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

Pulmonary metabolic changes in a rabbit model of Pseudomonas aeruginosa pneumonia: insights from metabolomic analysis

BACKGROUND

The current problem associated with Pseudomonas aeruginosa (PA) pneumonia, which is frequently encountered in clinical settings, is drug resistance. If Pseudomonas aeruginosa pneumonia can be rapidly diagnosed in early stage, the occurrence of drug resistance can be reduced. Therefore, our study aimed to investigate pulmonary metabolic changes associated with PA pneumonia and to identify relevant metabolic biomarkers and key metabolic pathways, providing a reference for rapid diagnosis.

METHODS

Eighteen rabbits were randomly assigned to either the PA or normal saline (NS) group. Bronchoalveolar lavage fluid (BALF) was analyzed via untargeted liquid chromatography-mass spectrometry (ULCMS) to identify and analyze differentially abundant metabolites between the groups. Univariate comparisons were performed using Student's t-test, while multivariate patterns were analyzed via principal component analysis (PCA) and orthogonal projections to latent structure-discriminant analysis(OPLS-DA).

RESULTS

Successful modeling was achieved in 17 rabbits (8 PAs, 9 NSs). The most abundant metabolite classes detected in BALF were lipids and lipid-like molecules, organoheterocyclic compounds, and benzenoids. A total of 2,451 differentially abundant metabolites were identified, including 1,205 upregulated and 1,210 downregulated metabolites. Key metabolic pathways such as histidine metabolism, arginine and proline metabolism, nucleotide metabolism, and ABC transporters were upregulated in the PA group, whereas choline metabolism in the cancer pathway was downregulated.

CONCLUSION

PA pneumonia induces distinctive metabolic alterations in the lungs, highlighting potential biomarkers and pathways that could provide valuable insights for clinical diagnosis and treatment.

CLINICAL TRIAL NUMBER

Not applicable.

Investigating the Potential of L(+)-Lactic Acid as a Green Inhibitor and Eradicator of a Dual-Species Campylobacter spp. Biofilm Formed on Food Processing Model Surfaces

Campylobacter spp. are prevalent foodborne bacterial enteric pathogens. Their inclusion in biofilms on abiotic surfaces is considered a strategy that facilitates their extraintestinal survival. Organic acid (OA) treatments could be used in a green approach to decontaminate various surfaces. This work aimed to evaluate the inhibitory and eradicative effects of L(+)-lactic acid (LA), a naturally occurring OA, on a dual-species biofilm formed on two food processing model surfaces (polystyrene and stainless steel) by three selected foodborne Campylobacter spp. isolates (two C. jejuni and one C. coli). The influence of aerobiosis conditions (microaerophilic, aerobic and CO2 enriched) on the resistance of the established biofilms to the acid was also tested. In parallel, the predominant metabolites contained in the planktonic media of biofilm monocultures and mixed-culture biofilm were comparatively analyzed by an untargeted metabolomics approach. Results revealed that LA inhibited mixed-culture biofilm formation by more than 2 logs (>99%) on both surfaces when this was applied at its highest tested concentration (4096 μg/mL; 0.34% v/v). However, all the preformed mixed-culture biofilms (ca. 106-7 CFU/cm2) could not be eradicated even when the acid was used at concentrations exceeding 5% v/v, denoting their extremely high recalcitrance which was still influenced by the abiotic substratum, and the biofilm-forming aerobiosis conditions. The metabolic analysis revealed a strain-specific metabolite production which might also be related to the strain-specific biofilm-forming and resistance behaviors and resulted in the distinct clustering of the different samples. Overall, the current findings provide important information on the effectiveness of LA against biofilm campylobacteria and may assist in mitigating their risk in the food chain.

Metabolomics-based estimation of activated sludge microbial composition and prediction of filamentous bulking

Understanding the structure and activity of activated sludge (AS) microbiome is key to ensuring optimal operation of wastewater treatment processes. While high-throughput metagenomics offers a comprehensive view of AS microbiome, its cost and time demands warrant alternative approaches. This study employed machine learning methods to integrate metabolomic and metagenomic data, enabling predictions of selected microbial abundances from metabolite profiling. Model training relied on rich microbial and metabolite abundance data collected in an intensively sampled AS system, including a period of filamentous bulking, as well as a few other AS systems. Multiple linear regression out-competed other three algorithms in achieving relatively high prediction accuracy (R2 = 0.70±0.02) for the abundances of 10 selected, either keystone or core metagenome-assembled genomes (MAGs). The model predicted the abundances of filamentous Microtrichaceae and Thiotrichaceae during bulking with an error range of 14-17.8 %. This predictive power extends beyond the specific system studied, showcasing potentials for broader applications across other AS systems. Aspartate, glycine, and folate were the most influential metabolite features contributing to model performance, which were also effective indicators for filamentous bulking, with up to one week of early warning potential. This study pioneers the application of metabolomics for fast, relatively accurate and cost-effective prediction of AS community composition, enabling proactive management of AS systems towards improved efficiency and stability.

Systems-ecology designed bacterial consortium protects from severe Clostridioides difficile infection

Fecal Microbiota Transplant (FMT) is an emerging therapy that has had remarkable success in treatment and prevention of recurrent Clostridioides difficile infection (rCDI). FMT has recently been associated with adverse outcomes such as inadvertent transfer of antimicrobial resistance, necessitating development of more targeted bacteriotherapies. To address this challenge, we developed a novel systems biology pipeline to identify candidate probiotic strains that would be predicted to interrupt C. difficile pathogenesis. Utilizing metagenomic characterization of human FMT donor samples, we identified those metabolic pathways most associated with successful FMTs and reconstructed the metabolism of encoding species to simulate interactions with C. difficile . This analysis resulted in predictions of high levels of cross-feeding for amino acids in species most associated with FMT success. Guided by these in silico models, we assembled consortia of bacteria with increased amino acid cross-feeding which were then validated in vitro . We subsequently tested the consortia in a murine model of CDI, demonstrating total protection from severe CDI through decreased toxin levels, recovered gut microbiota, and increased intestinal eosinophils. These results support the novel framework that amino acid cross-feeding is likely a critical mechanism in the initial resolution of CDI by FMT. Importantly, we conclude that our predictive platform based on predicted and testable metabolic interactions between the microbiota and C. difficile led to a rationally designed biotherapeutic framework that may be extended to other enteric infections.

Metabolomic profiling reveals bacterial metabolic adaptation strategies and new metabolites

How has metabolomics helped our understanding of infectious diseases? With the threat of antimicrobial resistance to human health around the world, metabolomics has emerged as a powerful tool to comprehensively characterize metabolic pathways to identify new drug targets. However, its output is constrained to known metabolites and their metabolic pathways. Recent advances in instrumentation, methodologies, and computational mass spectrometry have accelerated the use of metabolomics to understand pathogen-host metabolic interactions. This short review discusses a selection of recent publications using metabolomics in infectious/bacterial diseases. These studies unravel the links between metabolic adaptations to environments and host metabolic responses. Moreover, they highlight the importance of enzyme function and metabolite characterization in identifying new drug targets and biomarkers, as well as precision medicine in monitoring therapeutics and diagnosing diseases.

Human tear metabolites associated with nucleoside-signalling pathways in bacterial keratitis

OBJECTIVE

The study aimed to profile and quantify tear metabolites associated with bacterial keratitis using both untargeted and targeted metabolomic platforms.

METHODS

Untargeted metabolomic analysis using liquid-chromatography-Q Exactive-HF mass-spectrometry explored tear metabolites significantly associated with bacterial keratitis (n = 6) compared to healthy participants (n = 6). Differential statistics and principal component analysis determined meaningful metabolite differences between cases and controls. Purines and nucleosides were further quantified and compared between 15 cases and 15 controls in the targeted metabolomic platform using TSQ quantum access triple quadrupole mass spectrometry. Compound quantification was done by plotting the calibration curves and the difference in the compound levels was evaluated using the Wilcoxon rank-sum test.

RESULTS

In the untargeted analysis, 49 tear metabolites (27 upregulated and 22 downregulated) were differentially expressed between cases and controls. The untargeted analysis indicated that the purine metabolism pathway was the most affected by bacterial keratitis. Metabolite quantification in the targeted analysis further confirmed the upregulation of xanthine (P = 0.02) and downregulation of adenine (P < 0.0001), adenosine (P < 0.0001) and cytidine (P < 0.0001) in the tears of participants with bacterial keratitis compared to that of healthy participants.

CONCLUSIONS

Bacterial keratitis significantly changes the tear metabolite profile, including five major compound classes such as indoles, amino acids, nucleosides, carbohydrates, and steroids. This study also indicates that tear fluids can be used to map the metabolic pathways and uncover metabolic markers associated with bacterial keratitis. Conceivably, the inhibition of nucleoside synthesis may contribute to the pathophysiology of bacterial keratitis because nucleosides are required for maintaining cellular energy homeostasis and immune adaptability.

Lactobacillus salivarius SNK-6 Regulates Liver Lipid Metabolism Partly via the miR-130a-5p/MBOAT2 Pathway in a NAFLD Model of Laying Hens

Lactobacillus spp., as probiotics, have shown efficacy in alleviating nonalcoholic fatty liver disease (NAFLD). Here, we screened a new probiotic strain, Lactobacillus salivarius SNK-6 (L. salivarius SNK-6), which was isolated from the ileum of healthy Xinyang black-feather laying hens in China. We investigated the beneficial activity of L. salivarius SNK-6 in a NAFLD model in laying hens and found that L. salivarius SNK-6 inhibited liver fat deposition and decreased serum triglyceride levels and activity of aspartate transaminase and alanine transaminase. MBOAT2 (membrane-bound O-acyltransferase domain containing 2) was directly targeted by miR-130a-5p, which was downregulated in the liver of NAFLD laying hens but reversed after L. salivarius SNK-6 treatment. Downregulation of MBOAT2, L. salivarius SNK-6 supplementation in vivo, and L. salivarius SNK-6 cell culture treatment in vitro suppressed the mRNA expression of genes involved in the PPAR/SREBP pathway. In addition, 250 metabolites were identified in the supernatants of L. salivarius SNK-6 culture media, and most of them participated in metabolic pathways, including amino acid, carbohydrate, and lipid metabolism. Targeted metabolomic analysis revealed that acetate, butyrate, and propionate were the most abundant short-chain fatty acids, while cholic acid, ursodeoxycholic acid, chenodeoxycholic acid, and tauroursodeoxycholic acid were the four most-enriched bile acids among L. salivarius SNK-6 metabolites. This may have contributed to the reparative effect of L. salivarius SNK-6 in the NAFLD chicken model. Our study suggested that L. salivarius SNK-6 alleviated liver damage partly via the miR-130a-5p/MBOAT2 signaling pathway.

Systematic identification of molecular mediators of interspecies sensing in a community of two frequently coinfecting bacterial pathogens

Bacteria typically exist in dynamic, multispecies communities where polymicrobial interactions influence fitness. Elucidating the molecular mechanisms underlying these interactions is critical for understanding and modulating bacterial behavior in natural environments. While bacterial responses to foreign species are frequently characterized at the molecular and phenotypic level, the exogenous molecules that elicit these responses are understudied. Here, we outline a systematic strategy based on transcriptomics combined with genetic and biochemical screens of promoter-reporters to identify the molecules from one species that are sensed by another. We utilized this method to study interactions between the pathogens Pseudomonas aeruginosa and Staphylococcus aureus that are frequently found in coinfections. We discovered that P. aeruginosa senses diverse staphylococcal exoproducts including the metallophore staphylopine (StP), intermediate metabolites citrate and acetoin, and multiple molecules that modulate its iron starvation response. We observed that StP inhibits biofilm formation and that P. aeruginosa can utilize citrate and acetoin for growth, revealing that these interactions have both antagonistic and beneficial effects. Due to the unbiased nature of our approach, we also identified on a genome scale the genes in S. aureus that affect production of each sensed exoproduct, providing possible targets to modify multispecies community dynamics. Further, a combination of these identified S. aureus products recapitulated a majority of the transcriptional response of P. aeruginosa to S. aureus supernatant, validating our screening strategy. Cystic fibrosis (CF) clinical isolates of both S. aureus and P. aeruginosa also showed varying degrees of induction or responses, respectively, which suggests that these interactions are widespread among pathogenic strains. Our screening approach thus identified multiple S. aureus secreted molecules that are sensed by P. aeruginosa and affect its physiology, demonstrating the efficacy of this approach, and yielding new insight into the molecular basis of interactions between these two species.

Leveraging big data bioinformatics approaches to extract knowledge from Staphylococcus aureus public omics data

Staphylococcus aureus is a notorious pathogen posing challenges in the medical industry due to drug resistance and biofilm formation. The horizon of knowledge on S. aureus pathogenesis has expanded with the advancement of data-driven bioinformatics techniques. Mining information from sequenced genomes and their expression data is an economic approach that alleviates wastage of resources and redundancy in experiments. The current review covers how big data bioinformatics has been used in the analysis of S. aureus from publicly available -omics data to uncover mechanisms of infection and inhibition. Particularly, advances in the past two decades in biomarker discovery, host responses, phenotype identification, consolidation of information, and drug development are discussed highlighting the challenges and shortcomings. Overall, the review summarizes the diverse aspects of scrupulous re-analysis of S. aureus proteomic and transcriptomic expression datasets retrieved from public repositories in terms of the efforts taken, benefits offered, and follow-up actions. The detailed review thus serves as a reference and aid for (i) Computational biologists by briefing the approaches utilized for bacterial omics re-analysis concerning S. aureus and (ii) Experimental biologists by elucidating the potential of bioinformatics in biological research to generate reliable postulates in a prompt and economical manner.

Molecular Methodologies for Improved Polymicrobial Sepsis Diagnosis

Polymicrobial sepsis is associated with worse patient outcomes than monomicrobial sepsis. Routinely used culture-dependent microbiological diagnostic techniques have low sensitivity, often leading to missed identification of all causative organisms. To overcome these limitations, culture-independent methods incorporating advanced molecular technologies have recently been explored. However, contamination, assay inhibition and interference from host DNA are issues that must be addressed before these methods can be relied on for routine clinical use. While the host component of the complex sepsis host–pathogen interplay is well described, less is known about the pathogen’s role, including pathogen–pathogen interactions in polymicrobial sepsis. This review highlights the clinical significance of polymicrobial sepsis and addresses how promising alternative molecular microbiology methods can be improved to detect polymicrobial infections. It also discusses how the application of shotgun metagenomics can be used to uncover pathogen/pathogen interactions in polymicrobial sepsis cases and their potential role in the clinical course of this condition.