The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry techniques are well-suited to high-throughput characterization of natural products, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social molecular networking (GNPS, http://gnps.ucsd.edu), an open-access knowledge base for community wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of ‘living data’ through continuous reanalysis of deposited data.

The coffee berry borer (Hypothenemus hampei) is the most devastating insect pest of coffee worldwide with its infestations decreasing crop yield by up to 80%. Caffeine is an alkaloid that can be toxic to insects and is hypothesized to act as a defence mechanism to inhibit herbivory. Here we show that caffeine is degraded in the gut of H. hampei, and that experimental inactivation of the gut microbiota eliminates this activity. We demonstrate that gut microbiota in H. hampei specimens from seven major coffee-producing countries and laboratory-reared colonies share a core of microorganisms. Globally ubiquitous members of the gut microbiota, including prominent Pseudomonas species, subsist on caffeine as a sole source of carbon and nitrogen. Pseudomonas caffeine demethylase genes are expressed in vivo in the gut of H. hampei, and re-inoculation of antibiotic-treated insects with an isolated Pseudomonas strain reinstates caffeine-degradation ability confirming their key role.

a b s t r a c tThe cycling of soil organic matter (SOM) by microorganisms is a critical component of the global carbon cycle but remains poorly understood. There is an emerging view that much of SOM, and especially the dissolved fraction (DOM), is composed of small molecules of plant and microbial origin resulting from lysed cells and released metabolites. Unfortunately, little is known about the small molecule composition of soils and how these molecules are cycled (by microbes or plants or by adsorption to mineral surfaces). The water-extractable organic matter (WEOM) fraction is of particular interest given that this is presumably the most biologically-accessible component of SOM. Here we describe the development of a simple soil metabolomics workflow and a novel spike recovery approach using 13 C bacterial lysates to assess the types of metabolites remaining in the WEOM fraction. Soil samples were extracted with multiple mass spectrometry-compatible extraction buffers (water, 10 mM K 2 SO 4 or NH 4 HCO 3 , 10e100% methanol or isopropanol/methanol/water [3:3:2 v/v/v]) with and without prior chloroform vapor fumigation. Profiling of derivatized extracts was performed using gas chromatography/mass spectrometry (GC/MS) with 55 metabolites identified by comparing fragmentation patterns and retention times with authentic standards. As expected, fumigation, which is thought to lyse microbial cells, significantly increased the range and abundance of metabolites relative to unfumigated samples. To assess the types of microbial metabolites from lysed bacterial cells that remain in the WEOM fraction, an extract was prepared from the soil bacterium Pseudomonas stutzerii RCH2 grown on 13 C acetate. This approach produced highly labeled metabolites that were easily discriminated from the endogenous soil metabolites. Comparing the composition of the fresh bacterial extract with what was recovered following a 15 min incubation with soil revealed that only 27% of the metabolites showed >50% recovery in the WEOM. Many, especially cations (polyamines) and anions, showed <10% recovery. These represent metabolites that may be inaccessible to microbes in this environment and would be most likely to accumulate as SOM presumably due to binding with minerals and negatively-charged clay particles. This study presents a simple untargeted metabolomics workflow for extractable organic matter and an approach to estimate microbial metabolite availability in soils. These methods can be used to further our understanding of SOM and DOM composition and examine the link between metabolic pathways and microbial communities to terrestrial carbon cycling.Published by Elsevier Ltd.

Exometabolomics enables analysis of metabolite utilization of low molecular weight organic substances by soil bacteria. Environmentally-based defined media are needed to examine ecologically relevant patterns of substrate utilization. Here, we describe an approach for the construction of defined media using untargeted characterization of water soluble soil microbial metabolites from a saprolite soil collected from the Oak Ridge Field Research Center (ORFRC). To broadly characterize metabolites, both liquid chromatography mass spectrometry (LC/MS) and gas chromatography mass spectrometry (GC/MS) were used. With this approach, 96 metabolites were identified, including amino acids, amino acid derivatives, sugars, sugar alcohols, mono- and di-carboxylic acids, nucleobases, and nucleosides. From this pool of metabolites, 25 were quantified. Molecular weight cut-off filtration determined the fraction of carbon accounted for by the quantified metabolites and revealed that these soil metabolites have an uneven quantitative distribution (e.g., trehalose accounted for 9.9% of the <1 kDa fraction). This quantitative information was used to formulate two soil defined media (SDM), one containing 23 metabolites (SDM1) and one containing 46 (SDM2). To evaluate the viability of the SDM, we examined the growth of 30 phylogenetically diverse soil bacterial isolates from the ORFRC field site. The simpler SDM1 supported the growth of 13 isolates while the more complex SDM2 supported 15 isolates. To investigate SDM1 substrate preferences, one isolate, Pseudomonas corrugata strain FW300-N2E2 was selected for a time-series exometabolomics analysis. Interestingly, it was found that this organism preferred lower-abundance substrates such as guanine, glycine, proline and arginine and glucose and did not utilize the more abundant substrates maltose, mannitol, trehalose and uridine. These results demonstrate the viability and utility of using exometabolomics to construct a tractable environmentally relevant media. We anticipate that this approach can be expanded to other environments to enhance isolation and characterization of diverse microbial communities.

BackgroundMixed cultures of different microbial species are increasingly being used to carry out a specific biochemical function in lieu of engineering a single microbe to do the same task. However, knowing how different species’ metabolisms will integrate to reach a desired outcome is a difficult problem that has been studied in great detail using steady-state models. However, many biotechnological processes, as well as natural habitats, represent a more dynamic system. Examining how individual species use resources in their growth medium or environment (exometabolomics) over time in batch culture conditions can provide rich phenotypic data that encompasses regulation and transporters, creating an opportunity to integrate the data into a predictive model of resource use by a mixed community.ResultsHere we use exometabolomic profiling to examine the time-varying substrate depletion from a mixture of 19 amino acids and glucose by two Pseudomonas and one Bacillus species isolated from ground water. Contrary to studies in model organisms, we found surprisingly few correlations between resource preferences and maximal growth rate or biomass composition. We then modeled patterns of substrate depletion, and used these models to examine if substrate usage preferences and substrate depletion kinetics of individual isolates can be used to predict the metabolism of a co-culture of the isolates. We found that most of the substrates fit the model predictions, except for glucose and histidine, which were depleted more slowly than predicted, and proline, glycine, glutamate, lysine and arginine, which were all consumed significantly faster.ConclusionsOur results indicate that a significant portion of a model community’s overall metabolism can be predicted based on the metabolism of the individuals. Based on the nature of our model, the resources that significantly deviate from the prediction highlight potential metabolic pathways affected by species-species interactions, which when further studied can potentially be used to modulate microbial community structure and/or function.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1478-2) contains supplementary material, which is available to authorized users.