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. M icrobes that colonize mammalian hosts can form polymicrobial communities, such as biofilms, where they establish commensual, mutualistic, competitive, or antagonistic interactions with one another and with the host. In microbial disease, this complex interplay can affect the outcome of antimicrobial therapy (1). Therefore, it is important to understand polymicrobial populations and their interactions at the molecular level.In persons with cystic fibrosis (CF), the lungs are lined with a viscous mucus layer susceptible to polymicrobial infections (2). Pseudomonas aeruginosa, a Gram-negative bacterial opportunistic pathogen, is the most prevalent and persistent microorganism (3) isolated from the sputum of CF lungs and leading cause of mortality in CF patients (4). Within the CF lung, P. aeruginosa exists in biofilm-like macrocolonies (5) and is refractory to antimicrobial agents and the host immune response (6). Aspergillus fumigatus, an opportunistic fungal pathogen, is the second-most persistent microbe in the CF lung, with a 10-57% prevalence rate (3), and is capable of causing allergic bronchopulmonary aspergillosis (7).Superinfection with both P. aeruginosa and A. fumigatus in CF patients leads to decreased pulmonary function compared with monoinfection with either microbe (8). Interestingly, however, in a pulmonary mouse model, mice coinfected with P. aeruginosa and A. fumigatus had a higher survival rate than mice...

The ability to correlate the production of specialized metabolites to the genetic capacity of the organism that produces such molecules has become an invaluable tool in aiding the discovery of biotechnologically applicable molecules. Here, we accomplish this task by matching molecular families with gene cluster families, making these correlations to 60 microbes at one time instead of connecting one molecule to one organism at a time, such as how it is traditionally done. We can correlate these families through the use of nanospray desorption electrospray ionization MS/MS, an ambient pressure MS technique, in conjunction with MS/MS networking and peptidogenomics. We matched the molecular families of peptide natural products produced by 42 bacilli and 18 pseudomonads through the generation of amino acid sequence tags from MS/MS data of specific clusters found in the MS/MS network. These sequence tags were then linked to biosynthetic gene clusters in publicly accessible genomes, providing us with the ability to link particular molecules with the genes that produced them. As an example of its use, this approach was applied to two unsequenced Pseudoalteromonas species, leading to the discovery of the gene cluster for a molecular family, the bromoalterochromides, in the previously sequenced strain P. piscicida JCM 20779 T . The approach itself is not limited to 60 related strains, because spectral networking can be readily adopted to look at molecular family-gene cluster families of hundreds or more diverse organisms in one single MS/MS network.MS/MS molecular networking | mass spectrometry | microbial ecology T ens of thousands of sequenced microbial genomes or rough drafts of genomes are available at this time, and this number is predicted to grow into the millions over the next decades. This wealth of sequence data has the potential to be used for the discovery of small bioactive molecules through genome mining (1-6). Genome mining is a process in which small molecules are discovered by predicting what compound will be genetically encoded based on the sequences of biosynthetic gene clusters. However, the process of mining genetically encoded small molecules is not keeping pace with the rate by which genome sequences are being obtained. In general, genome mining is still done one gene cluster at a time and requires many person-years of effort to annotate a single molecule. The time and significant expertise that current genome mining requires also make genome mining very expensive. In light of this extensive effort and cost, alternative approaches to genome mining and annotating specialized metabolites must be developed that not only take advantage of the sequenced resources available and make it efficient to perform genome mining on a more global scale but also enable the molecular analysis of unsequenced organisms. Such methods will then significantly reduce the cost of genome mining by increasing the speed with which molecules are connected to candidate genes and using resources already available. Here, we put fo...

Mono-and multispecies microbial populations alter the chemistry of their surrounding environments during colony development thereby influencing multicellular behavior and interspecies interactions of neighboring microbes. Here we present a methodology that enables the creation of three-dimensional (3D) models of a microbial chemotype that can be correlated to the colony phenotype through multimodal imaging analysis. These models are generated by performing matrixassisted laser desorption ionization time-of-flight (MALDI-TOF) imaging mass spectrometry (IMS) on serial cross-sections of microbial colonies grown on 8 mm deep agar, registering data sets of each serial section in MATLAB to create a model, and then superimposing the model with a photograph of the colonies themselves. As proof-of-principle, 3D models were used to visualize metabolic exchange during microbial interactions between Bacillus subtilis and Streptomyces coelicolor, as well as, Candida albicans and Pseudomonas aeruginosa. The resulting models were able to capture the depth profile of secreted metabolites within the agar medium and revealed properties of certain mass signals that were previously not observable using two-dimensional MALDI-TOF IMS. Most significantly, the 3D models were capable of mapping previously unobserved chemical distributions within the array of sub-surface hyphae of C. albicans and how this chemistry is altered by the presence of P. aeruginosa, an opportunistic pathogen known to alter virulence of C. albicans. It was determined that the presence of C. albicans triggered increased rhamnolipid production by P. aeruginosa, which in turn was capable of inhibiting embedded hyphal growth produced beneath the C. albicans colony at ambient temperature.