the bacterial rubromycin family of aromatic polyketides, which exhibit a wide array of bioactivities such as the inhibition of HIV reverse transcriptase or DNA helicase. Here we elucidate the complex flavoenzyme-driven formation of the rubromycin pharmacophore that is markedly distinct from conventional (bio)synthetic strategies for spiroketal formation. Accordingly, a polycyclic aromatic precursor undergoes extensive enzymatic oxidative rearrangement catalyzed by two flavoprotein monooxygenases and a flavoprotein oxidase that ultimately results in a drastic distortion of the carbon skeleton. The one-pot in vitro reconstitution of the key enzymatic steps as well as the comprehensive characterization of reactive intermediates allow to unravel the intricate underlying reactions, during which four carbon-carbon bonds are broken and two CO2 become eliminated. This work provides detailed insight into perplexing redox tailoring enzymology that sets the stage for the (chemo)enzymatic production and bioengineering of bioactive spiroketal-containing polyketides., Nature Chemical Biology, 17 (4), ISSN:1552-4450, ISSN:1552-4469

Published
2021

27. Repository-scale propagated spectral library of suspects

Author

Bittremieux, Wout, Gauglitz, Julia, Aksenov, Alexander, Jarmusch, Alan K., Nothias, Louis-Felix, Petras, Daniel, Wang, Mingxun, and Dorrestein, Pieter C.

Abstract

In untargeted metabolomics a majority of the acquired MS/MS spectra typically remains unidentified. In part, this is due to the fact that the molecular coverage of reference spectral libraries is necessarily incomplete. However, even if an exact match cannot be found in a spectral library, it is often possible to propagate annotations to structurally related molecules. We have created a "suspect" reference spectral library, consisting of putative molecule identities and a loss or addition characterized by a difference in precursor mass. By propagating library annotations to previously unidentified spectra using repository-scale molecular networking, we are able to extract 79,461 high-quality suspect spectra in a data-driven fashion from 92,063 raw files originating from 1,289 heterogeneous public datasets deposited to GNPS. This forms a rich data resource of novel, real-life, reference spectra that will be freely available for the community to use via GNPS.

Published
2020
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28. Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

Author

Aksenov, Alexander A., Laponogov, Ivan, Zhang, Zheng, Doran, Sophie L.F., Belluomo, Ilaria, Veselkov, Dennis, Bittremieux, Wout, Nothias, Louis Felix, Nothias-Esposito, Mélissa, Maloney, Katherine N., Misra, Biswapriya B., Melnik, Alexey V., Smirnov, Aleksandr, Du, Xiuxia, Jones, Kenneth L., Dorrestein, Kathleen, Panitchpakdi, Morgan, Ernst, Madeleine, van der Hooft, Justin J.J., Gonzalez, Mabel, Carazzone, Chiara, Amézquita, Adolfo, Callewaert, Chris, Morton, James T., Quinn, Robert A., Bouslimani, Amina, Orio, Andrea Albarracín, Petras, Daniel, Smania, Andrea M., Couvillion, Sneha P., Burnet, Meagan C., Nicora, Carrie D., Zink, Erika, Metz, Thomas O., Artaev, Viatcheslav, Humston-Fulmer, Elizabeth, Gregor, Rachel, Meijler, Michael M., Mizrahi, Itzhak, Eyal, Stav, Anderson, Brooke, Dutton, Rachel, Lugan, Raphaël, Boulch, Pauline Le, Guitton, Yann, Prevost, Stephanie, Poirier, Audrey, Dervilly, Gaud, Le Bizec, Bruno, Fait, Aaron, Persi, Noga Sikron, Song, Chao, Gashu, Kelem, Coras, Roxana, Guma, Monica, Manasson, Julia, Scher, Jose U., Barupal, Dinesh Kumar, Alseekh, Saleh, Fernie, Alisdair R., Mirnezami, Reza, Vasiliou, Vasilis, Schmid, Robin, Borisov, Roman S., Kulikova, Larisa N., Knight, Rob, Wang, Mingxun, Hanna, George B., Dorrestein, Pieter C., Veselkov, Kirill, Aksenov, Alexander A., Laponogov, Ivan, Zhang, Zheng, Doran, Sophie L.F., Belluomo, Ilaria, Veselkov, Dennis, Bittremieux, Wout, Nothias, Louis Felix, Nothias-Esposito, Mélissa, Maloney, Katherine N., Misra, Biswapriya B., Melnik, Alexey V., Smirnov, Aleksandr, Du, Xiuxia, Jones, Kenneth L., Dorrestein, Kathleen, Panitchpakdi, Morgan, Ernst, Madeleine, van der Hooft, Justin J.J., Gonzalez, Mabel, Carazzone, Chiara, Amézquita, Adolfo, Callewaert, Chris, Morton, James T., Quinn, Robert A., Bouslimani, Amina, Orio, Andrea Albarracín, Petras, Daniel, Smania, Andrea M., Couvillion, Sneha P., Burnet, Meagan C., Nicora, Carrie D., Zink, Erika, Metz, Thomas O., Artaev, Viatcheslav, Humston-Fulmer, Elizabeth, Gregor, Rachel, Meijler, Michael M., Mizrahi, Itzhak, Eyal, Stav, Anderson, Brooke, Dutton, Rachel, Lugan, Raphaël, Boulch, Pauline Le, Guitton, Yann, Prevost, Stephanie, Poirier, Audrey, Dervilly, Gaud, Le Bizec, Bruno, Fait, Aaron, Persi, Noga Sikron, Song, Chao, Gashu, Kelem, Coras, Roxana, Guma, Monica, Manasson, Julia, Scher, Jose U., Barupal, Dinesh Kumar, Alseekh, Saleh, Fernie, Alisdair R., Mirnezami, Reza, Vasiliou, Vasilis, Schmid, Robin, Borisov, Roman S., Kulikova, Larisa N., Knight, Rob, Wang, Mingxun, Hanna, George B., Dorrestein, Pieter C., and Veselkov, Kirill

Abstract

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.

Published
2021

30. Auto-deconvolution and molecular networking of gas chromatography–mass spectrometry data

Author

Aksenov, Alexander A., primary, Laponogov, Ivan, additional, Zhang, Zheng, additional, Doran, Sophie L. F., additional, Belluomo, Ilaria, additional, Veselkov, Dennis, additional, Bittremieux, Wout, additional, Nothias, Louis Felix, additional, Nothias-Esposito, Mélissa, additional, Maloney, Katherine N., additional, Misra, Biswapriya B., additional, Melnik, Alexey V., additional, Smirnov, Aleksandr, additional, Du, Xiuxia, additional, Jones, Kenneth L., additional, Dorrestein, Kathleen, additional, Panitchpakdi, Morgan, additional, Ernst, Madeleine, additional, van der Hooft, Justin J. J., additional, Gonzalez, Mabel, additional, Carazzone, Chiara, additional, Amézquita, Adolfo, additional, Callewaert, Chris, additional, Morton, James T., additional, Quinn, Robert A., additional, Bouslimani, Amina, additional, Orio, Andrea Albarracín, additional, Petras, Daniel, additional, Smania, Andrea M., additional, Couvillion, Sneha P., additional, Burnet, Meagan C., additional, Nicora, Carrie D., additional, Zink, Erika, additional, Metz, Thomas O., additional, Artaev, Viatcheslav, additional, Humston-Fulmer, Elizabeth, additional, Gregor, Rachel, additional, Meijler, Michael M., additional, Mizrahi, Itzhak, additional, Eyal, Stav, additional, Anderson, Brooke, additional, Dutton, Rachel, additional, Lugan, Raphaël, additional, Boulch, Pauline Le, additional, Guitton, Yann, additional, Prevost, Stephanie, additional, Poirier, Audrey, additional, Dervilly, Gaud, additional, Le Bizec, Bruno, additional, Fait, Aaron, additional, Persi, Noga Sikron, additional, Song, Chao, additional, Gashu, Kelem, additional, Coras, Roxana, additional, Guma, Monica, additional, Manasson, Julia, additional, Scher, Jose U., additional, Barupal, Dinesh Kumar, additional, Alseekh, Saleh, additional, Fernie, Alisdair R., additional, Mirnezami, Reza, additional, Vasiliou, Vasilis, additional, Schmid, Robin, additional, Borisov, Roman S., additional, Kulikova, Larisa N., additional, Knight, Rob, additional, Wang, Mingxun, additional, Hanna, George B., additional, Dorrestein, Pieter C., additional, and Veselkov, Kirill, additional

Published
2020
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31. Revisiting microbe-metabolite interactions: doing better than random

Author

Morton, James T., McDonald, Daniel, Aksenov, Alexander A., Nothias, Louis Felix, Foulds, James R., Quinn, Robert A., Badri, Michelle H., Swenson, Tami L., Van Goethem, Marc W., Northen, Trent R., Vázquez-Baeza, Yoshiki, Wang, Mingxun, Bokulich, Nicholas, Watters, Aaron, Song, Se Jin, Bonneau, Richard, Dorrestein, Pieter C., and Knight, Rob

Abstract

Recently, Quinn and Erb et al [1] made the case that when used correctly, correlation and proportionality can outperform MMvec when identifying microbe-metabolite interactions. We revisit this comparison and show that the proposed correlation and proportionality are outperformed by MMvec on real data due to their inability to deal with sparsity commonly observed in microbiome and metabolome datasets., bioRxiv

Published
2019

32. Identification of Four Amoebicidal Nontoxic Compounds by a Molecular Docking Screen of Naegleria fowleri Sterol Δ8-Δ7-Isomerase and Phenotypic Assays

Author

Shi, Da, Chahal, Kirti Kandhwal, Oto, Patricia, Nothias, Louis-Felix, Debnath, Anjan, McKerrow, James H, Podust, Larissa M, and Abagyan, Ruben

Subjects
Protein Conformation, homology modeling, Neurosciences, Sequence Homology, Molecular, primary amoebic meningoencephalitis, drug discovery, Brain Disorders, Dose-Response Relationship, Small Molecule Libraries, Molecular Docking Simulation, Phenotype, HEK293 Cells, Orphan Drug, Rare Diseases, Good Health and Well Being, Models, Medical Microbiology, Humans, virtual ligand screen, Amebicides, Drug, Isomerases, Naegleria fowleri
Abstract

Naegleria fowleri is a free-living amoeba causing primary amoebic meningoencephalitis, a rapid-onset brain infection in humans with over 97% mortality rate. Despite some progress in the treatment of the disease, there is no single, proven, evidence-based treatment with a high probability of cure. Here we report the chemical library screening and experimental identification of four new compounds with amoebicidal effects against N. fowleri. The chemical library was screened by molecular docking against a homology model of sterol Δ8-Δ7 isomerase (NfERG2). Thirty top-ranking hits were then tested in a cell-based assay for antiproliferative/amoebicidal activities. Eight chemicals exhibited nearly 100% inhibition of N. fowleri at 50 μM, with the EC50 values ranging from 6 to 25 μM. A cell toxicity assay using human HEK-293 cells was also performed. Four of the compounds preferentially kill amoeba cells with no apparent human cell toxicities. These compounds fall into two distinct chemical scaffolds with druglike properties.

Published
2019

34. Mining the NIST Mass Spectral Library Reveals the Extent of Sodium Assisted Inductive Cleavage in Collision-Induced Fragmentation

Author

Fonds National de la Recherche - FnR [sponsor], Ludwig, Marcus, Broeckling, Corey D., Dorrestein, Pieter, Dührkop, Kai, Schymanski, Emma, Böcker, Sebastian, Nothias, Louis-Felix, Fonds National de la Recherche - FnR [sponsor], Ludwig, Marcus, Broeckling, Corey D., Dorrestein, Pieter, Dührkop, Kai, Schymanski, Emma, Böcker, Sebastian, and Nothias, Louis-Felix

Published
2020

35. ReDU: a framework to find and reanalyze public mass spectrometry data

Author

Jarmusch, Alan K., Wang, Mingxun, Aceves, Christine M., Advani, Rohit S., Aguirre, Shaden, Aksenov, Alexander A., Aleti, Gajender, Aron, Allegra T., Bauermeister, Anelize, Bolleddu, Sanjana, Bouslimani, Amina, Caraballo Rodriguez, Andres Mauricio, Chaar, Rama, Coras, Roxana, Elijah, Emmanuel O., Ernst, Madeleine, Gauglitz, Julia M., Gentry, Emily C., Husband, Makhai, Jarmusch, Scott A., Jones, Kenneth L., Kamenik, Zdenek, Le Gouellec, Audrey, Lu, Aileen, McCall, Laura Isobel, McPhail, Kerry L., Meehan, Michael J., Melnik, Alexey V., Menezes, Riya C., Montoya Giraldo, Yessica Alejandra, Nguyen, Ngoc Hung, Nothias, Louis Felix, Nothias-Esposito, Mélissa, Panitchpakdi, Morgan, Petras, Daniel, Quinn, Robert A., Sikora, Nicole, van der Hooft, Justin J.J., Vargas, Fernando, Vrbanac, Alison, Weldon, Kelly C., Knight, Rob, Bandeira, Nuno, Dorrestein, Pieter C., Jarmusch, Alan K., Wang, Mingxun, Aceves, Christine M., Advani, Rohit S., Aguirre, Shaden, Aksenov, Alexander A., Aleti, Gajender, Aron, Allegra T., Bauermeister, Anelize, Bolleddu, Sanjana, Bouslimani, Amina, Caraballo Rodriguez, Andres Mauricio, Chaar, Rama, Coras, Roxana, Elijah, Emmanuel O., Ernst, Madeleine, Gauglitz, Julia M., Gentry, Emily C., Husband, Makhai, Jarmusch, Scott A., Jones, Kenneth L., Kamenik, Zdenek, Le Gouellec, Audrey, Lu, Aileen, McCall, Laura Isobel, McPhail, Kerry L., Meehan, Michael J., Melnik, Alexey V., Menezes, Riya C., Montoya Giraldo, Yessica Alejandra, Nguyen, Ngoc Hung, Nothias, Louis Felix, Nothias-Esposito, Mélissa, Panitchpakdi, Morgan, Petras, Daniel, Quinn, Robert A., Sikora, Nicole, van der Hooft, Justin J.J., Vargas, Fernando, Vrbanac, Alison, Weldon, Kelly C., Knight, Rob, Bandeira, Nuno, and Dorrestein, Pieter C.

Abstract

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.

Published
2020

36. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

Author

Massachusetts Institute of Technology. Department of Biological Engineering, Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics, Bolyen, Evan, Rideout, Jai Ram, Dillon, Matthew R., Bokulich, Nicholas A., Abnet, Christian C., Al-Ghalith, Gabriel A., Alexander, Harriet, Alm, Eric J, Arumugam, Manimozhiyan, Asnicar, Francesco, Bai, Yang, Bisanz, Jordan E., Bittinger, Kyle, Brejnrod, Asker, Brislawn, Colin J., Brown, C. Titus, Callahan, Benjamin J., Caraballo-Rodríguez, Andrés Mauricio, Chase, John, Cope, Emily K., Da Silva, Ricardo, Diener, Christian, Dorrestein, Pieter C., Douglas, Gavin M., Durall, Daniel M., Duvallet, Claire, Edwardson, Christian F., Ernst, Madeleine, Estaki, Mehrbod, Fouquier, Jennifer, Gauglitz, Julia M., Gibbons, Sean M., Gibson, Deanna L., Gonzalez, Antonio, Gorlick, Kestrel, Guo, Jiarong, Hillmann, Benjamin, Holmes, Susan, Holste, Hannes, Huttenhower, Curtis, Huttley, Gavin A., Janssen, Stefan, Jarmusch, Alan K., Jiang, Lingjing, Kaehler, Benjamin D., Kang, Kyo Bin, Keefe, Christopher R., Keim, Paul, Kelley, Scott T., Knights, Dan, Koester, Irina, Kosciolek, Tomasz, Kreps, Jorden, Langille, Morgan G. I., Lee, Joslynn, Ley, Ruth, Liu, Yong-Xin, Loftfield, Erikka, Lozupone, Catherine, Maher, Massoud, Marotz, Clarisse, Martin, Bryan D., McDonald, Daniel, McIver, Lauren J., Melnik, Alexey V., Metcalf, Jessica L., Morgan, Sydney C., Morton, Jamie T., Naimey, Ahmad Turan, Navas-Molina, Jose A., Nothias, Louis Felix, Orchanian, Stephanie B., Pearson, Talima, Peoples, Samuel L., Petras, Daniel, Preuss, Mary Lai, Pruesse, Elmar, Rasmussen, Lasse Buur, Rivers, Adam, Robeson, Michael S., Rosenthal, Patrick, Segata, Nicola, Shaffer, Michael, Shiffer, Arron, Sinha, Rashmi, Song, Se Jin, Spear, John R., Swafford, Austin D., Thompson, Luke R., Torres, Pedro J., Trinh, Pauline, Tripathi, Anupriya, Turnbaugh, Peter J., Ul-Hasan, Sabah, van der Hooft, Justin J. J., Vargas, Fernando, Vázquez-Baeza, Yoshiki, Vogtmann, Emily, von Hippel, Max, Walters, William, Wan, Yunhu, Wang, Mingxun, Warren, Jonathan, Weber, Kyle C., Williamson, Charles H. D., Willis, Amy D., Xu, Zhenjiang Zech, Zaneveld, Jesse R., Zhang, Yilong, Zhu, Qiyun, Knight, Rob, Caporaso, J. Gregory, Massachusetts Institute of Technology. Department of Biological Engineering, Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics, Bolyen, Evan, Rideout, Jai Ram, Dillon, Matthew R., Bokulich, Nicholas A., Abnet, Christian C., Al-Ghalith, Gabriel A., Alexander, Harriet, Alm, Eric J, Arumugam, Manimozhiyan, Asnicar, Francesco, Bai, Yang, Bisanz, Jordan E., Bittinger, Kyle, Brejnrod, Asker, Brislawn, Colin J., Brown, C. Titus, Callahan, Benjamin J., Caraballo-Rodríguez, Andrés Mauricio, Chase, John, Cope, Emily K., Da Silva, Ricardo, Diener, Christian, Dorrestein, Pieter C., Douglas, Gavin M., Durall, Daniel M., Duvallet, Claire, Edwardson, Christian F., Ernst, Madeleine, Estaki, Mehrbod, Fouquier, Jennifer, Gauglitz, Julia M., Gibbons, Sean M., Gibson, Deanna L., Gonzalez, Antonio, Gorlick, Kestrel, Guo, Jiarong, Hillmann, Benjamin, Holmes, Susan, Holste, Hannes, Huttenhower, Curtis, Huttley, Gavin A., Janssen, Stefan, Jarmusch, Alan K., Jiang, Lingjing, Kaehler, Benjamin D., Kang, Kyo Bin, Keefe, Christopher R., Keim, Paul, Kelley, Scott T., Knights, Dan, Koester, Irina, Kosciolek, Tomasz, Kreps, Jorden, Langille, Morgan G. I., Lee, Joslynn, Ley, Ruth, Liu, Yong-Xin, Loftfield, Erikka, Lozupone, Catherine, Maher, Massoud, Marotz, Clarisse, Martin, Bryan D., McDonald, Daniel, McIver, Lauren J., Melnik, Alexey V., Metcalf, Jessica L., Morgan, Sydney C., Morton, Jamie T., Naimey, Ahmad Turan, Navas-Molina, Jose A., Nothias, Louis Felix, Orchanian, Stephanie B., Pearson, Talima, Peoples, Samuel L., Petras, Daniel, Preuss, Mary Lai, Pruesse, Elmar, Rasmussen, Lasse Buur, Rivers, Adam, Robeson, Michael S., Rosenthal, Patrick, Segata, Nicola, Shaffer, Michael, Shiffer, Arron, Sinha, Rashmi, Song, Se Jin, Spear, John R., Swafford, Austin D., Thompson, Luke R., Torres, Pedro J., Trinh, Pauline, Tripathi, Anupriya, Turnbaugh, Peter J., Ul-Hasan, Sabah, van der Hooft, Justin J. J., Vargas, Fernando, Vázquez-Baeza, Yoshiki, Vogtmann, Emily, von Hippel, Max, Walters, William, Wan, Yunhu, Wang, Mingxun, Warren, Jonathan, Weber, Kyle C., Williamson, Charles H. D., Willis, Amy D., Xu, Zhenjiang Zech, Zaneveld, Jesse R., Zhang, Yilong, Zhu, Qiyun, Knight, Rob, and Caporaso, J. Gregory

Published
2020

37. MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools

Author

Ernst, Madeleine, primary, Kang, Kyo Bin, additional, Caraballo-Rodríguez, Andrés Mauricio, additional, Nothias, Louis-Felix, additional, Wandy, Joe, additional, Chen, Christopher, additional, Wang, Mingxun, additional, Rogers, Simon, additional, Medema, Marnix H., additional, Dorrestein, Pieter C., additional, and van der Hooft, Justin J.J., additional

Published
2019
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38. Molnetenhancer: Enhanced molecular networks by integrating metabolome mining and annotation tools

Author

Ernst, Madeleine, Kang, Kyo Bin, Caraballo-Rodríguez, Andrés Mauricio, Nothias, Louis Felix, Wandy, Joe, Chen, Christopher, Wang, Mingxun, Rogers, Simon, Medema, Marnix H., Dorrestein, Pieter C., van der Hooft, Justin J.J., Ernst, Madeleine, Kang, Kyo Bin, Caraballo-Rodríguez, Andrés Mauricio, Nothias, Louis Felix, Wandy, Joe, Chen, Christopher, Wang, Mingxun, Rogers, Simon, Medema, Marnix H., Dorrestein, Pieter C., and van der Hooft, Justin J.J.

Abstract

Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR), and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.

Published
2019

39. Author Correction:Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2 (Nature Biotechnology, (2019), 37, 8, (852-857), 10.1038/s41587-019-0209-9)

Author

Bolyen, Evan, Rideout, Jai Ram, Dillon, Matthew R., Bokulich, Nicholas A., Abnet, Christian C., Al-Ghalith, Gabriel A., Alexander, Harriet, Alm, Eric J., Arumugam, Manimozhiyan, Asnicar, Francesco, Bai, Yang, Bisanz, Jordan E., Bittinger, Kyle, Brejnrod, Asker, Brislawn, Colin J., Brown, C. Titus, Callahan, Benjamin J., Caraballo-Rodríguez, Andrés Mauricio, Chase, John, Cope, Emily K., Da Silva, Ricardo, Diener, Christian, Dorrestein, Pieter C., Douglas, Gavin M., Durall, Daniel M., Duvallet, Claire, Edwardson, Christian F., Ernst, Madeleine, Estaki, Mehrbod, Fouquier, Jennifer, Gauglitz, Julia M., Gibbons, Sean M., Gibson, Deanna L., Gonzalez, Antonio, Gorlick, Kestrel, Guo, Jiarong, Hillmann, Benjamin, Holmes, Susan, Holste, Hannes, Huttenhower, Curtis, Huttley, Gavin A., Janssen, Stefan, Jarmusch, Alan K., Jiang, Lingjing, Kaehler, Benjamin D., Kang, Kyo Bin, Keefe, Christopher R., Keim, Paul, Kelley, Scott T., Knights, Dan, Koester, Irina, Kosciolek, Tomasz, Kreps, Jorden, Langille, Morgan G.I., Lee, Joslynn, Ley, Ruth, Liu, Yong Xin, Loftfield, Erikka, Lozupone, Catherine, Maher, Massoud, Marotz, Clarisse, Martin, Bryan D., McDonald, Daniel, McIver, Lauren J., Melnik, Alexey V., Metcalf, Jessica L., Morgan, Sydney C., Morton, Jamie T., Naimey, Ahmad Turan, Navas-Molina, Jose A., Nothias, Louis Felix, Orchanian, Stephanie B., Pearson, Talima, Peoples, Samuel L., Petras, Daniel, Preuss, Mary Lai, Pruesse, Elmar, Rasmussen, Lasse Buur, Rivers, Adam, Robeson, Michael S., Rosenthal, Patrick, Segata, Nicola, Shaffer, Michael, Shiffer, Arron, Sinha, Rashmi, Song, Se Jin, Spear, John R., Swafford, Austin D., Thompson, Luke R., Torres, Pedro J., Trinh, Pauline, Tripathi, Anupriya, Turnbaugh, Peter J., Ul-Hasan, Sabah, van der Hooft, Justin J.J., Vargas, Fernando, Vázquez-Baeza, Yoshiki, Vogtmann, Emily, von Hippel, Max, Walters, William, Wan, Yunhu, Wang, Mingxun, Warren, Jonathan, Weber, Kyle C., Williamson, Charles H.D., Willis, Amy D., Xu, Zhenjiang Zech, Zaneveld, Jesse R., Zhang, Yilong, Zhu, Qiyun, Knight, Rob, Caporaso, J. Gregory, Bolyen, Evan, Rideout, Jai Ram, Dillon, Matthew R., Bokulich, Nicholas A., Abnet, Christian C., Al-Ghalith, Gabriel A., Alexander, Harriet, Alm, Eric J., Arumugam, Manimozhiyan, Asnicar, Francesco, Bai, Yang, Bisanz, Jordan E., Bittinger, Kyle, Brejnrod, Asker, Brislawn, Colin J., Brown, C. Titus, Callahan, Benjamin J., Caraballo-Rodríguez, Andrés Mauricio, Chase, John, Cope, Emily K., Da Silva, Ricardo, Diener, Christian, Dorrestein, Pieter C., Douglas, Gavin M., Durall, Daniel M., Duvallet, Claire, Edwardson, Christian F., Ernst, Madeleine, Estaki, Mehrbod, Fouquier, Jennifer, Gauglitz, Julia M., Gibbons, Sean M., Gibson, Deanna L., Gonzalez, Antonio, Gorlick, Kestrel, Guo, Jiarong, Hillmann, Benjamin, Holmes, Susan, Holste, Hannes, Huttenhower, Curtis, Huttley, Gavin A., Janssen, Stefan, Jarmusch, Alan K., Jiang, Lingjing, Kaehler, Benjamin D., Kang, Kyo Bin, Keefe, Christopher R., Keim, Paul, Kelley, Scott T., Knights, Dan, Koester, Irina, Kosciolek, Tomasz, Kreps, Jorden, Langille, Morgan G.I., Lee, Joslynn, Ley, Ruth, Liu, Yong Xin, Loftfield, Erikka, Lozupone, Catherine, Maher, Massoud, Marotz, Clarisse, Martin, Bryan D., McDonald, Daniel, McIver, Lauren J., Melnik, Alexey V., Metcalf, Jessica L., Morgan, Sydney C., Morton, Jamie T., Naimey, Ahmad Turan, Navas-Molina, Jose A., Nothias, Louis Felix, Orchanian, Stephanie B., Pearson, Talima, Peoples, Samuel L., Petras, Daniel, Preuss, Mary Lai, Pruesse, Elmar, Rasmussen, Lasse Buur, Rivers, Adam, Robeson, Michael S., Rosenthal, Patrick, Segata, Nicola, Shaffer, Michael, Shiffer, Arron, Sinha, Rashmi, Song, Se Jin, Spear, John R., Swafford, Austin D., Thompson, Luke R., Torres, Pedro J., Trinh, Pauline, Tripathi, Anupriya, Turnbaugh, Peter J., Ul-Hasan, Sabah, van der Hooft, Justin J.J., Vargas, Fernando, Vázquez-Baeza, Yoshiki, Vogtmann, Emily, von Hippel, Max, Walters, William, Wan, Yunhu, Wang, Mingxun, Warren, Jonathan, Weber, Kyle C., Williamson, Charles H.D., Willis, Amy D., Xu, Zhenjiang Zech, Zaneveld, Jesse R., Zhang, Yilong, Zhu, Qiyun, Knight, Rob, and Caporaso, J. Gregory

Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2019

40. Before platelets: the production of platelet-activating factor during growth and stress in a basal marine organism

Author

Galtier d'Auriac, Ines, primary, Quinn, Robert A., additional, Maughan, Heather, additional, Nothias, Louis-Felix, additional, Little, Mark, additional, Kapono, Clifford A., additional, Cobian, Ana, additional, Reyes, Brandon T., additional, Green, Kevin, additional, Quistad, Steven D., additional, Leray, Matthieu, additional, Smith, Jennifer E., additional, Dorrestein, Pieter C., additional, Rohwer, Forest, additional, Deheyn, Dimitri D., additional, and Hartmann, Aaron C., additional

Published
2018
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