PAMDB: a comprehensive Pseudomonas aeruginosa metabolome database

Pseudomonas aeruginosa heme metabolites biliverdin IXβ and IXδ are integral to lifestyle adaptations associated with chronic infection

Pseudomonas aeruginosa is a versatile opportunistic pathogen requiring iron for its survival and virulence within the host. The ability to switch to heme as an iron source and away from siderophore uptake provides an advantage in chronic infection. We have recently shown the extracellular heme metabolites biliverdin IXβ (BVIXβ) and BVIXδ positively regulate the heme-dependent cell surface signaling cascade. We further investigated the role of BVIXβ and BVIXδ in cell signaling utilizing allelic strains lacking a functional heme oxygenase (hemOin) or one reengineered to produce BVIXα (hemOα). Compared to PAO1, both strains show a heme-dependent growth defect, decreased swarming and twitching, and less robust biofilm formation. Interestingly, the motility and biofilm defects were partially rescued on addition of exogenous BVIXβ and BVIXδ. Utilizing liquid chromatography-tandem mass spectrometry, we performed a comparative proteomics and metabolomics analysis of PAO1 versus the allelic strains in shaking and static conditions. In shaking conditions, the hemO allelic strains showed a significant increase in proteins involved in quorum sensing, phenazine production, and chemotaxis. Metabolite profiling further revealed increased levels of Pseudomonas quinolone signal and phenazine metabolites. In static conditions, we observed a significant repression of chemosensory pathways and type IV pili biogenesis proteins as well as several phosphodiesterases associated with biofilm dispersal. We propose BVIX metabolites function as signaling and chemotactic molecules integrating heme utilization as an iron source into the adaptation of P. aeruginosa from a planktonic to sessile lifestyle.

IMPORTANCE

The opportunistic pathogen Pseudomonas aeruginosa causes long-term chronic infection in the airways of cystic fibrosis patients. The ability to scavenge iron and to establish chronic infection within this environment coincides with a switch to utilize heme as the primary iron source. Herein, we show the heme metabolites biliverdin beta and delta are themselves important signaling molecules integrating the switch in iron acquisition systems with cooperative behaviors such as motility and biofilm formation that are essential for long-term chronic infection. These significant findings will enhance the development of viable multi-targeted therapeutics effective against both heme utilization and cooperative behaviors essential for survival and persistence within the host.

HILIC-IM-MS for Simultaneous Lipid and Metabolite Profiling of Bacteria

Although MALDI-ToF platforms for microbial identifications have found great success in clinical microbiology, the sole use of protein fingerprints for the discrimination of closely related species, strain-level identifications, and detection of antimicrobial resistance remains a challenge for the technology. Several alternative mass spectrometry-based methods have been proposed to address the shortcomings of the protein-centric approach, including MALDI-ToF methods for fatty acid/lipid profiling and LC-MS profiling of metabolites. However, the molecular diversity of microbial pathogens suggests that no single "ome" will be sufficient for the accurate and sensitive identification of strain- and susceptibility-level profiling of bacteria. Here, we describe the development of an alternative approach to microorganism profiling that relies upon both metabolites and lipids rather than a single class of biomolecule. Single-phase extractions based on butanol, acetonitrile, and water (the BAW method) were evaluated for the recovery of lipids and metabolites from Gram-positive and -negative microorganisms. We found that BAW extraction solutions containing 45% butanol provided optimal recovery of both molecular classes in a single extraction. The single-phase extraction method was coupled to hydrophilic interaction liquid chromatography (HILIC) and ion mobility-mass spectrometry (IM-MS) to resolve similar-mass metabolites and lipids in three dimensions and provide multiple points of evidence for feature annotation in the absence of tandem mass spectrometry. We demonstrate that the combined use of metabolites and lipids can be used to differentiate microorganisms to the species- and strain-level for four of the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Acinetobacter baumannii, and Pseudomonas aeruginosa) using data from a single ionization mode. These results present promising, early stage evidence for the use of multiomic signatures for the identification of microorganisms by liquid chromatography, ion mobility, and mass spectrometry that, upon further development, may improve upon the level of identification provided by current methods.

Untargeted metabolomics unveiled the role of butanoate metabolism in the development of Pseudomonas aeruginosa hypoxic biofilm

Pseudomonas aeruginosa is a versatile opportunistic pathogen which causes a variety of acute and chronic human infections, some of which are associated with the biofilm phenotype of the pathogen. We hypothesize that defining the intracellular metabolome of biofilm cells, compared to that of planktonic cells, will elucidate the metabolic pathways and biomarkers indicative of biofilm inception. Disc-shaped stainless-steel coupons (12.7 mm diameter) were employed as a surface for static biofilm establishment. Each disc was immersed in a well, of a 24-well microtiter plate, containing a 1-mL Lysogeny broth (LB) suspension of P. aeruginosa ATCC 9027, a strain known for its biofilm prolificacy. This setup underwent oxygen-depleted incubation at 37°C for 24 hours to yield hypoxic biofilms and the co-existing static planktonic cells. In parallel, another planktonic phenotype of ATCC 9027 was produced in LB under shaking (200 rpm) incubation at 37°C for 24 hours. Planktonic and biofilm cells were harvested, and the intracellular metabolites were subjected to global untargeted metabolomic analysis using LC-MS technology, where small metabolites (below 1.5 kDa) were selected. Data analysis showed the presence of 324 metabolites that differed (p < 0.05) in abundance between planktonic and biofilm cells, whereas 70 metabolites did not vary between these phenotypes (p > 0.05). Correlation, principal components, and partial least square discriminant analyses proved that the biofilm metabolome is distinctly clustered away from that of the two planktonic phenotypes. Based on the functional enrichment analysis, arginine and proline metabolism were enriched in planktonic cells, but butanoate metabolism was enriched in biofilm cells. Key differential metabolites within the butanoate pathway included acetoacetate, 2,3-butandiol, diacetyl, and acetoin, which were highly upregulated in the biofilm compared to the planktonic cells. Exogenous supplementation of acetoin (2 mM), a critical metabolite in butanoate metabolism, augmented biofilm mass, increased the structural integrity and thickness of the biofilm, and maintained the intracellular redox potential by balancing NADH/NAD+ ratio. In conclusion, P. aeruginosa hypoxic biofilm has a specialized metabolic landscape, and butanoate pathway is a metabolic preference and possibly required for promoting planktonic cells to the biofilm state. The butanoate pathway metabolites, particularly acetoin, could serve as markers for biofilm development.

The discovery of subunit-selective GluN1/GluN2B NMDAR antagonist via pharmacophere-based virtual screening

The incidence and mortality rates of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease, are gradually increasing worldwide. Numerous studies have demonstrated that N-methyl-D-aspartic acid receptor (NMDAR)-mediated excitotoxicity contributes to neurodegenerative diseases. Ifenprodil, a subtype-selective NMDAR antagonist, showed strong therapeutic potential. However, it suffers from low oral bioavailability and off-target side effects. In this study, natural compounds were identified for selective inhibition of GluN1/GluN2B NMDAR of human. We obtained a set of natural compounds (n = 81,366) from COCONUT, TIPdb, PAMDB, CMNPD, YMDB, and NPAtlas databases, and then virtually screened by an ifenprodil-structure-based pharmacophore model and molecular docking. The top 100 compounds were selected for binding affinity prediction via batch drug-target affinity (BatchDTA). Then, the top 50 compounds were analyzed by absorption, distribution, metabolism, excretion, toxicity (ADMET). Molecular dynamics involving molecular mechanics/position-Boltzmann surface area (MM-PBSA) calculation were performed to further screening. The top 15 compounds with strong binding affinity and ifenprodil, a proven GluN2B-selective NMDAR antagonist, were subjected to molecular dynamic simulations (100 ns), root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), H-bonds, solvent accessible surface area (SASA), principal component analysis (PCA), and binding free energy calculations. Based on these analyses, one possible lead compound carrying positive charges (CNP0099440) was identified, with great binding affinity and less off-target activity by contrast to ifenprodil. CNP0099440 has great potential to be a GluN1/GluN2B NMDAR antagonist candidate and can be further detected via in vitro and in vivo experiments.

Enzyme Databases in the Era of Omics and Artificial Intelligence

Enzyme research is important for the development of various scientific fields such as medicine and biotechnology. Enzyme databases facilitate this research by providing a wide range of information relevant to research planning and data analysis. Over the years, various databases that cover different aspects of enzyme biology (e.g., kinetic parameters, enzyme occurrence, and reaction mechanisms) have been developed. Most of the databases are curated manually, which improves reliability of the information; however, such curation cannot keep pace with the exponential growth in published data. Lack of data standardization is another obstacle for data extraction and analysis. Improving machine readability of databases is especially important in the light of recent advances in deep learning algorithms that require big training datasets. This review provides information regarding the current state of enzyme databases, especially in relation to the ever-increasing amount of generated research data and recent advancements in artificial intelligence algorithms. Furthermore, it describes several enzyme databases, providing the reader with necessary information for their use.

Synthesis of Metabolites and Metabolite-like Compounds Using Biocatalytic Systems

Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions.

Animal Metabolite Database: Metabolite Concentrations in Animal Tissues and Convenient Comparison of Quantitative Metabolomic Data

The Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60-90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies.

Mass spectrometry imaging for biosolids characterization to assess ecological or health risks before reuse

Biosolids are byproducts of wastewater treatment. With the increasing global population, the amounts of wastewater to be treated are expanding, along with the amounts of biosolids generated. The reuse of biosolids is now accepted for diversified applications in fields such as agriculture, engineering, agro-forestry. However, biosolids are known to be potential carriers of compounds that can be toxic to living beings or alter the environment. Therefore, biosolid reuse is subject to regulations, mandatory analyses are performed on heavy metals, persistent organic pollutants or pathogens. Conventional methods for the analysis of heavy metals and persistent organic pollutants are demanding, lengthy, and sometimes unsafe. Here, we propose mass spectrometry imaging as a faster and safer method using small amounts of material to monitor heavy metals and persistent organic pollutants in different types of biosolids, allowing for ecological and health risk assessment before reuse. Our methodology can be extended to other soil-like matrices.

Computationally Efficient Assembly of Pseudomonas aeruginosa Gene Expression Compendia

Thousands of Pseudomonas aeruginosa RNA sequencing (RNA-seq) gene expression profiles are publicly available via the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). In this work, the transcriptional profiles from hundreds of studies performed by over 75 research groups were reanalyzed in aggregate to create a powerful tool for hypothesis generation and testing. Raw sequence data were uniformly processed using the Salmon pseudoaligner, and this read mapping method was validated by comparison to a direct alignment method. We developed filtering criteria to exclude samples with aberrant levels of housekeeping gene expression or an unexpected number of genes with no reported values and normalized the filtered compendia using the ratio-of-medians method. The filtering and normalization steps greatly improved gene expression correlations for genes within the same operon or regulon across the 2,333 samples. Since the RNA-seq data were generated using diverse strains, we report the effects of mapping samples to noncognate reference genomes by separately analyzing all samples mapped to cDNA reference genomes for strains PAO1 and PA14, two divergent strains that were used to generate most of the samples. Finally, we developed an algorithm to incorporate new data as they are deposited into the SRA. Our processing and quality control methods provide a scalable framework for taking advantage of the troves of biological information hibernating in the depths of microbial gene expression data and yield useful tools for P. aeruginosa RNA-seq data to be leveraged for diverse research goals. IMPORTANCE Pseudomonas aeruginosa is a causative agent of a wide range of infections, including chronic infections associated with cystic fibrosis. These P. aeruginosa infections are difficult to treat and often have negative outcomes. To aid in the study of this problematic pathogen, we mapped, filtered for quality, and normalized thousands of P. aeruginosa RNA-seq gene expression profiles that were publicly available via the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). The resulting compendia facilitate analyses across experiments, strains, and conditions. Ultimately, the workflow that we present could be applied to analyses of other microbial species.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

The LOTUS initiative for open knowledge management in natural products research

Contemporary bioinformatic and chemoinformatic capabilities hold promise to reshape knowledge management, analysis and interpretation of data in natural products research. Currently, reliance on a disparate set of non-standardized, insular, and specialized databases presents a series of challenges for data access, both within the discipline and for integration and interoperability between related fields. The fundamental elements of exchange are referenced structure-organism pairs that establish relationships between distinct molecular structures and the living organisms from which they were identified. Consolidating and sharing such information via an open platform has strong transformative potential for natural products research and beyond. This is the ultimate goal of the newly established LOTUS initiative, which has now completed the first steps toward the harmonization, curation, validation and open dissemination of 750,000+ referenced structure-organism pairs. LOTUS data is hosted on Wikidata and regularly mirrored on https://lotus.naturalproducts.net. Data sharing within the Wikidata framework broadens data access and interoperability, opening new possibilities for community curation and evolving publication models. Furthermore, embedding LOTUS data into the vast Wikidata knowledge graph will facilitate new biological and chemical insights. The LOTUS initiative represents an important advancement in the design and deployment of a comprehensive and collaborative natural products knowledge base.

Translational Informatics for Natural Products as Antidepressant Agents

Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.

Rapid visualization of lipopeptides and potential bioactive groups of compounds by combining ion mobility and MALDI imaging mass spectrometry

Mass spectrometry imaging (MSI) has become a powerful method for mapping metabolite distribution in a tissue. Applied to bacterial colonies, MSI has a bright future, both for the discovery of new bioactive compounds and for a better understanding of bacterial antibiotic resistance mechanisms. Coupled with separation techniques such as ion mobility mass spectrometry (IM-MS), the identification of metabolites directly on the image is now possible and does not require additional analysis such as HPLC-MS/MS. In this article, we propose to apply a semi-targeted workflow for rapid IM-MSI data analysis focused on the search for bioactive compounds. First, chemically-related compounds showing a repetitive mass unit (i.e. lipids and lipopeptides) were targeted based on the Kendrick mass defect analysis. The detected groups of potentially bioactive compounds were then confirmed by fitting their measured ion moibilites to their measured m/z values. Using both their m/z and ion mobility values, the selected groups of compounds were identified using the available databases and finally their distribution was observed on the image. Using this workflow on a co-culture of bacteria, we were able to detect and localize bioactive compounds involved in the microbial interaction.

The Human Innate Immune Protein Calprotectin Elicits a Multimetal Starvation Response in Pseudomonas aeruginosa

To combat infections, the mammalian host limits availability of essential transition metals such as iron (Fe), zinc (Zn), and manganese (Mn) in a strategy termed "nutritional immunity." The innate immune protein calprotectin (CP) contributes to nutritional immunity by sequestering these metals to exert antimicrobial activity against a broad range of microbial pathogens. One such pathogen is Pseudomonas aeruginosa, which causes opportunistic infections in vulnerable populations, including individuals with cystic fibrosis. CP was previously shown to withhold Fe(II) and Zn(II) from P. aeruginosa and induce Fe and Zn starvation responses in this pathogen. In this work, we performed quantitative, label-free proteomics to further elucidate how CP impacts metal homeostasis pathways in P. aeruginosa. We report that CP induces an incomplete Fe starvation response, as many Fe-containing proteins that are repressed by Fe limitation are not affected by CP treatment. The Zn starvation response elicited by CP seems to be more complete than the Fe starvation response and includes increases in Zn transporters and Zn-independent proteins. CP also induces the expression of membrane-modifying proteins, and metal depletion studies indicate this response results from the sequestration of multiple metals. Moreover, the increased expression of membrane-modifying enzymes upon CP treatment correlates with increased tolerance to polymyxin B. Thus, the response of P. aeruginosa to CP treatment includes both single- and multimetal starvation responses and includes many factors related to virulence potential, broadening our understanding of this pathogen's interaction with the host. IMPORTANCE Transition metal nutrients are critical for growth and infection by all pathogens, and the innate immune system withholds these metals from pathogens to limit their growth in a strategy termed "nutritional immunity." While multimetal depletion by the host is appreciated, the majority of studies have focused on individual metals. Here, we use the innate immune protein calprotectin (CP), which complexes with several metals, including iron (Fe), zinc (Zn), and manganese (Mn), and the opportunistic pathogen Pseudomonas aeruginosa to investigate multimetal starvation. Using an unbiased label-free proteomics approach, we demonstrate that multimetal withholding by CP induces a regulatory response that is not merely additive of individual metal starvation responses, including the induction of lipid A modification proteins.

Impact of artificial sputum media formulation on Pseudomonas aeruginosa secondary metabolite production

In vitro culture media are being developed to understand how host site-specific nutrient profiles influence microbial pathogenicity and ecology. To mimic the cystic fibrosis (CF) lung environment, a variety of artificial sputum media (ASM) have been created. However, the composition of these ASM vary in the concentration of key nutrients, including amino acids, lipids, DNA, and mucin. In this work, we used feature-based molecular networking (FBMN) to perform comparative metabolomics of Pseudomonas aeruginosa, the predominant opportunistic pathogen infecting the lungs of people with CF, cultured in nine different ASM. We found that the concentration of aromatic amino acids and iron from mucin added to the media contributes to differences in the production of P. aeruginosa virulence-associated secondary metabolites.

IMPORTANCE Different media formulations aiming to replicate in vivo infection environments contain different nutrients, which affects interpretation of experimental results. Inclusion of undefined components, such as commercial porcine gastric mucin (PGM), in an otherwise chemically defined medium can alter the nutrient content of the medium in unexpected ways and influence experimental outcomes.

Facilitating Imaging Mass Spectrometry of Microbial Specialized Metabolites with METASPACE

Metabolite annotation from imaging mass spectrometry (imaging MS) data is a difficult undertaking that is extremely resource intensive. Here, we adapted METASPACE, cloud software for imaging MS metabolite annotation and data interpretation, to quickly annotate microbial specialized metabolites from high-resolution and high-mass accuracy imaging MS data. Compared with manual ion image and MS1 annotation, METASPACE is faster and, with the appropriate database, more accurate. We applied it to data from microbial colonies grown on agar containing 10 diverse bacterial species and showed that METASPACE was able to annotate 53 ions corresponding to 32 different microbial metabolites. This demonstrates METASPACE to be a useful tool to annotate the chemistry and metabolic exchange factors found in microbial interactions, thereby elucidating the functions of these molecules.

The Combination of Tradition and Future: Data-Driven Natural-Product-Based Treatments for Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder in elderly people. The personalized diagnosis and treatment remain challenges all over the world. In recent years, natural products are becoming potential therapies for many complex diseases due to their stability and low drug resistance. With the development of informatics technologies, data-driven natural product discovery and healthcare is becoming reality. For PD, however, the relevant research and tools for natural products are quite limited. Here in this review, we summarize current available databases, tools, and models for general natural product discovery and synthesis. These useful resources could be used and integrated for future PD-specific natural product investigations. At the same time, the challenges and opportunities for future natural-product-based PD care will also be discussed.

A Milk Foodomics Investigation into the Effect of Pseudomonas fluorescens Growth under Cold Chain Conditions

Pseudomonas fluorescens is a psychrotrophic species associated with milk spoilage because of its lipolytic and proteolytic activities. Consequently, monitoring P. fluorescens or its antecedent activity in milk is critical to preventing quality defects of the product and minimizing food waste. Therefore, in this study, untargeted metabolomics and peptidomics were used to identify the changes in milk related to P. fluorescens activity by simulating the low-temperature conditions usually found in milk during the cold chain. Both unsupervised and supervised multivariate statistical approaches showed a clear effect caused by the P. fluorescens inoculation on milk samples. Our results showed that the levels of phosphatidylglycerophosphates and glycerophospholipids were directly related to the level of contamination. In addition, our metabolomic approach allowed us to detect lipid and protein degradation products that were directly correlated with the degradative metabolism of P. fluorescens. Peptidomics corroborated the proteolytic propensity of P. fluorescens-contaminated milk, but with lower sensitivity. The results obtained from this study provide insights into the alterations related to P. fluorescens 39 contamination, both pre and post heat treatment. This approach could represent a potential tool to retrospectively understand the actual quality of milk under cold chain storage conditions, either before or after heat treatments.

A wide-ranging Pseudomonas aeruginosa PeptideAtlas build: A useful proteomic resource for a versatile pathogen

Pseudomonas aeruginosa is an important opportunistic human pathogen with high prevalence in nosocomial infections. This microorganism is a good model for understanding biological processes such as the quorum-sensing response, the metabolic integration of virulence, the mechanisms of global regulation of bacterial physiology, and the evolution of antibiotic resistance. Till now, P. aeruginosa proteomic data, although available in several on-line repositories, were dispersed and difficult to access. In the present work, proteomes of the PAO1 strain grown under different conditions and from diverse cellular compartments have been joined to build the Pseudomonas PeptideAtlas. This resource is a comprehensive mass spectrometry-derived peptide and inferred protein database with 71.3% coverage of the total predicted proteome of P. aeruginosa PAO1, the highest coverage among bacterial PeptideAtlas datasets. The proteins included cover 89% of metabolic proteins, 72% of proteins involved in genetic information processing, 83% of proteins responsible for environmental information processing, more than 88% of the ones related to quorum sensing and biofilm formation, and 89% of proteins responsible for antimicrobial resistance. It exemplifies a necessary tool for targeted proteomics studies, system-wide observations, and cross-species observational studies. The manuscript describes the building of the PeptideAtlas and the contribution of the different proteomic data used. SIGNIFICANCE: Pseudomonas aeruginosa is among the most versatile human bacterial pathogens. Studies of its proteome are very important as they can reveal virulence factors and mechanisms of antibiotic resistance. The construction of a proteomic resource such as the PeptideAtlas enables targeted proteomics studies, system-wide observations, and cross-species observational studies.

Discovery of Spoilage Markers for Chicken Eggs Using Liquid Chromatography-High Resolution Mass Spectrometry-Based Untargeted and Targeted Foodomics

The current approaches remain insufficient for measuring chicken egg spoilage or present analytical limitations. This study aimed to complement the existing analyses and identify novel markers using liquid chromatography-high resolution mass spectrometry-based foodomics strategies. In the discovery set, comparative untargeted metabolomics was utilized to identify marker candidates in microbially inoculated chicken eggs. Markers were annotated by spectral matching with authentic standards, experimental libraries, or in silico fragmentation. In the validation set, targeted metabolomics was employed to verify the markers in stored chicken eggs from five farms. Statistical differences at a p-value < 0.001 revealed increases in lactic and 3-hydroxybutyric acids and decreases in phosphocholine, LPE(O-18:1), LPC(16:0), and LPC(18:0) in stored eggs. Receiver operating characteristic curve analysis of the six combined markers yielded an AUC of 0.956 and a sensitivity and specificity of ∼90%. Four phospholipids were highlighted as a novel class of spoilage markers. Our findings may contribute to further industrial implementation, benefiting the quality assurance and food safety of poultry egg production.

Infrared MALDI Mass Spectrometry with Laser-Induced Postionization for Imaging of Bacterial Colonies

Ultraviolet matrix-assisted laser desorption ionization mass spectrometry imaging (UV-MALDI-MSI) is a powerful tool to visualize bacterial metabolites in microbial colonies and in biofilms. However, a challenge for the method is the efficient extraction of analytes from deeper within the bacterial colonies and from the cytoplasm of individual cells during the matrix coating step. Here, we used a pulsed infrared (IR) laser of 2.94 μm wavelength to disrupt and ablate bacterial cells without a prior coating with a MALDI matrix. Instead, tissue water or, in some experiments, in addition a small amount of glycerol was exploited for the deposition of the IR laser energy and for supporting the ionization of the analytes. Compared to water, glycerol exhibits a lower vapor pressure, which prolonged the available measurement time window within an MSI experiment. Mass spectra were acquired with a hybrid Synapt G2-S HDMS instrument at a pixel size of 120 μm. A frequency-quadrupled q-switched Nd:YAG laser with 266 nm wavelength served for laser-induced postionization (MALDI-2). In this way, the ion abundances of numerous small molecules such as nucleobases, 2-alkyl-quinolones, a prominent class of Pseudomonas aeruginosa signaling molecules involved in one of the three quorum-sensing pathways, and also the signals of various bacterial phospholipids were boosted, partially by orders of magnitude. We analyzed single and cocultured colonies of Gram-negative P. aeruginosa and of Gram-positive Bacillus subtilis and Staphylococcus aureus as exemplary bacterial systems. To enable a rapid (within 5 s) MSI-compatible steam inactivation in a custom-made autoclave filled with hot water steam, bacterial cultures were grown on porous polyamide membranes. Compared to a UV-MALDI-2-MS measurement of the same systems, mass spectra with a reduced low mass background were generally generated. This resulted in the unequivocal detection of numerous metabolites only with the IR laser. In a fundamental part of our study, and to optimize the IR-MALDI-2 approach for the highest analytical sensitivity, we characterized the expansion dynamics of the particle plume as generated by the IR laser. Here, we recorded the total ion count and the intensities of selected signals registered from P. aeruginosa samples as a function of the interlaser delay and buffer gas pressure in the ion source. The data revealed that the IR-MALDI-2 ion signals are primarily generated from slow particles having mean velocities of ∼10 m/s. Interestingly, two different pressure/delay time regimes of the optimized ionization efficiency for phospholipids and smaller metabolites, respectively, were revealed, a result pointing to yet-unknown convoluted reaction cascades. The described IR-MALDI-2 method could be a helpful new tool for a microbial mass spectrometry imaging of small molecules requiring little sample preparation.

Metabotypes of Pseudomonas aeruginosa Correlate with Antibiotic Resistance, Virulence and Clinical Outcome in Cystic Fibrosis Chronic Infections

Pseudomonas aeruginosa (P.a) is one of the most critical antibiotic resistant bacteria in the world and is the most prevalent pathogen in cystic fibrosis (CF), causing chronic lung infections that are considered one of the major causes of mortality in CF patients. Although several studies have contributed to understanding P.a within-host adaptive evolution at a genomic level, it is still difficult to establish direct relationships between the observed mutations, expression of clinically relevant phenotypes, and clinical outcomes. Here, we performed a comparative untargeted LC/HRMS-based metabolomics analysis of sequential isolates from chronically infected CF patients to obtain a functional view of P.a adaptation. Metabolic profiles were integrated with expression of bacterial phenotypes and clinical measurements following multiscale analysis methods. Our results highlighted significant associations between P.a “metabotypes”, expression of antibiotic resistance and virulence phenotypes, and frequency of clinical exacerbations, thus identifying promising biomarkers and therapeutic targets for difficult-to-treat P.a infections

Pseudomonas aeruginosa detachment from surfaces via a self-made small molecule

Pseudomonas aeruginosa is a significant threat in both healthcare and industrial biofouling. Surface attachment of P. aeruginosa is particularly problematic as surface association induces virulence and is necessary for the ensuing process of biofilm formation, which hampers antibiotic treatments. Previous efforts have searched for dispersal agents of mature biofilm collectives, but there are no known factors that specifically disperse individual surface-attached P. aeruginosa. In this study, we develop a quantitative single-cell surface-dispersal assay and use it to show that P. aeruginosa itself produces factors that can stimulate its dispersal. Through bioactivity-guided fractionation, mass spectrometry, and nuclear magnetic resonance, we elucidated the structure of one such factor, 2-methyl-4-hydroxyquinoline (MHQ). MHQ is an alkyl quinolone with a previously unknown activity and is synthesized by the PqsABC enzymes. Pure MHQ is sufficient to disperse P. aeruginosa, but the dispersal activity of natural P. aeruginosa conditioned media requires additional factors. Whereas other alkyl quinolones have been shown to act as antibiotics or membrane depolarizers, MHQ lacks these activities and known antibiotics do not induce dispersal. In contrast, we show that MHQ inhibits the activity of Type IV Pili (TFP) and that TFP targeting can explain its dispersal activity. Our work thus identifies single-cell surface dispersal as a new activity of P. aeruginosa-produced small molecules, characterizes MHQ as a promising dispersal agent, and establishes TFP inhibition as a viable mechanism for P. aeruginosa dispersal.

Static Growth Promotes PrrF and 2-Alkyl-4(1H)-Quinolone Regulation of Type VI Secretion Protein Expression in Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen that is frequently associated with both acute and chronic infections. P. aeruginosa possesses a complex regulatory network that modulates nutrient acquisition and virulence, but our knowledge of these networks is largely based on studies with shaking cultures, which are not likely representative of conditions during infection. Here, we provide proteomic, metabolic, and genetic evidence that regulation by iron, a critical metallonutrient, is altered in static P. aeruginosa cultures. Specifically, we observed a loss of iron-induced expression of proteins for oxidative phosphorylation, tricarboxylic acid (TCA) cycle metabolism under static conditions. Moreover, we identified type VI secretion as a target of iron regulation in P. aeruginosa cells under static but not shaking conditions, and we present evidence that this regulation occurs via PrrF small regulatory RNA (sRNA)-dependent production of 2-alkyl-4(1H)-quinolone metabolites. These results yield new iron regulation paradigms in an important opportunistic pathogen and highlight the need to redefine iron homeostasis in static microbial communities.IMPORTANCE Host-mediated iron starvation is a broadly conserved signal for microbial pathogens to upregulate expression of virulence traits required for successful infection. Historically, global iron regulatory studies in microorganisms have been conducted in shaking cultures to ensure culture homogeneity, yet these conditions are likely not reflective of growth during infection. Pseudomonas aeruginosa is a well-studied opportunistic pathogen and model organism for iron regulatory studies. Iron homeostasis is maintained through the Fur protein and PrrF small regulatory sRNAs, the functions of which are highly conserved in many other bacterial species. In the current study, we examined how static growth affects the known iron and PrrF regulons of P. aeruginosa, leading to the discovery of novel PrrF-regulated virulence processes. This study demonstrates how the utilization of distinct growth models can enhance our understanding of basic physiological processes that may also affect pathogenesis.

The Evanescent GacS Signal

The GacS histidine kinase is the membrane sensor of the major upstream two-component system of the regulatory Gac/Rsm signal transduction pathway. This pathway governs the expression of a wide range of genes in pseudomonads and controls bacterial fitness and motility, tolerance to stress, biofilm formation, and virulence or plant protection. Despite the importance of these roles, the ligands binding to the sensor domain of GacS remain unknown, and their identification is an exciting challenge in this domain. At high population densities, the GacS signal triggers a switch from primary to secondary metabolism and a change in bacterial lifestyle. It has been suggested, based on these observations, that the GacS signal is a marker of the emergence of nutritional stress and competition. Biochemical investigations have yet to characterize the GacS signal fully. However, they portray this cue as a low-molecular weight, relatively simple and moderately apolar metabolite possibly resembling, but nevertheless different, from the aliphatic organic acids acting as quorum-sensing signaling molecules in other Proteobacteria. Significant progress in the development of metabolomic tools and new databases dedicated to Pseudomonas metabolism should help to unlock some of the last remaining secrets of GacS induction, making it possible to control the Gac/Rsm pathway.

Review on natural products databases: where to find data in 2020

Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400,000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.

Metabolomic studies of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a common, Gram-negative environmental organism. It can be a significant pathogenic factor of severe infections in humans, especially in cystic fibrosis patients. Due to its natural resistance to antibiotics and the ability to form biofilms, infection with this pathogen can cause severe therapeutic problems. In recent years, metabolomic studies of P. aeruginosa have been performed. Therefore, in this review, we discussed recent achievements in the use of metabolomics methods in bacterial identification, differentiation, the interconnection between genome and metabolome, the influence of external factors on the bacterial metabolome and identification of new metabolites produced by P. aeruginosa. All of these studies may provide valuable information about metabolic pathways leading to an understanding of the adaptations of bacterial strains to a host environment, which can lead to new drug development and/or elaboration of new treatment and diagnostics strategies for Pseudomonas.

Proteomic Analysis of the Pseudomonas aeruginosa Iron Starvation Response Reveals PrrF Small Regulatory RNA-Dependent Iron Regulation of Twitching Motility, Amino Acid Metabolism, and Zinc Homeostasis Proteins

Iron is a critical nutrient for most microbial pathogens, and the immune system exploits this requirement by sequestering iron. The opportunistic pathogen Pseudomonas aeruginosa exhibits a high requirement for iron yet an exquisite ability to overcome iron deprivation during infection. Upon iron starvation, P. aeruginosa induces the expression of several high-affinity iron acquisition systems, as well as the PrrF small regulatory RNAs (sRNAs) that mediate an iron-sparing response. Here, we used liquid chromatography-tandem mass spectrometry to conduct proteomics of the iron starvation response of P. aeruginosa Iron starvation increased levels of multiple proteins involved in amino acid catabolism, providing the capacity for iron-independent entry of carbons into the tricarboxylic acid (TCA) cycle. Proteins involved in sulfur assimilation and cysteine biosynthesis were reduced upon iron starvation, while proteins involved in iron-sulfur cluster biogenesis were increased, highlighting the central role of iron in P. aeruginosa metabolism. Iron starvation also resulted in changes in the expression of several zinc-responsive proteins and increased levels of twitching motility proteins. Subsequent analyses provided evidence for the regulation of many of these proteins via posttranscriptional regulatory events, some of which are dependent upon the PrrF sRNAs. Moreover, we showed that iron-regulated twitching motility is partially dependent upon the prrF locus, highlighting a novel link between the PrrF sRNAs and motility. These findings add to the known impacts of iron starvation in P. aeruginosa and outline potentially novel roles for the PrrF sRNAs in iron homeostasis and pathogenesis.IMPORTANCE Iron is central for growth and metabolism of almost all microbial pathogens, and as such, this element is sequestered by the host innate immune system to restrict microbial growth. Here, we used label-free proteomics to investigate the Pseudomonas aeruginosa iron starvation response, revealing a broad landscape of metabolic and metal homeostasis changes that have not previously been described. We further provide evidence that many of these processes, including twitching motility, are regulated through the iron-responsive PrrF small regulatory RNAs. As such, this study demonstrates the power of proteomics for defining stress responses of microbial pathogens.

Microdialysis Sampling of Quorum Sensing Homoserine Lactones during Biofilm Formation

Bacteria communicate chemically through a system called quorum sensing. In this work, microdialysis sampling procedures were optimized to collect quorum sensing molecules produced during in situ biofilm formation directly on the polymeric semipermeable membrane of the microdialysis probe. V. harveyi, a Gram-negative bacterium, was used as the model organism and releases variable chain length acylhomoserine lactones (AHLs) and acyl-oxohomoserine lactones (AOHLs) as signaling molecules during quorum sensing. Eliciting biofilm formation required coating fetal bovine serum onto the poly(ether sulfone) microdialysis membrane. Dialysates were collected in different experiments either during or after biofilm formation directly on a microdialysis probe. Continuous sampling of C4-AHL, C6-AHL, C8-AHL, C6-OXO-AHL, and C12-OXO-AHL was achieved over a period of up to 4 days. The AHLs and AOHLs in dialysates were concentrated with solid-phase extraction and quantified using LC-MS. Dialysate concentrations obtained for the AOHLs and AHLs ranged between 1 and 100 ppb (ng/mL) and varied between sampling days. This work demonstrates the initial use of microdialysis sampling to collect quorum sensing signaling chemicals during biofilm formation by a Gram-negative bacterial species.

Resources for Chemical, Biological, and Structural Data on Natural Products

Natural products from plants, marine life, animals, fungi, bacteria, and other organisms remain the most productive source of inspiration for small-molecule drug discovery. Today, a wealth of information on natural products that is particularly valuable to applications in cheminformatics is at our disposal. In this contribution, we provide a timely overview of relevant resources for measured chemical, biological, and structural data on natural products. In particular, we comment on the accessibility, scope, chemical space, and limitations of the individual data sources. The bottleneck of natural products remains the limited availability of material for testing. In this context, we analyze the number of natural products readily obtainable from commercial and other sources.

The 2018 Nucleic Acids Research database issue and the online molecular biology database collection

The 2018 Nucleic Acids Research Database Issue contains 181 papers spanning molecular biology. Among them, 82 are new and 84 are updates describing resources that appeared in the Issue previously. The remaining 15 cover databases most recently published elsewhere. Databases in the area of nucleic acids include 3DIV for visualisation of data on genome 3D structure and RNArchitecture, a hierarchical classification of RNA families. Protein databases include the established SMART, ELM and MEROPS while GPCRdb and the newcomer STCRDab cover families of biomedical interest. In the area of metabolism, HMDB and Reactome both report new features while PULDB appears in NAR for the first time. This issue also contains reports on genomics resources including Ensembl, the UCSC Genome Browser and ENCODE. Update papers from the IUPHAR/BPS Guide to Pharmacology and DrugBank are highlights of the drug and drug target section while a number of proteomics databases including proteomicsDB are also covered. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been updated, reviewing 138 entries, adding 88 new resources and eliminating 47 discontinued URLs, bringing the current total to 1737 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.