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The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.

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.

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.

White teeth can give confidence and tend to be associated with a healthier lifestyle in modern society. Therefore, tooth-bleaching strategies have been developed, including the use of hydrogen peroxide. Recently, peroxymonosulfate has been introduced as an alternative bleaching method to hydrogen peroxide. Although both chemicals are oxidizing agents, their effects on the molecular composition of the stained teeth are yet unknown. In this study, the molecular profiles of teeth bleached with hydrogen peroxide and peroxymonosulfate were compared using Liquid Chromatography-Tandem Mass Spectrometry. Statistical analyses were used to assess the samples. In addition, reference spectral libraries and in silico tools were used to perform metabolite annotation. Overall, principal component analysis showed a strong separation between control and hydrogen peroxide and peroxymonosulfate samples ( p  < 0.001). The analysis of molecular changes revealed amino acids and dipeptides in stained teeth samples after hydrogen peroxide and peroxymonosulfate treatments. Noteworthy, the two bleaching methods led to distinct molecular profiles. For example, diterpenoids were more prevalent after peroxymonosulfate treatment, while a greater abundance of alkaloids was detected after hydrogen peroxide treatment. Whereas non-bleached samples (controls) showed mainly lipids. Therefore, this study shows how two different tooth-whitening peroxides could affect the molecular profiles of human teeth.

Bacteria of the genera Pseudomonas and Bacillus can promote plant growth and protect plants from pathogens. However, the interactions between these plant-beneficial bacteria are understudied. Here, we explore the interaction between Bacillus subtilis 3610 and Pseudomonas chlororaphis PCL1606. We show that the extracellular matrix protects B. subtilis colonies from infiltration by P. chlororaphis . The absence of extracellular matrix results in increased fluidity and loss of structure of the B. subtilis colony. The P. chlororaphis type VI secretion system (T6SS) is activated upon contact with B. subtilis cells, and stimulates B. subtilis sporulation. Furthermore, we find that B. subtilis sporulation observed prior to direct contact with P. chlororaphis is mediated by histidine kinases KinA and KinB. Finally, we demonstrate the importance of the extracellular matrix and the T6SS in modulating the coexistence of the two species on melon plant leaves and seeds. Pseudomonas and Bacillus can promote plant growth but their mutual interactions are unclear. Here, the authors show that the extracellular matrix protects Bacillus colonies from infiltration by Pseudomonas cells, while the Pseudomonas type VI secretion system stimulates Bacillus sporulation.

Appropriate diet and health of the common marmoset in captivity are essential both for the welfare of the animal and to improve experimental outcomes. Our study shows that a gel diet compared to a biscuit diet improves the health of a marmoset colony, is linked to increases in Bifidobacterium species, and increases the removal of molecules associated with disease. The diet transition had an influence on the molecular changes at both the pair and time point group levels, but only at the pair level for the microbial changes. It appears to be more important which genes and functions present changed rather than specific microbes. Further studies are needed to identify specific components that should be considered when choosing an appropriate diet and additional supplementary foods, as well as to validate the benefits of providing probiotics. Probiotics containing Bifidobacterium species appear to be useful as probiotic supplements to the laboratory marmoset diet, but additional work is needed to validate these findings.

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.

Global Natural Product Social Molecular Networking (GNPS) is an interactive online small molecule–focused tandem mass spectrometry (MS 2 ) data curation and analysis infrastructure. It is intended to provide as much chemical insight as possible into an untargeted MS 2 dataset and to connect this chemical insight to the user’s underlying biological questions. This can be performed within one liquid chromatography (LC)-MS 2 experiment or at the repository scale. GNPS-MassIVE is a public data repository for untargeted MS 2 data with sample information (metadata) and annotated MS 2 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 MS 2 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. Global Natural Product Social Molecular Networking (GNPS) is an online tandem mass spectrometry (MS 2 ) data curation and analysis infrastructure. This protocol describes how to use GNPS to explore uploaded metabolomics data.

The Panamanian rocket frog Colostethus panamansis (family Dendrobatidae) has been affected by chytridiomycosis, a deadly disease caused by the fungus Batrachochytrium dendrobatidis (Bd). While there are still uninfected frogs, we set out to isolate microbes from anatomically distinct regions in an effort to create a cultivable resource within Panama for potential drug/agricultural/ecological applications that perhaps could also be used as part of a strategy to protect frogs from infections. To understand if there are specific anatomies that should be explored in future applications of this resource, we mapped skin-associated bacteria of C. panamansis and their metabolite production potential by mass spectrometry on a 3D model. Our results indicate that five bacterial families (Enterobacteriaceae, Comamonadaceae, Aeromonadaceae, Staphylococcaceae and Pseudomonadaceae) dominate the cultivable microbes from the skin of C. panamansis. The combination of microbial classification and molecular analysis in relation to the anti-Bd inhibitory databases reveals the resource has future potential for amphibian conservation.