Virulence as a Side Effect of Interspecies Interaction in Vibrio Coral Pathogens

The increase in prevalence and severity of coral disease outbreaks produced by Vibrio pathogens, and related to global warming, has seriously impacted reef-building corals throughout the oceans. The coral Oculina patagonica has been used as a model system to study coral bleaching produced by Vibrio infection. Previous data demonstrated that when two coral pathogens (Vibrio coralliilyticus and Vibrio mediterranei) simultaneously infected the coral O. patagonica, their pathogenicity was greater than when each bacterium was infected separately. Here, to understand the mechanisms underlying this synergistic effect, transcriptomic analyses of monocultures and cocultures as well as experimental infection experiments were performed. Our results revealed that the interaction between the two vibrios under culture conditions overexpressed virulence factor genes (e.g., those encoding siderophores, the type VI secretion system, and toxins, among others). Moreover, under these conditions, vibrios were also more likely to form biofilms or become motile through induction of lateral flagella. All these changes that occur as a physiological response to the presence of a competing species could favor the colonization of the host when they are present in a mixed population. Additionally, during coral experimental infections, we showed that exposure of corals to molecules released during V. coralliilyticus and V. mediterranei coculture induced changes in the coral microbiome that favored damage to coral tissue and increased the production of lyso-platelet activating factor. Therefore, we propose that competition sensing, defined as the physiological response to detection of harm or to the presence of a competing Vibrio species, enhances the ability of Vibrio coral pathogens to invade their host and cause tissue necrosis.This research was supported in part by the EU-H2020 MetaFluidics project with grant agreement number 685474 (to J.A.) and NSF-PIRE grant number OISE1243541 (to F.R.). E.R.-P. was funded by the postdoctoral program Vali+d (GVA) (grant number APOSTD-2016-091). A.M.C.-R. and P.C.D. were supported by the National Sciences Foundation grant IOS-1656481

Open Access Repository-Scale Propagated Nearest Neighbor Suspect Spectral Library for Untargeted Metabolomics

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

Nerpa: A Tool for Discovering Biosynthetic Gene Clusters of Bacterial Nonribosomal Peptides

Microbial natural products are a major source of bioactive compounds for drug discovery. Among these molecules, nonribosomal peptides (NRPs) represent a diverse class of natural products that include antibiotics, immunosuppressants, and anticancer agents. Recent breakthroughs in natural product discovery have revealed the chemical structure of several thousand NRPs. However, biosynthetic gene clusters (BGCs) encoding them are known only for a few hundred compounds. Here, we developed Nerpa, a computational method for the high-throughput discovery of novel BGCs responsible for producing known NRPs. After searching 13,399 representative bacterial genomes from the RefSeq repository against 8368 known NRPs, Nerpa linked 117 BGCs to their products. We further experimentally validated the predicted BGC of ngercheumicin from Photobacterium galatheae via mass spectrometry. Nerpa supports searching new genomes against thousands of known NRP structures, and novel molecular structures against tens of thousands of bacterial genomes. The availability of these tools can enhance our understanding of NRP synthesis and the function of their biosynthetic enzymes

Chemical Proportionality within Molecular Networks

Molecular networking of non-targeted tandem mass spectrometry data connects structurally related molecules based on similar fragmentation spectra. Here we report the Chemical Proportionality contextualization of molecular networks. ChemProp scores the changes of abundance between two connected nodes over sequential data series which can be displayed as a direction within the network to prioritize potential biological and chemical transformations or proportional changes of related compounds. We tested the ChemProp workflow on a ground truth data set of defined mixture and highlighted the utility of the tool to prioritize specific molecules within biological samples, including bacterial transformations of bile acids, human drug metabolism and bacterial natural products biosynthesis. The ChemProp workflow is freely available through the Global Natural Products Social Molecular Networking environment. </b

The molecular impact of life in an indoor environment

The chemistry of indoor surfaces, and the role of microbes in shaping and responding to that chemistry, are largely unexplored. We found that over one month, people’s presence and activities profoundly reshaped the chemistry of a house. Molecules associated with eating/cooking, bathroom use, and personal care were found throughout the entire house, while molecules associated with medications, outdoor biocides, and microbially-derived compounds were distributed in a location-dependent manner. The house, and its microbial occupants, in turn, also introduced chemical transformations such as oxidation and transformations of foodborne molecules. The awareness of and the ability to observe the molecular changes introduced by people should influence future building designs

The molecular impact of life in an indoor environment.

The chemistry of indoor surfaces and the role of microbes in shaping and responding to that chemistry are largely unexplored. We found that, over 1 month, people's presence and activities profoundly reshaped the chemistry of a house. Molecules associated with eating/cooking, bathroom use, and personal care were found throughout the entire house, while molecules associated with medications, outdoor biocides, and microbially derived compounds were distributed in a location-dependent manner. The house and its microbial occupants, in turn, also introduced chemical transformations such as oxidation and transformations of foodborne molecules. The awareness of and the ability to observe the molecular changes introduced by people should influence future building designs

Reproducible Molecular Networking Of Untargeted Mass Spectrometry Data Using GNPS.

Herein, we present a protocol for the use of Global Natural Products Social (GNPS) Molecular Networking, an interactive online chemistry-focused mass spectrometry data curation and analysis infrastructure. The goal of GNPS is to provide as much chemical insight for an untargeted tandem mass spectrometry data set as possible and to connect this chemical insight to the underlying biological questions a user wishers to address. This can be performed within one experiment or at the repository scale. GNPS not only serves as a public data repository for untargeted tandem mass spectrometry data with the sample information (metadata), it also captures community knowledge that is disseminated via living data across all public data. One or the main analysis tools used by the GNPS community is molecular networking. Molecular networking creates a structured data table that reflects the chemical space from tandem mass spectrometry experiments via computing the relationships of the tandem mass spectra through spectral similarity. This protocol provides step-by-step instructions for creating reproducible high-quality molecular networks. For training purposes, the reader is led through the protocol from recalling a public data set and its sample information to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions

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

n 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

Reproducible molecular networking of untargeted mass spectrometry data using GNPS

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule–focused tandem mass spectrometry (MS2) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS2 dataset and to connect this chemical insight to the user’s underlying biological questions. This can be performed within one liquid chromatography (LC)-MS2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS2 data with sample information (metadata) and annotated MS2 spectra. These publicly accessible data can be annotated and updated with the GNPS infrastructure keeping a continuous record of all changes. This knowledge is disseminated across all public data; it is a living dataset. Molecular networking—one of the main analysis tools used within the GNPS platform—creates a structured data table that reflects the molecular diversity captured in tandem mass spectrometry experiments by computing the relationships of the MS2 spectra as spectral similarity. This protocol provides step-by-step instructions for creating reproducible, high-quality molecular networks. For training purposes, the reader is led through a 90- to 120-min procedure that starts by recalling an example public dataset and its sample information and proceeds to creating and interpreting a molecular network. Each data analysis job can be shared or cloned to disseminate the knowledge gained, thus propagating information that can lead to the discovery of molecules, metabolic pathways, and ecosystem/community interactions.UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones en Productos Naturales (CIPRONA)UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de Químic

Ion Identity Molecular Networking in the GNPS Environment

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; AlemaniaFil: Petras, Daniel. University Of California At San Diego. Skaggs School Of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center.; Estados UnidosFil: Nothias, Louis Félix. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Wang, Mingxun. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Aron, Allegra T.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Jagels, Annika. University of Munster; AlemaniaFil: Tsugawa, Hiroshi. RIKEN Center for Sustainable Resource Science; JapónFil: Rainer, Johannes. University of Lübeck; ItaliaFil: Garcia-Aloy, Mar. University of Lübeck; ItaliaFil: Dührkop, Kai. Universitat Jena; AlemaniaFil: Korf, Ansgar. University of Munster; AlemaniaFil: Pluskal, Tomáš. Czech Academy of Sciences; República ChecaFil: Kameník, Zdeněk. Czech Academy of Sciences; República ChecaFil: Jarmusch, Alan K.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Caraballo Rodríguez, Andrés Mauricio. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Weldon, Kelly. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Nothias Esposito, Melissa. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Aksenov, Alexander A.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Bauermeister, Anelize. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: 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.; ArgentinaFil: Grundmann, Carlismari O.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Vargas, Fernando. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Koester, Irina. University of California at San Diego; Estados UnidosFil: Gauglitz, Julia M.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Gentry, Emily C.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Hövelmann, Yannick. University of Munster; AlemaniaFil: Kalinina, Svetlana A.. University of Munster; AlemaniaFil: Pendergraft, Matthew A.. University of California at San Diego; Estados UnidosFil: Panitchpakdi, Morgan W.. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Tehan, Richard. Oregon State University; Estados UnidosFil: Le Gouellec, Audrey. Universite Grenoble Alpes; Francia. Centre National de la Recherche Scientifique; FranciaFil: Aleti, Gajender. University of California at San Diego; Estados UnidosFil: Mannochio Russo, Helena. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados UnidosFil: Arndt, Birgit. University of Munster; AlemaniaFil: Hübner, Florian. University of Munster; AlemaniaFil: Hayen, Heiko. University of Munster; AlemaniaFil: Zhi, Hui. University of California at San Diego; Estados UnidosFil: Raffatellu, Manuela. University of California at San Diego; Estados UnidosFil: Prather, Kimberly A.. University of California at San Diego; Estados UnidosFil: Aluwihare, Lihini I.. University of California at San Diego; Estados UnidosFil: Böcker, Sebastian. Friedrich-Schiller-University, Jena; AlemaniaFil: McPhail, Kerry L.. State University of Oregon; Estados UnidosFil: Humpf, Hans-Ulrich. University of Munster; AlemaniaFil: Karst, Uwe. University of Munster; FranciaFil: Dorrestein, Pieter. University of California at San Diego. Skaggs School of Pharmacy & Pharmaceutical Sciences. Collaborative Mass Spectrometry Innovation Center; Estados Unido