Significance: Acinetobacter baumannii is one of the most significant hospital-acquired bacterial pathogens, able to cause life-threatening infections and develop resistance to all currently available antibiotic agents. Here, we established zebrafish as a model to study real-time interactions between innate immune cells and A. baumannii during infection. We identified a bacterial metabolic pathway that, when inhibited, leads to enhanced immune responses toward the bacteria, improving bacterial clearance and reducing severity of disease. The enhanced immune response was secondary to accumulation of a metabolic by-product, which acted as a direct, bacterial-mediated attractant of neutrophils, the key immune cell important in response to bacterial infections. These results pave the way for novel therapeutic targeting of bacterial metabolism to stimulate immune responses to fight off infection.

Combination therapy is deployed for the treatment of multidrug-resistantAcinetobacter baumannii, as it can rapidly develop resistance to current antibiotics. This is the first study to investigate the synergistic effect of colistin/doripenem combination on the metabolome ofA. baumannii. The metabolite levels were measured using LC-MS following treatment with colistin (2 mg/L) or doripenem (25 mg/L) alone, and their combination at 15 min, 1 hr and 4 hr (n = 4). Colistin caused early (15 min and 1 hr) disruption of the bacterial outer membrane and cell wall, as demonstrated by perturbation of glycerophospholipids and fatty acids. Concentrations of peptidoglycan biosynthesis metabolites decreased at 4 hr by doripenem alone, reflecting its mechanism of action. The combination induced significant changes to more key metabolic pathways relative to either monotherapy. Down-regulation of cell wall biosynthesis (via D-sedoheptulose 7-phosphate) and nucleotide metabolism (via D-ribose 5-phosphate) was associated with perturbations in the pentose phosphate pathway induced initially by colistin (15 min and 1 hr) and later by doripenem (4 hr). We discovered that the combination synergistically killedA. baumanniivia time-dependent inhibition of different key metabolic pathways. Our study highlights the significant potential of systems pharmacology in elucidating the mechanism of synergy and optimizing antibiotic pharmacokinetics/pharmacodynamics.

Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level. A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1%, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9%. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover. Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.

Multidrug-resistant Acinetobacter baumannii presents a global medical crisis and polymyxins are used as the last-line therapy. This study aimed to identify metabolic differences between polymyxin-susceptible and polymyxin-resistant A. baumannii using untargeted metabolomics. The metabolome of each A. baumannii strain was measured using liquid chromatography-mass spectrometry. Multivariate and univariate statistics and pathway analyses were employed to elucidate metabolic differences between the polymyxin-susceptible and -resistant A. baumannii strains. Significant differences were identified between the metabolic profiles of the polymyxin-susceptible and -resistant A. baumannii strains. The lipopolysaccharide (LPS) deficient, polymyxin-resistant 19606R showed perturbation in specific amino acid and carbohydrate metabolites, particularly pentose phosphate pathway (PPP) and tricarboxylic acid (TCA) cycle intermediates. Levels of nucleotides were lower in the LPS-deficient 19606R. Furthermore, 19606R exhibited a shift in its glycerophospholipid profile towards increased abundance of short-chain lipids compared to the parent polymyxin-susceptible ATCC 19606. In contrast, in a pair of clinical isolates 03–149.1 (polymyxin-susceptible) and 03–149.2 (polymyxin-resistant, due to modification of lipid A), minor metabolic differences were identified. Notably, peptidoglycan biosynthesis metabolites were significantly depleted in both of the aforementioned polymyxin-resistant strains. This is the first comparative untargeted metabolomics study to show substantial differences in the metabolic profiles of the polymyxin-susceptible and -resistant A. baumannii.

Metabolomics is a comprehensive analysis of metabolites existing in biological systems. As one of the important “omics” tools, the approach has been widely employed in various fields in helping to better understand the complex cellular metabolic states and changes. Bacterial metabolomics has gained a significant interest as bacteria serve to provide a better subject or model at systems level. The approach in metabolomics is categorized into untargeted and targeted which serves different paradigms of interest. Nevertheless, the bottleneck in metabolomics has been the sample or metabolite preparation method. A custom-made method and design for a particular species or strain of bacteria might be necessary as most studies generally refer to other bacteria or even yeast and fungi that may lead to unreliable analysis. The paramount aspect of metabolomics design comprises sample harvesting, quenching, and metabolite extraction procedures. Depending on the type of samples and research objective, each step must be at optimal conditions which are significantly important in determining the final output. To date, there are no standardized nor single designated protocols that have been established for a specific bacteria strain for untargeted and targeted approaches. In this paper, the existing and current developments of sample preparation methods of bacterial metabolomics used in both approaches are reviewed. The review also highlights previous literature of optimized conditions used to propose the most ideal methods for metabolite preparation, particularly for bacterial cells. Advantages and limitations of methods are discussed for future improvement of bacterial metabolomics.

Consumer confidence in halal integrity of the unique and various food products provides Hat Yai, Thailand a great potential for a global destination of Muslim-friendly tourism. Islam prohibits the consumption of pork and its derivatives in any food products. The issue of food adulteration and contamination, particularly in the processed halal meat products with pork and its derivatives, greatly concern Muslim consumers. The aim of this study was to detect the presence of pork DNA from processed meat products collected from self-proclaimed “halal” Muslim street food stalls at Hat Yai, Thailand. Thirty-six samples of various processed meat products were randomly collected from seven Muslim street food stalls including patties, meatballs, and sausages containing processed chicken, beef, or a mixture of various meats. The detection of the presence of pork and its derivatives was performed by a conventional polymerase chain reaction (PCR) technique based on the pork-specific primers for a conserved region in the mitochondrial (mt) 12S ribosomal RNA (rRNA) gene. The results revealed that three out of the thirty-six samples (8.3%) were positively identified to contain porcine DNA by the detection of the expected single band of size 387 bp. The DNA method conveniently provides reliable results for routine food analysis for halal requirement. Overall, the study highlights the importance of halal integrity between the producers, suppliers, and street food business owners to provide halal food products particularly to Muslim consumers.