Metabolomics as a tool to explore the staphylococcal biofilm

Orthopaedic infections can be polymicrobial existing as a microbiome. Infections often incorporate staphylococcal species, including Staphylococcus aureus. Such infections can lead to life threatening illness and implant failure. Furthermore, biofilm formation on the implant surface can occur, increasing pathogenicity, exacerbating antibiotic resistance and altering antimicrobial mechanism of action. Bacteria change dramatically during the transition to a biofilm growth state: phenotypically; transcriptionally; and metabolically, highlighting the need for research into molecular mechanisms involved in biofilm formation. Metabolomics can provide a tool to analyse metabolic changes which are directly related to the expressed phenotype. Here, we aimed to provide greater understanding of orthopaedic infection caused by S. aureus and biofilm formation on the implant surface. Through metagenome analysis by employing: implant material extraction; DNA extraction; microbial enrichment; and whole genome sequencing, we present a microbiome study of the infected prosthesis to resolve the causative species of orthopaedic hip infection. Results highlight the presence of S. aureus as a primary cause of orthopaedic infection along with Enterococcus faecium and the presence of secondary pathogen Clostridium difficile. Although results were hindered by the presence of host contaminating DNA even after microbial enrichment, conclusions could be made over the potential increased pathogenicity caused by the presence of a secondary pathogen and highlight method and sample preparation considerations when undertaking such a study. Following this finding, studies were focused on an orthopaedic clinical isolate of S. aureus and a metabolome extraction method for staphylococcal biofilms was developed using cell lysis through bead beating and solvent metabolome extraction. The method was found to be reproducible when coupled with liquid chromatography-mass spectrometry (LC-MS) and bioinformatics, allowing for the detection of significant changes in metabolism between planktonic and biofilm cultures to be identified and drug mechanism of actions (MOA) to be studied. Metabolomics results highlight significant changes in a number of metabolic pathways including arginine biosynthesis and purine metabolism between the two cell populations, evidence of S. aureus responding to their changing environment, including oxygen availability and a decrease in pH. Focused investigations on purine metabolism looking for biofilm modulation effects were carried out. Modulation of the S. aureus biofilm phenotype was observed through the addition of exogenous metabolites. Inosine increased biofilm biomass while formycin B, an inosine analogue, showed a dispersal effect and a potential synergistic effect in biofilm dispersal when coupled with gentamycin. Changes in metabolism between planktonic cells and biofilms highlight the requirement for antimicrobial testing to be carried out against planktonic cells and biofilms. Untargeted metabolomics was used to study the MOA of triclosan in S. aureus. The triclosan target and MOA in bacteria has already been characterised, however, questions remain over its effects in bacteria. Although the use of triclosan has come under increasing speculation, its full effects are still largely unknown. Results show that triclosan can induce a cascade of detrimental events in the cell metabolism including significant changes in amino acid metabolism, affecting planktonic cells and biofilms. Results and conclusions provide greater understanding of orthopaedic infections and specifically focus on the S. aureus biofilm, confirming S. aureus as a primary cause of orthopaedic infection and using metabolomic analysis to look at the changing state of metabolism between the different growth states. Metabolomics is a valuable tool for biofilm and drug MOA studies, helping understand orthopaedic infection and implant failure, providing crucial insight into the biochemistry of bacteria for the potential for inferences to be gained, such as the MOA of antimicrobials and the identification of novel metabolic drug targets.