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      Interkingdom metabolic transformations captured by microbial imaging mass spectrometry.

      Proceedings of the National Academy of Sciences of the United States of America
      Aspergillus fumigatus, metabolism, Chromatography, High Pressure Liquid, methods, Coinfection, microbiology, Cystic Fibrosis, Humans, Ions, Mass Spectrometry, Microbiological Techniques, Models, Biological, Pseudomonas aeruginosa, genetics, Species Specificity, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization, Tandem Mass Spectrometry, Time Factors

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          Abstract

          In polymicrobial infections, microbes can interact with both the host immune system and one another through direct contact or the secretion of metabolites, affecting disease progression and treatment options. The thick mucus in the lungs of patients with cystic fibrosis is highly susceptible to polymicrobial infections by opportunistic pathogens, including the bacterium Pseudomonas aeruginosa and the fungus Aspergillus fumigatus. Unravelling the hidden molecular interactions within such polymicrobial communities and their metabolic exchange processes will require effective enabling technologies applied to model systems. In the present study, MALDI-TOF and MALDI-FT-ICR imaging mass spectrometry (MALDI-IMS) combined with MS/MS networking were used to provide insight into the interkingdom interaction between P. aeruginosa and A. fumigatus at the molecular level. The combination of these technologies enabled the visualization and identification of metabolites secreted by these microorganisms grown on agar. A complex molecular interplay was revealed involving suppression, increased production, and biotransformation of a range of metabolites. Of particular interest is the observation that P. aeruginosa phenazine metabolites were converted by A. fumigatus into other chemical entities with alternative properties, including enhanced toxicities and the ability to induce fungal siderophores. This work highlights the capabilities of MALDI-IMS and MS/MS network analysis to study interkingdom interactions and provides insight into the complex nature of polymicrobial metabolic exchange and biotransformations.

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