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

Iron content of commercial mucin contributes to compositional stability of a cystic fibrosis airway synthetic microbiota community

In vitro culture models of mucosal environments are used to elucidate the mechanistic roles of the microbiota in human health. These models often include commercial mucins to reflect the in-situ role of mucins as an attachment site and nutrient source for the microbiota. Two types of mucins are commercially available: porcine gastric mucin (PGM) and bovine submaxillary mucin (BSM). These commercial mucins have been shown to contain iron, an essential element required by the microbiota as a co-factor for a variety of metabolic functions. In these mucin preparations, the concentration of available iron can exceed physiological concentrations present in the native environment. This unexpected source of iron influences experimental outcomes, including shaping the interactions between co-existing microbes in synthetic microbial communities used to elucidate the multispecies interactions within native microbiota. In this work, we leveraged the well-characterized iron-dependent production of secondary metabolites by the opportunistic pathogen Pseudomonas aeruginosa to aid in the development of a simple, low-cost, reproducible workflow to remove iron from commercial mucins. Using the mucosal environment of the cystic fibrosis (CF) airway as a model system, we show that P. aeruginosa is canonically responsive to iron concentration in the chemically defined synthetic CF medium complemented with semi-purified PGM, and community composition of a clinically relevant, synthetic CF airway microbial community is modulated, in part, by iron concentration in PGM.

Impact of Growth Conditions on High-Throughput Identification of Repurposing Drugs for Pseudomonas aeruginosa Cystic Fibrosis Lung Infections

Pseudomonas aeruginosa lung infections in cystic fibrosis (CF) patients represent a therapeutic challenge due to antibiotic resistance. Repurposing existing drugs is a promising approach for identifying new antimicrobials. A crucial factor in successful drug repurposing is using assay conditions that mirror the site of infection. Here, the impact of growth conditions on the anti-P. aeruginosa activity of a library of 3386 compounds was evaluated. To this, after 24 h exposure, the survival rate of CF P. aeruginosa RP73 planktonic cells was assessed spectrophotometrically under "CF-like" (artificial CF sputum, pH 6.8, 5% CO2) and enriched (Tryptone Soya Broth, pH 7.2, and aerobiosis) conditions. Among non-antibiotic compounds (n = 3127), 13.4% were active regardless of growth conditions, although only 3.2% had comparable activity; 4% and 6.2% were more active under CF-like or enriched conditions, respectively. Interestingly, 22.1% and 26.6% were active exclusively under CF-like and enriched conditions, respectively. Notably, 7 and 12 hits caused 100% killing under CF-like and enriched conditions, respectively. Among antibiotics (n = 234), 42.3% were active under both conditions, although only 18.4% showed comparable activity; 9.4% and 14.5% were more active under CF-like and enriched conditions, respectively. Interestingly, 23% and 16.6% were active exclusively under CF-like and enriched conditions, respectively. Sulphonamides showed higher activity under CF-like conditions, whereas tetracyclines, fluoroquinolones, and macrolides were more effective under enriched settings. Our findings indicated that growth conditions significantly affect the anti-P. aeruginosa activity of antibiotics and non-antibiotic drugs. Consequently, repurposing studies and susceptibility tests should be performed under physicochemical conditions that the pathogen tackles at the site of infection.

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.

Functional identification of bacterial spermine, thermospermine, norspermine, norspermidine, spermidine, and N1-aminopropylagmatine synthases

Spermine synthase is an aminopropyltransferase that adds an aminopropyl group to the essential polyamine spermidine to form tetraamine spermine, needed for normal human neural development, plant salt and drought resistance, and yeast CoA biosynthesis. We functionally identify for the first time bacterial spermine synthases, derived from phyla Bacillota, Rhodothermota, Thermodesulfobacteriota, Nitrospirota, Deinococcota, and Pseudomonadota. We also identify bacterial aminopropyltransferases that synthesize the spermine same mass isomer thermospermine, from phyla Cyanobacteriota, Thermodesulfobacteriota, Nitrospirota, Dictyoglomota, Armatimonadota, and Pseudomonadota, including the human opportunistic pathogen Pseudomonas aeruginosa. Most of these bacterial synthases were capable of synthesizing spermine or thermospermine from the diamine putrescine and so possess also spermidine synthase activity. We found that most thermospermine synthases could synthesize tetraamine norspermine from triamine norspermidine, that is, they are potential norspermine synthases. This finding could explain the enigmatic source of norspermine in bacteria. Some of the thermospermine synthases could synthesize norspermidine from diamine 1,3-diaminopropane, demonstrating that they are potential norspermidine synthases. Of 18 bacterial spermidine synthases identified, 17 were able to aminopropylate agmatine to form N1-aminopropylagmatine, including the spermidine synthase of Bacillus subtilis, a species known to be devoid of putrescine. This suggests that the N1-aminopropylagmatine pathway for spermidine biosynthesis, which bypasses putrescine, may be far more widespread than realized and may be the default pathway for spermidine biosynthesis in species encoding L-arginine decarboxylase for agmatine production. Some thermospermine synthases were able to aminopropylate N1-aminopropylagmatine to form N12-guanidinothermospermine. Our study reveals an unsuspected diversification of bacterial polyamine biosynthesis and suggests a more prominent role for agmatine.

Pseudomonas aeruginosa transcriptome analysis of metal restriction in ex vivo cystic fibrosis sputum

Chronic Pseudomonas aeruginosa lung infections are a feature of cystic fibrosis (CF) that many patients experience even with the advent of highly effective modulator therapies. Identifying factors that impact P. aeruginosa in the CF lung could yield novel strategies to eradicate infection or otherwise improve outcomes. To complement published P. aeruginosa studies using laboratory models or RNA isolated from sputum, we analyzed transcripts of strain PAO1 after incubation in sputum from different CF donors prior to RNA extraction. We compared PAO1 gene expression in this "spike-in" sputum model to that for P. aeruginosa grown in synthetic cystic fibrosis sputum medium to determine key genes, which are among the most differentially expressed or most highly expressed. Using the key genes, gene sets with correlated expression were determined using the gene expression analysis tool eADAGE. Gene sets were used to analyze the activity of specific pathways in P. aeruginosa grown in sputum from different individuals. Gene sets that we found to be more active in sputum showed similar activation in published data that included P. aeruginosa RNA isolated from sputum relative to corresponding in vitro reference cultures. In the ex vivo samples, P. aeruginosa had increased levels of genes related to zinc and iron acquisition which were suppressed by metal amendment of sputum. We also found a significant correlation between expression of the H1-type VI secretion system and CFTR corrector use by the sputum donor. An ex vivo sputum model or synthetic sputum medium formulation that imposes metal restriction may enhance future CF-related studies.IMPORTANCEIdentifying the gene expression programs used by Pseudomonas aeruginosa to colonize the lungs of people with cystic fibrosis (CF) will illuminate new therapeutic strategies. To capture these transcriptional programs, we cultured the common P. aeruginosa laboratory strain PAO1 in expectorated sputum from CF patient donors. Through bioinformatic analysis, we defined sets of genes that are more transcriptionally active in real CF sputum compared to a synthetic cystic fibrosis sputum medium. Many of the most differentially active gene sets contained genes related to metal acquisition, suggesting that these gene sets play an active role in scavenging for metals in the CF lung environment which may be inadequately represented in some models. Future studies of P. aeruginosa transcript abundance in CF may benefit from the use of an expectorated sputum model or media supplemented with factors that induce metal restriction.

Discovery of Modified Metabolites, Secondary Metabolites, and Xenobiotics by Structure-Oriented LC-MS/MS

Exogenous compounds and metabolites derived from therapeutics, microbiota, or environmental exposures directly interact with endogenous metabolic pathways, influencing disease pathogenesis and modulating outcomes of clinical interventions. With few spectral library references, the identification of covalently modified biomolecules, secondary metabolites, and xenobiotics is a challenging task using global metabolomics profiling approaches. Numerous liquid chromatography-coupled mass spectrometry (LC-MS) small molecule analytical workflows have been developed to curate global profiling experiments for specific compound groups of interest. These workflows exploit shared structural moiety, functional groups, or elemental composition to discover novel and undescribed compounds through nontargeted small molecule discovery pipelines. This Review introduces the concept of structure-oriented LC-MS discovery methodology and aims to highlight common approaches employed for the detection and characterization of covalently modified biomolecules, secondary metabolites, and xenobiotics. These approaches represent a combination of instrument-dependent and computational techniques to rapidly curate global profiling experiments to detect putative ions of interest based on fragmentation patterns, predictable phase I or phase II metabolic transformations, or rare elemental composition. Application of these methods is explored for the detection and identification of novel and undescribed biomolecules relevant to the fields of toxicology, pharmacology, and drug discovery. Continued advances in these methods expand the capacity for selective compound discovery and characterization that promise remarkable insights into the molecular interactions of exogenous chemicals with host biochemical pathways.

Metabolomic Analysis of Polymicrobial Wound Infections and an Associated Adhesive Bandage

Concerns about ion suppression, spectral contamination, or interference have led to avoidance of polymers in mass spectrometry (MS)-based metabolomics. This avoidance, however, has left many biochemical fields underexplored, including wounds, which are often treated with adhesive bandages. Here, we found that despite previous concerns, the addition of an adhesive bandage can still result in biologically informative MS data. Initially, a test LC-MS analysis was performed on a mixture of known chemical standards and a polymer bandage extract. Results demonstrated successful removal of many polymer-associated features through a data processing step. Furthermore, the bandage presence did not interfere with metabolite annotation. This method was then implemented in the context of murine surgical wound infections covered with an adhesive bandage and inoculated with Staphylococcus aureus, Pseudomonas aeruginosa, or a 1:1 mix of these pathogens. Metabolites were extracted and analyzed by LC-MS. On the bandage side, we observed a greater impact of infection on the metabolome. Distance analysis showed significant differences between all conditions and demonstrated that coinfected samples were more similar to S. aureus-infected samples compared to P. aeruginosa-infected samples. We also found that coinfection was not merely a summative effect of each monoinfection. Overall, these results represent an expansion of LC-MS-based metabolomics to a novel, previously under-investigated class of samples, leading to actionable biological information.

Analysis of Pseudomonas aeruginosa transcription in an ex vivo cystic fibrosis sputum model identifies metal restriction as a gene expression stimulus

Chronic Pseudomonas aeruginosa lung infections are a distinctive feature of cystic fibrosis (CF) pathology, that challenge adults with CF even with the advent of highly effective modulator therapies. Characterizing P. aeruginosa transcription in the CF lung and identifying factors that drive gene expression could yield novel strategies to eradicate infection or otherwise improve outcomes. To complement published P. aeruginosa gene expression studies in laboratory culture models designed to model the CF lung environment, we employed an ex vivo sputum model in which laboratory strain PAO1 was incubated in sputum from different CF donors. As part of the analysis, we compared PAO1 gene expression in this "spike-in" sputum model to that for P. aeruginosa grown in artificial sputum medium (ASM). Analyses focused on genes that were differentially expressed between sputum and ASM and genes that were most highly expressed in sputum. We present a new approach that used sets of genes with correlated expression, identified by the gene expression analysis tool eADAGE, to analyze the differential activity of pathways in P. aeruginosa grown in CF sputum from different individuals. A key characteristic of P. aeruginosa grown in expectorated CF sputum was related to zinc and iron acquisition, but this signal varied by donor sputum. In addition, a significant correlation between P. aeruginosa expression of the H1-type VI secretion system and corrector use by the sputum donor was observed. These methods may be broadly useful in looking for variable signals across clinical samples.

Cellulose Hydrogel with Hyaluronic Acid and Silver Nanoparticles: Sustained-Release Formulation with Antibacterial Properties against Pseudomonas aeruginosa

Pathogenic bacteria resistant to conventional antibiotics represent a global challenge and justify the need for new antimicrobials capable of combating bacterial multidrug resistance. This study describes the development of a topical hydrogel in a formulation composed of cellulose, hyaluronic acid (HA), and silver nanoparticles (AgNPs) against strains of Pseudomonas aeruginosa. AgNPs as an antimicrobial agent were synthesized by a new method based on green chemistry, using arginine as a reducing agent and potassium hydroxide as a carrier. Scanning electron microscopy showed the formation of a composite between cellulose and HA in a three-dimensional network of cellulose fibrils, with thickening of the fibrils and filling of spaces by HA with the presence of pores. Ultraviolet-visible spectroscopy (UV-vis) and particle size distribution for dynamic light scattering (DLS) confirmed the formation of AgNPs with peak absorption at ~430 nm and 57.88 nm. AgNPs dispersion showed a minimum inhibitory concentration (MIC) of 1.5 µg/mL. The time-kill assay showed that after 3 h of exposure to the hydrogel containing AgNPs, there were no viable cells, corresponding to a bactericidal efficacy of 99.999% in the 95% confidence level. We obtained a hydrogel that is easy to apply, with sustained release and bactericidal properties against strains of Pseudomonas aeruginosa at low concentrations of the agent.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Pseudomonas aeruginosa utilizes the host-derived polyamine spermidine to facilitate antimicrobial tolerance

Pseudomonas aeruginosa undergoes diversification during infection of the cystic fibrosis (CF) lung. Understanding these changes requires model systems that capture the complexity of the CF lung environment. We previously identified loss-of-function mutations in the 2-component regulatory system sensor kinase gene pmrB in P. aeruginosa from CF lung infections and from experimental infection of mice. Here, we demonstrate that, while such mutations lowered in vitro minimum inhibitory concentrations for multiple antimicrobial classes, this was not reflected in increased antibiotic susceptibility in vivo. Loss of PmrB impaired aminoarabinose modification of LPS, increasing the negative charge of the outer membrane and promoting uptake of cationic antimicrobials. However, in vivo, this could be offset by increased membrane binding of other positively charged molecules present in lungs. The polyamine spermidine readily coated the surface of PmrB-deficient P. aeruginosa, reducing susceptibility to antibiotics that rely on charge differences to bind the outer membrane and increasing biofilm formation. Spermidine was elevated in lungs during P. aeruginosa infection in mice and during episodes of antimicrobial treatment in people with CF. These findings highlight the need to study antimicrobial resistance under clinically relevant environmental conditions. Microbial mutations carrying fitness costs in vitro may be advantageous during infection, where host resources can be utilized.

“Omic” Approaches to Bacteria and Antibiotic Resistance Identification

The quick and accurate identification of microorganisms and the study of resistance to antibiotics is crucial in the economic and industrial fields along with medicine. One of the fastest-growing identification methods is the spectrometric approach consisting in the matrix-assisted laser ionization/desorption using a time-of-flight analyzer (MALDI-TOF MS), which has many advantages over conventional methods for the determination of microorganisms presented. Thanks to the use of a multiomic approach in the MALDI-TOF MS analysis, it is possible to obtain a broad spectrum of data allowing the identification of microorganisms, understanding their interactions and the analysis of antibiotic resistance mechanisms. In addition, the literature data indicate the possibility of a significant reduction in the time of the sample preparation and analysis time, which will enable a faster initiation of the treatment of patients. However, it is still necessary to improve the process of identifying and supplementing the existing databases along with creating new ones. This review summarizes the use of “-omics” approaches in the MALDI TOF MS analysis, including in bacterial identification and antibiotic resistance mechanisms analysis.

Enhanced Biosynthesis of Fatty Acids Contributes to Ciprofloxacin Resistance in Pseudomonas aeruginosa

Antibiotic-resistant Pseudomonas aeruginosa is insensitive to antibiotics and difficult to deal with. An understanding of the resistance mechanisms is required for the control of the pathogen. In this study, gas chromatography-mass spectrometer (GC-MS)-based metabolomics was performed to identify differential metabolomes in ciprofloxacin (CIP)-resistant P. aeruginosa strains that originated from P. aeruginosa ATCC 27853 and had minimum inhibitory concentrations (MICs) that were 16-, 64-, and 128-fold (PA-R16_CIP, PA-R64_CIP, and PA-R128_CIP, respectively) higher than the original value, compared to CIP-sensitive P. aeruginosa (PA-S). Upregulation of fatty acid biosynthesis forms a characteristic feature of the CIP-resistant metabolomes and fatty acid metabolome, which was supported by elevated gene expression and enzymatic activity in the metabolic pathway. The fatty acid synthase inhibitor triclosan potentiates CIP to kill PA-R128_CIP and clinically multidrug-resistant P. aeruginosa strains. The potentiated killing was companied with reduced gene expression and enzymatic activity and the returned abundance of fatty acids in the metabolic pathway. Consistently, membrane permeability was reduced in the PA-R and clinically multidrug-resistant P. aeruginosa strains, which were reverted by triclosan. Triclosan also stimulated the uptake of CIP. These findings highlight the importance of the elevated biosynthesis of fatty acids in the CIP resistance of P. aeruginosa and provide a target pathway for combating CIP-resistant P. aeruginosa.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Untargeted Metabolomics Reveals Species-Specific Metabolite Production and Shared Nutrient Consumption by Pseudomonas aeruginosa and Staphylococcus aureus

While bacterial metabolism is known to impact antibiotic efficacy and virulence, the metabolic capacities of individual microbes in cystic fibrosis lung infections are difficult to disentangle from sputum samples. Here, we show that untargeted metabolomic profiling of supernatants of multiple strains of Pseudomonas aeruginosa and Staphylococcus aureus grown in monoculture in synthetic cystic fibrosis media (SCFM) reveals distinct species-specific metabolic signatures despite intraspecies metabolic variability. We identify a set of 15 metabolites that were significantly consumed by both P. aeruginosa and S. aureus, suggesting that nutrient competition has the potential to impact community dynamics even in the absence of other pathogen-pathogen interactions. Finally, metabolites that were uniquely produced by one species or the other were identified. Specifically, the virulence factor precursor anthranilic acid, as well as the quinoline 2,4-quinolinediol (DHQ), were robustly produced across all tested strains of P. aeruginosa. Through the direct comparison of the extracellular metabolism of P. aeruginosa and S. aureus in a physiologically relevant environment, this work provides insight toward the potential for metabolic interactions in vivo and supports the development of species-specific diagnostic markers of infection. IMPORTANCE Interactions between P. aeruginosa and S. aureus can impact pathogenicity and antimicrobial efficacy. In this study, we aim to better understand the potential for metabolic interactions between P. aeruginosa and S. aureus in an environment resembling the cystic fibrosis lung. We find that S. aureus and P. aeruginosa consume many of the same nutrients, suggesting that metabolic competition may play an important role in community dynamics during coinfection. We further identify metabolites uniquely produced by either organism with the potential to be developed into species-specific biomarkers of infection in the cystic fibrosis lung.

Antibiotic resistance: Time of synthesis in a post-genomic age

Antibiotic resistance has been highlighted by international organizations, including World Health Organization, World Bank and United Nations, as one of the most relevant global health problems. Classical approaches to study this problem have focused in infected humans, mainly at hospitals. Nevertheless, antibiotic resistance can expand through different ecosystems and geographical allocations, hence constituting a One-Health, Global-Health problem, requiring specific integrative analytic tools. Antibiotic resistance evolution and transmission are multilayer, hierarchically organized processes with several elements (from genes to the whole microbiome) involved. However, their study has been traditionally gene-centric, each element independently studied. The development of robust-economically affordable whole genome sequencing approaches, as well as other -omic techniques as transcriptomics and proteomics, is changing this panorama. These technologies allow the description of a system, either a cell or a microbiome as a whole, overcoming the problems associated with gene-centric approaches. We are currently at the time of combining the information derived from -omic studies to have a more holistic view of the evolution and spread of antibiotic resistance. This synthesis process requires the accurate integration of -omic information into computational models that serve to analyse the causes and the consequences of acquiring AR, fed by curated databases capable of identifying the elements involved in the acquisition of resistance. In this review, we analyse the capacities and drawbacks of the tools that are currently in use for the global analysis of AR, aiming to identify the more useful targets for effective corrective interventions.