Secondary metabolite profiling of Pseudomonas aeruginosa isolates reveals rare genomic traits

Pseudomonas aeruginosa is a ubiquitous Gram-negative opportunistic pathogen with remarkable phylogenetic and phenotypic variabilities. In this work, we applied classical molecular networking analysis to secondary metabolite profiling data from seven Pseudomonas aeruginosa strains, including five clinical isolates from the lung secretions of people with cystic fibrosis (CF). We provide three vignettes illustrating how secondary metabolite profiling aids in the identification of rare genomics traits in P. aeruginosa. First, we describe the identification of a previously unreported class of acyl putrescines produced by isolate mFLRO1. Secondary analysis of publicly available metabolomics data revealed that acyl putrescines are produced by <5% of P. aeruginosa strains. Second, we show that isolate SH3A does not produce di-rhamnolipids. Whole-genome sequencing and comparative genomics revealed that SH3A cannot produce di-rhamnolipids because its genome belongs to clade 5 of the P. aeruginosa phylogenetic tree. Previous phylogenetic analysis of thousands of P. aeruginosa strains concluded that <1% of publicly available genome sequences contribute to this clade. Last, we show that isolate SH1B does not produce the phenazine pyocyanin or rhamnolipids because it has a one-base insertion frameshift mutation (678insC) in the gene rhlR, which disrupts rhl-driven quorum sensing. Secondary analysis of the tens of thousands of publicly available genomes in the National Center for Biotechnology Information (NCBI) and the Pseudomonas Genome Database revealed that this mutation was present in only four P. aeruginosa genomes. Taken together, this study highlights that secondary metabolite profiling combined with genomic analysis can identify rare genetic traits of P. aeruginosa isolates.IMPORTANCESecondary metabolite profiling of five Pseudomonas aeruginosa isolates from cystic fibrosis sputum captured three traits present in <1%-5% of publicly available data, pointing to how our current library of P. aeruginosa strains may not represent the diversity within this species or the genetic variance that occurs in the CF lung.

Pyochelin biotransformation by Staphylococcus aureus shapes bacterial competition with Pseudomonas aeruginosa in polymicrobial infections

Pseudomonas aeruginosa and Staphylococcus aureus are among the most frequently isolated bacterial species from polymicrobial infections of patients with cystic fibrosis and chronic wounds. We apply mass spectrometry guided interaction studies to determine how chemical interaction shapes the fitness and community structure during co-infection of these two pathogens. We demonstrate that S. aureus is equipped with an elegant mechanism to inactivate pyochelin via the yet uncharacterized methyltransferase Spm (staphylococcal pyochelin methyltransferase). Methylation of pyochelin abolishes the siderophore activity of pyochelin and significantly lowers pyochelin-mediated intracellular reactive oxygen species (ROS) production in S. aureus. In a murine wound co-infection model, an S. aureus mutant unable to methylate pyochelin shows significantly lower fitness compared with its parental strain. Thus, Spm-mediated pyochelin methylation is a mechanism to increase S. aureus survival during in vivo competition with P. aeruginosa.

Spray-Based Application of Matrix to Agar-Based Microbial Samples for Reproducible Sample Adherence in MALDI MSI

Microbial mass spectrometry imaging (MSI) is a powerful tool used to generate biological hypotheses about the roles of metabolites in microbial competition based upon their two-dimensional spatial distribution. The most commonly used ionization method for microbial MSI is matrix-assisted laser desorption ionization (MALDI). However, medium components and microbial metabolites influence the adhesion of agar samples to the MALDI target, limiting the applicability of MALDI MSI to microbes grown on specific media. Here, we describe a three-step process using a robotic sprayer for a matrix application that improves the adherence of agar samples to the MALDI target, enabling the use of different media for microbial growth and an MSI analysis of larger sample surface areas.

Feature-Based Molecular Networking for Metabolite Annotation

The Global Natural Product Social Molecular Networking (GNPS) platform leverages tandem mass spectrometry (MS/MS) data for annotation of compounds. Molecular networks aid in the visualization of the chemical space within a metabolomics experiment. Recently, molecular networking has been combined with feature detection methods to yield Feature-Based Molecular Networking (FBMN). FBMN allows for the discrimination of isomers within the molecular network, incorporation of quantitative information generated by the feature detection tools into visualization of the molecular network, and compatibility with forthcoming in silico annotation tools. This chapter provides step-by-step methods for generating a molecular network to annotate microbial natural products using the Global Natural Product Social Molecular Networking (GNPS) Feature-Based Molecular Networking (FBMN) workflow.

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing.

Microbiome and metabolome data integration provides insight into health and disease

For much of our history, the most basic information about the microbial world has evaded characterization. Next-generation sequencing has led to a rapid increase in understanding of the structure and function of host-associated microbial communities in diverse diseases ranging from obesity to autism. Through experimental systems such as gnotobiotic mice only colonized with known microbes, a causal relationship between microbial communities and disease phenotypes has been supported. Now, microbiome research must move beyond correlations and general demonstration of causality to develop mechanistic understandings of microbial influence, including through their metabolic activities. Similar to the microbiome field, advances in technologies for cataloguing small molecules have broadened our understanding of the metabolites that populate our bodies. Integration of microbial and metabolomics data paired with experimental validation has promise for identifying microbial influence on host physiology through production, modification, or degradation of bioactive metabolites. Realization of microbial metabolic activities that affect health is hampered by gaps in our understanding of (1) biological properties of microbes and metabolites, (2) which microbial enzymes/pathways produce which metabolites, and (3) the effects of metabolites on hosts. Capitalizing on known mechanistic relationships and filling gaps in our understanding has the potential to enable translational microbiome research across disease contexts.

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

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 (MS) techniques are well-suited to high-throughput characterization of NP, 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.

Natural products as mediators of disease

Covering: up to 2016Humans are walking microbial ecosystems, each harboring a complex microbiome with the genetic potential to produce a vast array of natural products. Recent sequencing data suggest that our microbial inhabitants are critical for maintaining overall health. Shifts in microbial communities have been correlated to a number of diseases including infections, inflammation, cancer, and neurological disorders. Some of these clinically and diagnostically relevant phenotypes are a result of the presence of small molecules, yet we know remarkably little about their contributions to the health of individuals. Here, we review microbe-derived natural products as mediators of human disease.

Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

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 (MS) techniques are well-suited to high-throughput characterization of NP, 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.

Microbial, host and xenobiotic diversity in the cystic fibrosis sputum metabolome

Cystic fibrosis (CF) lungs are filled with thick mucus that obstructs airways and facilitates chronic infections. Pseudomonas aeruginosa is a significant pathogen of this disease that produces a variety of toxic small molecules. We used molecular networking-based metabolomics to investigate the chemistry of CF sputa and assess how the microbial molecules detected reflect the microbiome and clinical culture history of the patients. Metabolites detected included xenobiotics, P. aeruginosa specialized metabolites and host sphingolipids. The clinical culture and microbiome profiles did not correspond to the detection of P. aeruginosa metabolites in the same samples. The P. aeruginosa molecules that were detected in sputum did not match those from laboratory cultures. The pseudomonas quinolone signal (PQS) was readily detectable from cultured strains, but absent from sputum, even when its precursor molecules were present. The lack of PQS production in vivo is potentially due to the chemical nature of the CF lung environment, indicating that culture-based studies of this pathogen may not explain its behavior in the lung. The most differentially abundant molecules between CF and non-CF sputum were sphingolipids, including sphingomyelins, ceramides and lactosylceramide. As these highly abundant molecules contain the inflammatory mediator ceramide, they may have a significant role in CF hyperinflammation. This study demonstrates that the chemical makeup of CF sputum is a complex milieu of microbial, host and xenobiotic molecules. Detection of a bacterium by clinical culturing and 16S rRNA gene profiling do not necessarily reflect the active production of metabolites from that bacterium in a sputum sample.

Mass Spectrometry Analysis of Pseudomonas aeruginosa Treated with Azithromycin

In microbiology, changes in specialized metabolite production (cell-to-cell signaling metabolites, virulence factors, and natural products) are measured using phenotypic assays. However, advances in mass spectrometry-based techniques including imaging mass spectrometry (IMS) now allow researchers to directly visualize the production of specialized metabolites from microbial colony biofilms. In this study, a combination of IMS and liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to visualize the effect of the macrolide antibiotic azithromycin (AZM) on colony biofilms of Pseudomonas aeruginosa. Although previous research suggested that AZM may inhibit cell-to-cell signaling of P. aeruginosa and thereby reduce pathogenicity, we observed no clear decrease in specialized metabolite production.

Microbial metabolic exchange in 3D

Mono- and multispecies microbial populations alter the chemistry of their surrounding environments during colony development thereby influencing multicellular behavior and interspecies interactions of neighboring microbes. Here we present a methodology that enables the creation of three-dimensional (3D) models of a microbial chemotype that can be correlated to the colony phenotype through multimodal imaging analysis. These models are generated by performing matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) imaging mass spectrometry (IMS) on serial cross-sections of microbial colonies grown on 8 mm deep agar, registering data sets of each serial section in MATLAB to create a model, and then superimposing the model with a photograph of the colonies themselves. As proof-of-principle, 3D models were used to visualize metabolic exchange during microbial interactions between Bacillus subtilis and Streptomyces coelicolor, as well as, Candida albicans and Pseudomonas aeruginosa. The resulting models were able to capture the depth profile of secreted metabolites within the agar medium and revealed properties of certain mass signals that were previously not observable using two-dimensional MALDI-TOF IMS. Most significantly, the 3D models were capable of mapping previously unobserved chemical distributions within the array of sub-surface hyphae of C. albicans and how this chemistry is altered by the presence of P. aeruginosa, an opportunistic pathogen known to alter virulence of C. albicans. It was determined that the presence of C. albicans triggered increased rhamnolipid production by P. aeruginosa, which in turn was capable of inhibiting embedded hyphal growth produced beneath the C. albicans colony at ambient temperature.

Microbial metabolic exchange--the chemotype-to-phenotype link

The function of microbial interactions is to enable microorganisms to survive by establishing a homeostasis between microbial neighbors and local environments. A microorganism can respond to environmental stimuli using metabolic exchange-the transfer of molecular factors, including small molecules and proteins. Microbial interactions not only influence the survival of the microbes but also have roles in morphological and developmental processes of the organisms themselves and their neighbors. This, in turn, shapes the entire habitat of these organisms. Here we highlight our current understanding of metabolic exchange as well as the emergence of new technologies that are allowing us to eavesdrop on microbial conversations comprising dozens to hundreds of secreted metabolites that control the behavior, survival and differentiation of members of the community. The goal of the rapidly advancing field studying multifactorial metabolic exchange is to devise a microbial 'Rosetta stone' in order to understand the language by which microbial interactions are negotiated and, ultimately, to control the outcome of these conversations.

Bacillus cereus phosphopentomutase is an alkaline phosphatase family member that exhibits an altered entry point into the catalytic cycle

Bacterial phosphopentomutases (PPMs) are alkaline phosphatase superfamily members that interconvert α-D-ribose 5-phosphate (ribose 5-phosphate) and α-D-ribose 1-phosphate (ribose 1-phosphate). We investigated the reaction mechanism of Bacillus cereus PPM using a combination of structural and biochemical studies. Four high resolution crystal structures of B. cereus PPM revealed the active site architecture, identified binding sites for the substrate ribose 5-phosphate and the activator α-D-glucose 1,6-bisphosphate (glucose 1,6-bisphosphate), and demonstrated that glucose 1,6-bisphosphate increased phosphorylation of the active site residue Thr-85. The phosphorylation of Thr-85 was confirmed by Western and mass spectroscopic analyses. Biochemical assays identified Mn(2+)-dependent enzyme turnover and demonstrated that glucose 1,6-bisphosphate treatment increases enzyme activity. These results suggest that protein phosphorylation activates the enzyme, which supports an intermolecular transferase mechanism. We confirmed intermolecular phosphoryl transfer using an isotope relay assay in which PPM reactions containing mixtures of ribose 5-[(18)O(3)]phosphate and [U-(13)C(5)]ribose 5-phosphate were analyzed by mass spectrometry. This intermolecular phosphoryl transfer is seemingly counter to what is anticipated from phosphomutases employing a general alkaline phosphatase reaction mechanism, which are reported to catalyze intramolecular phosphoryl transfer. However, the two mechanisms may be reconciled if substrate encounters the enzyme at a different point in the catalytic cycle.

Adenylation enzyme characterization using gamma -(18)O(4)-ATP pyrophosphate exchange

We present here a rapid, highly sensitive nonradioactive assay for adenylation enzyme selectivity determination and characterization. This method measures the isotopic back exchange of unlabeled pyrophosphate into gamma-(18)O(4)-labeled ATP via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MS), electrospray ionization liquid chromatography MS, or electrospray ionization liquid chromatography-tandem MS and is demonstrated for both nonribosomal (TycA, ValA) and ribosomal synthetases (TrpRS, LysRS) of known specificity. This low-volume (6 microl) method detects as little as 0.01% (600 fmol) exchange, comparable in sensitivity to previously reported radioactive assays and readily adaptable to kinetics measurements and high throughput analysis of a wide spectrum of synthetases. Finally, a previously uncharacterized A-T didomain from anthramycin biosynthesis in the thermophile S. refuinius was demonstrated to selectively activate 4-methyl-3-hydroxyanthranilic acid at 47 degrees C, providing biochemical evidence for a new aromatic beta-amino acid activating adenylation domain and the first functional analysis of the anthramycin biosynthetic gene cluster.

Phosphonopeptide K-26 biosynthetic intermediates in Astrosporangium hypotensionis

Precursors and advanced intermediates for phosphonopeptide K-26 biosynthesis were synthesized and incorporation studies in Astrosporangium hypotensionis suggest a new mechanism of C-P bond formation in aromatic phosphonates.