| Autor: |
Ness M; Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States., Holmes AL; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73019, United States., Wu C; Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States., Hossain E; Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States., Ibberson CB; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73019, United States., McCall LI; Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, United States.; Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma 73019, United States.; Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, Norman, Oklahoma 73019, United States. |
| Abstrakt: |
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. |
