Clustering of MS2 spectra using unsupervised methods to aid the identification of secondary metabolites from Pseudomonas aeruginosa

Gene expression and metabolic activity of Streptococcus mutans during exposure to dietary carbohydrates glucose, sucrose, lactose, and xylitol

Recent RNA sequencing studies have given us a deeper insight into the cariogenic impact of carbohydrate sources in the bacterium Streptococcus mutans, the principal microbial agent in dental caries etiopathogenesis. The process of dental caries development is facilitated by the ability of this bacterium to ferment some carbohydrates into organic acids contributing to a pH decrease in the oral cavity and the demineralization of the hard tissues of the tooth. Furthermore, in dental caries progression, biofilm formation, which starts and ends with free planktonic cells, plays an important role and has several unique properties called virulence factors. The most cariogenic carbohydrate is sucrose, an easily metabolizable source of energy that induces the acidification and synthesis of glucans, forming typical bacterial cell clumps. We used multifaceted methodological approaches to compare the transcriptomic and metabolomic profiles of S. mutans growing in planktonic culture on preferred and nonpreferred carbohydrates and in fasting conditions. Streptococcus mutans in a planktonic culture with lactose produced the same pH drop as glucose and sucrose. By contrast, xylitol and lactose showed high effectiveness in regulating intracellular polysaccharide metabolism, cell wall structure, and overall virulence involved in the initial phase of biofilm formation and structure but with an opposite pattern compared with sucrose and glucose. Our results confirmed the recent findings that xylitol and lactose play a vital role in biofilm structure. However, they do not reduce its formation, which is related to the creation of a cariogenic environment.

A Metabolomics-Based Toolbox to Assess and Compare the Metabolic Potential of Unexplored, Difficult-to-Grow Bacteria

Novel high-throughput cultivation techniques create a demand to pre-select strains for in-depth follow-up studies. We report a workflow to identify promising producers of novel natural products by systematically characterizing their metabolomes. For this purpose, 60 strains from four phyla (Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes) comprising 16 novel species and six novel genera were cultivated from marine and terrestrial sources. Their cellular metabolomes were recorded by LC-MS/MS; data analysis comprised databases MS/MS matching, in silico compound assignment, and GNPS-based molecular networking. Overall, 1052 different molecules were identified from 6418 features, among them were unusual metabolites such as 4-methoxychalcone. Only a minor portion of the 755 features were found in all phyla, while the majority occurred in a single phylogroup or even in a single strain. Metabolomic methods enabled the recognition of highly talented strains such as AEG42_45, which had 107 unique features, among which a family of 28 potentially novel and related compounds according to MS/MS similarities. In summary, we propose that high-throughput cultivation and isolation of bacteria in combination with the presented systematic and unbiased metabolome analysis workflow is a promising approach to capture and assess the enormous metabolic potential of previously uncultured bacteria.

Identifying congeners and transformation products of organic contaminants within complex chemical mixtures in impacted surface waters with a top-down non-targeted screening workflow

Over 350,000 compounds are registered for production and use including a high number of congeners found in complex chemical mixtures (CCMs). With such a high number of chemicals being released in the environment and degraded into transformation products (TPs), the challenge of identifying contaminants by non-targeted screening (NTS) is massive. "Bottom-up" studies, where compounds are subjected to conditions simulating environmental degradation to identify new TPs, are time consuming and cannot be relied upon to study the TPs of hundreds of thousands of compounds. Therefore, the development of "top-down" workflows, where the structural elucidation of unknown compounds is carried directly on the sample, is of interest. In this study, a top-down NTS workflow was developed using molecular networking and clustering (MNC). A total of 438 compounds were identified including 176 congeners of consumer product additives and 106 TPs. Reference standards were used to confirm the identification of 53 contaminants among them lesser-known pharmaceuticals (aliskiren, sitagliptin) and consumer product additives (lauramidopropyl betaine, 2,2,4-trimethyl-1,2-dihydroquinoline). The MNC tools allowed to group similar TPs and congeners together. As such, several previously unknown TPs of pesticides (metolachlor) and pharmaceuticals (gliclazide, irbesartan) were identified as tentative candidates or probable structures. Moreover, some congeners that had no entry on global repositories (PubChem, ChemSpider) were identified as probable structures. The workflow worked efficiently with oligomers containing ethylene oxide moieties, and with TPs structurally related to their parent compounds. The top-down approach shown in this study addresses several issues with the identification of congeners of industrial compounds from CCMs. Furthermore, it allows elucidating the structure of TPs directly from samples without relying on bottom-up studies under conditions discussed herein. The top-down workflow and the MNC tools show great potential for data mining and retrospective analysis of previous NTS studies.

Alkyl-Quinolones derivatives as potential biomarkers for Pseudomonas aeruginosa infection chronicity in Cystic Fibrosis

In Cystic Fibrosis (CF), a rapid and standardized definition of chronic infection would allow a better management of Pseudomonas aeruginosa (Pa) infections, as well as a quick grouping of patients during clinical trials allowing better comparisons between studies. With this purpose, we compared the metabolic profiles of 44 in vitro cultures of Pa strains isolated from CF patients at different stages of infection in order to identify metabolites differentially synthetized according to these clinical stages. Compounds produced and secreted by each strain in the supernatant of a liquid culture were analysed by metabolomic approaches (UHPLC-DAD-ESI/QTOF, UV and UPLC-Orbitrap, MS). Multivariate analyses showed that first colonization strains could be differentiated from chronic colonization ones, by producing notably more Alkyl-Quinolones (AQs) derivatives. Especially, five AQs were discriminant: HQC5, HQNOC7, HQNOC7:1, db-PQS C9 and HQNOC9:1. However, the production of HHQ was equivalent between strain types. The HHQ/HQNOC9:1 ratio was then found to be significantly different between chronic and primo-colonising strains by using both UV (p = 0.003) and HRMS data (p = 1.5 × 10^-5). Our study suggests that some AQ derivatives can be used as biomarkers for an improved management of CF patients as well as a better definition of the clinical stages of Pa infection.

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree

Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.

Production of the antimicrobial compound tetrabromopyrrole and the Pseudomonas quinolone system precursor, 2-heptyl-4-quinolone, by a novel marine species Pseudoalteromonas galatheae sp. nov

Novel antimicrobials are urgently needed due to the rapid spread of antibiotic resistant bacteria. In a genome-wide analysis of Pseudoalteromonas strains, one strain (S4498) was noticed due to its potent antibiotic activity. It did not produce the yellow antimicrobial pigment bromoalterochromide, which was produced by several related type strains with which it shared less than 95% average nucleotide identity. Also, it produced a sweet-smelling volatile not observed from other strains. Mining the genome of strain S4498 using the secondary metabolite prediction tool antiSMASH led to eight biosynthetic gene clusters with no homology to known compounds, and synteny analyses revealed that the yellow pigment bromoalterochromide was likely lost during evolution. Metabolome profiling of strain S4498 using HPLC-HRMS analyses revealed marked differences to the type strains. In particular, a series of quinolones known as pseudanes were identified and verified by NMR. The characteristic odor of the strain was linked to the pseudanes. The highly halogenated compound tetrabromopyrrole was detected as the major antibacterial component by bioassay-guided fractionation. Taken together, the polyphasic analysis demonstrates that strain S4498 belongs to a novel species within the genus Pseudoalteromonas, and we propose the name Pseudoalteromonas galatheae sp. nov. (type strain S4498T = NCIMB 15250T = LMG 31599T).

Bacterial Alkyl-4-quinolones: Discovery, Structural Diversity and Biological Properties

The alkyl-4-quinolones (AQs) are a class of metabolites produced primarily by members of the Pseudomonas and Burkholderia genera, consisting of a 4-quinolone core substituted by a range of pendant groups, most commonly at the C-2 position. The history of this class of compounds dates back to the 1940s, when a range of alkylquinolones with notable antibiotic properties were first isolated from Pseudomonas aeruginosa. More recently, it was discovered that an alkylquinolone derivative, the Pseudomonas Quinolone Signal (PQS) plays a key role in bacterial communication and quorum sensing in Pseudomonas aeruginosa. Many of the best-studied examples contain simple hydrocarbon side-chains, but more recent studies have revealed a wide range of structurally diverse examples from multiple bacterial genera, including those with aromatic, isoprenoid, or sulfur-containing side-chains. In addition to their well-known antimicrobial properties, alkylquinolones have been reported with antimalarial, antifungal, antialgal, and antioxidant properties. Here we review the structural diversity and biological activity of these intriguing metabolites.

The Peptide Chain Release Factor Methyltransferase PrmC Influences the Pseudomonas aeruginosa PA14 Endo- and Exometabolome

Pseudomonas aeruginosa is one of the most important nosocomial pathogens and understanding its virulence is the key to effective control of P. aeruginosa infections. The regulatory network governing virulence factor production in P. aeruginosa is exceptionally complex. Previous studies have shown that the peptide chain release factor methyltransferase PrmC plays an important role in bacterial pathogenicity. Yet, the underlying molecular mechanism is incompletely understood. In this study, we used untargeted liquid and gas chromatography coupled to mass spectrometry to characterise the metabolome of a prmC defective P. aeruginosa PA14 strain in comparison with the corresponding strain complemented with prmC in trans. The comprehensive metabolomics data provided new insight into the influence of prmC on virulence and metabolism. prmC deficiency had broad effects on the endo- and exometabolome of P. aeruginosa PA14, with a marked decrease of the levels of aromatic compounds accompanied by reduced precursor supply from the shikimate pathway. Furthermore, a pronounced decrease of phenazine production was observed as well as lower abundance of alkylquinolones. Unexpectedly, the metabolomics data showed no prmC-dependent effect on rhamnolipid production and an increase in pyochelin levels. A putative virulence biomarker identified in a previous study was significantly less abundant in the prmC deficient strain.

hcapca: Automated Hierarchical Clustering and Principal Component Analysis of Large Metabolomic Datasets in R

Microbial natural product discovery programs face two main challenges today: rapidly prioritizing strains for discovering new molecules and avoiding the rediscovery of already known molecules. Typically, these problems have been tackled using biological assays to identify promising strains and techniques that model variance in a dataset such as PCA to highlight novel chemistry. While these tools have shown successful outcomes in the past, datasets are becoming much larger and require a new approach. Since PCA models are dependent on the members of the group being modeled, large datasets with many members make it difficult to accurately model the variance in the data. Our tool, hcapca, first groups strains based on the similarity of their chemical composition, and then applies PCA to the smaller sub-groups yielding more robust PCA models. This allows for scalable chemical comparisons among hundreds of strains with thousands of molecular features. As a proof of concept, we applied our open-source tool to a dataset with 1046 LCMS profiles of marine invertebrate associated bacteria and discovered three new analogs of an established anticancer agent from one promising strain.

Untargeted LC-MS Metabolomics Differentiates Between Virulent and Avirulent Clinical Strains of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a facultative pathogen that can cause, inter alia, acute or chronic pneumonia in predisposed individuals. The gram-negative bacterium displays considerable genomic and phenotypic diversity that is also shaped by small molecule secondary metabolites. The discrimination of virulence phenotypes is highly relevant to the diagnosis and prognosis of P. aeruginosa infections. In order to discover small molecule metabolites that distinguish different virulence phenotypes of P. aeruginosa, 35 clinical strains were cultivated under standard conditions, characterized in terms of virulence and biofilm phenotype, and their metabolomes were investigated by untargeted liquid chromatography-mass spectrometry. The data was both mined for individual candidate markers as well as used to construct statistical models to infer the virulence phenotype from metabolomics data. We found that clinical strains that differed in their virulence and biofilm phenotype also had pronounced divergence in their metabolomes, as underlined by 332 features that were significantly differentially abundant with fold changes greater than 1.5 in both directions. Important virulence-associated secondary metabolites like rhamnolipids, alkyl quinolones or phenazines were found to be strongly upregulated in virulent strains. In contrast, we observed little change in primary metabolism. A hitherto novel cationic metabolite with a sum formula of C₁₂H₁₅N₂ could be identified as a candidate biomarker. A random forest model was able to classify strains according to their virulence and biofilm phenotype with an area under the Receiver Operation Characteristics curve of 0.84. These findings demonstrate that untargeted metabolomics is a valuable tool to characterize P. aeruginosa virulence, and to explore interrelations between clinically important phenotypic traits and the bacterial metabolome.

CluMSID: an R package for similarity-based clustering of tandem mass spectra to aid feature annotation in metabolomics

SUMMARY

Compound identification is one of the most eminent challenges in the untargeted analysis of complex mixtures of small molecules by mass spectrometry. Similarity of tandem mass spectra can provide valuable information on putative structural similarities between known and unknown analytes and hence aids feature identification in the bioanalytical sciences. We have developed CluMSID (Clustering of MS2 spectra for metabolite identification), an R package that enables researchers to make use of tandem mass spectra and neutral loss pattern similarities as a part of their metabolite annotation workflow. CluMSID offers functions for all analysis steps from import of raw data to data mining by unsupervised multivariate methods along with respective (interactive) visualizations. A detailed tutorial with example data is provided as supplementary information.

AVAILABILITY AND IMPLEMENTATION

CluMSID is available as R package from https://github.com/tdepke/CluMSID/and from https://bioconductor.org/packages/CluMSID/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra

Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique for obtaining structural information of complex mixtures. However, just knowing the molecular masses of the mixture's constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented, giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra? We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover - unsupervised - groups of mass fragments and/or neutral losses, termed Mass2Motifs, that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases. Here, we describe how additional strategies, taking advantage of (i) combinatorial in silico matching of experimental mass features to substructures of candidate molecules, and (ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations. To make annotated Mass2Motifs available to the community, we also present MotifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an Application Programming Interface (API). MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database, we can more quickly gain insight into the constituents of complex mixtures. This will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks.

New metabolites from the marine-derived bacterium Pseudomonas sp. ZZ820R

Six previously undescribed compounds, named monaxanthones A and B, monaphenol A, monathioamide A, monaprenylindole A, and monavalerolactone A, were isolated from the culture of a marine-sourced bacterium Pseudomonas sp. ZZ820R in rice medium. Their structures were elucidated based on the HRESIMS data, NMR and MS-MS spectroscopic analyses, optical rotation and ECD calculations. Monathioamide A is an unprecedented sulfur-contained compound and monavalerolactone A represents the first example of this type of natural products. Monaprenylindole A showed antibacterial activity against methicillin-resistant Staphylococcus aureus.

VOCCluster: Untargeted Metabolomics Feature Clustering Approach for Clinical Breath Gas Chromatography/Mass Spectrometry Data

Metabolic profiling of breath analysis involves processing, alignment, scaling, and clustering of thousands of features extracted from gas chromatography/mass spectrometry (GC/MS) data from hundreds of participants. The multistep data processing is complicated, operator error-prone, and time-consuming. Automated algorithmic clustering methods that are able to cluster features in a fast and reliable way are necessary. These accelerate metabolic profiling and discovery platforms for next-generation medical diagnostic tools. Our unsupervised clustering technique, VOCCluster, prototyped in Python, handles features of deconvolved GC/MS breath data. VOCCluster was created from a heuristic ontology based on the observation of experts undertaking data processing with a suite of software packages. VOCCluster identifies and clusters groups of volatile organic compounds (VOCs) from deconvolved GC/MS breath with similar mass spectra and retention index profiles. VOCCluster was used to cluster more than 15 000 features extracted from 74 GC/MS clinical breath samples obtained from participants with cancer before and after a radiation therapy. Results were evaluated against a panel of ground truth compounds and compared to other clustering methods (DBSCAN and OPTICS) that were used in previous metabolomics studies. VOCCluster was able to cluster those features into 1081 groups (including endogenous and exogenous compounds and instrumental artifacts) with an accuracy rate of 96% (±0.04 at 95% confidence interval).

Analyzing Metabolomics Data for Environmental Health and Exposome Research

The exposome is the cumulative measure of environmental influences and associated biological responses across the life span, with critical relevance for understanding how exposures can impact human health. Metabolomics analysis of biological samples offers unique advantages for examining the exposome. Simultaneous analysis of external exposures, biological responses, and host susceptibility at a systems level can help establish links between external exposures and health outcomes. As metabolomics technologies continue to evolve for the study of the exposome, metabolomics ultimately will help provide valuable insights for exposure risk assessment, and disease prevention and management. Here, we discuss recent advances in metabolomics, and describe data processing protocols that can enable analysis of the exposome. This chapter focuses on using liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics for analysis of the exposome, including (1) preprocessing of untargeted metabolomics data, (2) identification of exposure chemicals and their metabolites, and (3) methods to establish associations between exposures and diseases.

Ecological Insights to Track Cytotoxic Compounds among Maytenus ilicifolia Living Individuals and Clones of an Ex Situ Collection

Biodiversity is key for maintenance of life and source of richness. Nevertheless, concepts such as phenotype expression are also pivotal to understand how chemical diversity varies in a living organism. Sesquiterpene pyridine alkaloids (SPAs) and quinonemethide triterpenes (QMTs) accumulate in root bark of Celastraceae plants. However, despite their known bioactive traits, there is still a lack of evidence regarding their ecological functions. Our present contribution combines analytical tools to study clones and individuals of Maytenus ilicifolia (Celastraceae) kept alive in an ex situ collection and determine whether or not these two major biosynthetic pathways could be switched on simultaneously. The relative concentration of the QMTs maytenin (1) and pristimerin (2), and the SPA aquifoliunin E1 (3) were tracked in raw extracts by HPLC-DAD and ¹H-NMR. Hierarchical Clustering Analysis (HCA) was used to group individuals according their ability to accumulate these metabolites. Semi-quantitative analysis showed an extensive occurrence of QMT in most individuals, whereas SPA was only detected in minor abundance in five samples. Contrary to QMTs, SPAs did not accumulate extensively, contradicting the hypothesis of two different biosynthetic pathways operating simultaneously. Moreover, the production of QMT varied significantly among samples of the same ex situ collection, suggesting that the terpene contents in root bark extracts were not dependent on abiotic effects. HCA results showed that QMT occurrence was high regardless of the plant age. This data disproves the hypothesis that QMT biosynthesis was age-dependent. Furthermore, clustering analysis did not group clones nor same-age samples together, which might reinforce the hypothesis over gene regulation of the biosynthesis pathways. Indeed, plants from the ex situ collection produced bioactive compounds in a singular manner, which postulates that rhizosphere environment could offer ecological triggers for phenotypical plasticity.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

Detection of adulteration in Hydrastis canadensis (goldenseal) dietary supplements via untargeted mass spectrometry-based metabolomics

Current estimates report that approximately 25% of U.S. adults use dietary supplements for medicinal purposes. Yet, regulation and transparency within the dietary supplement industry remains a challenge, and economic incentives encourage adulteration or augmentation of botanical dietary supplement products. Undisclosed changes to the dietary supplement composition could impact safety and efficacy; thus, there is a continued need to monitor possible botanical adulteration or mis-identification. Goldenseal, Hydrastis canadensis L. (Ranunculaceae), is a well-known botanical used to combat bacterial infections and digestive problems and is widely available as a dietary supplement. The goal of this study was to evaluate potential adulteration in commercial botanical products using untargeted metabolomics, with H. canadensis supplements serving as a test case. An untargeted ultraperformance liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis was performed on 35 H. canadensis commercial products. Visual inspection of the chemometric data via principal component analysis (PCA) revealed several products that were distinct from the main groupings of samples, and subsequent evaluation of contributing metabolites led to their confirmation of the outliers as originating from a non-goldenseal species or a mixture of plant materials. The obtained results demonstrate the potential for untargeted metabolomics to discriminate between multiple unknown products and predict possible adulteration.

The Alkylquinolone Repertoire of Pseudomonas aeruginosa is Linked to Structural Flexibility of the FabH-like 2-Heptyl-3-hydroxy-4(1H)-quinolone (PQS) Biosynthesis Enzyme PqsBC

Pseudomonas aeruginosa is a bacterial pathogen that causes life-threatening infections in immunocompromised patients. It produces a large armory of saturated and mono-unsaturated 2-alkyl-4(1H)-quinolones (AQs) and AQ N-oxides (AQNOs) that serve as signaling molecules to control the production of virulence factors and that are involved in membrane vesicle formation and iron chelation; furthermore, they also have, for example, antibiotic properties. It has been shown that the β-ketoacyl-acyl-carrier protein synthase III (FabH)-like heterodimeric enzyme PqsBC catalyzes the last step in the biosynthesis of the most abundant AQ congener, 2-heptyl-4(1H)-quinolone (HHQ), by condensing octanoyl-coenzyme A (CoA) with 2-aminobenzoylacetate (2-ABA), but the basis for the large number of other AQs/AQNOs produced by P. aeruginosa is not known. Here, we demonstrate that PqsBC uses different medium-chain acyl-CoAs to produce various saturated AQs/AQNOs and that it also biosynthesizes mono-unsaturated congeners. Further, we determined the structures of PqsBC in four different crystal forms at 1.5 to 2.7 Å resolution. Together with a previous report, the data reveal that PqsBC adopts open, intermediate, and closed conformations that alter the shape of the acyl-binding cavity and explain the promiscuity of PqsBC. The different conformations also allow us to propose a model for structural transitions that accompany the catalytic cycle of PqsBC that might have broader implications for other FabH-enzymes, for which such structural transitions have been postulated but have never been observed.

Two new 2-alkylquinolones, inhibitory to the fish skin ulcer pathogen Tenacibaculum maritimum, produced by a rhizobacterium of the genus Burkholderia sp

Exploration of rhizobacteria of the genus Burkholderia as an under-tapped resource of bioactive molecules resulted in the isolation of two new antimicrobial 2-alkyl-4-quinolones. (E)-2-(Hept-2-en-1-yl)quinolin-4(1H)-one (1) and (E)-2-(non-2-en-1-yl)quinolin-4(1H)-one (3) were isolated from the culture broth of strain MBAF1239 together with four known alkylquinolones (2 and 4-6), pyrrolnitrin (7), and BN-227 (8). The structures of 1 and 3 were unambiguously characterized using NMR spectroscopy and mass spectrometry. Compounds 1-8 inhibited the growth of the marine bacterium Tenacibaculum maritimum, an etiological agent of skin ulcers in marine fish, offering new opportunities to develop antibacterial drugs for fish farming.

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.

Stacked Injections of Biphasic Extractions for Improved Metabolomic Coverage and Sample Throughput

Omics technologies attempt to provide comprehensive coverage of their target analytes. Comprehensive coverage of metabolites, the aim of nontargeted metabolomics applications, is hindered by the extreme diversity in physiochemical properties of the metabolome. One approach to deal with this challenge is the use of biphasic extractions. These methods generate two largely complementary extracts from a single sample, with an organic lipid-rich fraction and an aqueous fraction containing largely primary and secondary metabolites. To improve metabolite coverage, these two fractions are then independently analyzed resulting in a doubling of the experimental time. In this manuscript, we describe a novel injection approach, stacked injections of a biphasic extraction (SIBE), which enables simultaneous analysis of the two fractions. We demonstrate that SIBE offers nearly 3-fold more total peak area than a monophasic extract without dramatically increasing instrumentation time required for the analysis. The analytical variance is very slightly increased; however, significant improvements in retention time stability are obtained with SIBE vs monophasic injections. Collectively, these data indicate that SIBE is a viable injection approach whenever comprehensive metabolomic coverage is desired.