Precise spatial structure impacts antimicrobial susceptibility of S. aureus in polymicrobial wound infections.

A hallmark of microbial ecology is that interactions between members of a community shape community function. This includes microbial communities in human infections, such as chronic wounds, where interactions can result in more severe diseases. Staphylococcus aureus is the most common organism isolated from human chronic wound infections and has been shown to have both cooperative and competitive interactions with Pseudomonas aeruginosa. Still, despite considerable study, most interactions between these microbes have been characterized using in vitro well-mixed systems, which do not recapitulate the infection environment. Here, we characterized interactions between S. aureus and P. aeruginosa in chronic murine wounds, focusing on the role that both macro- and micro-scale spatial structures play in disease. We discovered that S. aureus and P. aeruginosa coexist at high cell densities in murine wounds. High-resolution imaging revealed that these microbes establish a patchy distribution, only occupying 5 to 25% of the wound volume. Using a quantitative framework, we identified a precise spatial structure at both the macro (mm)- and micro (µm)-scales, which was largely mediated by P. aeruginosa production of the antimicrobial 2-heptyl-4-hydroxyquinoline N-oxide, while the antimicrobial pyocyanin had no impact. Finally, we discovered that this precise spatial structure enhances S. aureus tolerance to aminoglycoside antibiotics but not vancomycin. Our results provide mechanistic insights into the biogeography of S. aureus and P. aeruginosa coinfected wounds and implicate spatial structure as a key determinant of antimicrobial tolerance in wound infections.

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.