A Bead Biofilm Reactor for High-Throughput Growth and Translational Applications.
Annika GilmoreMarissa BadhamWinston RudisinNicholas AshtonDustin L WilliamsPublished in: Microorganisms (2024)
Bacteria in natural ecosystems such as soil, dirt, or debris preferentially reside in the biofilm phenotype. When a traumatic injury, such as an open fracture, occurs, these naturally dwelling biofilms and accompanying foreign material can contaminate the injury site. Given their high tolerance of systemic levels of antibiotics that may be administered prophylactically, biofilms may contribute to difficult-to-treat infections. In most animal models, planktonic bacteria are used as initial inocula to cause infection, and this might not accurately mimic clinically relevant contamination and infection scenarios. Further, few approaches and systems utilize the same biofilm and accompanying substrate throughout the experimental continuum. In this study, we designed a unique reactor to grow bacterial biofilms on up to 50 silica beads that modeled environmental wound contaminants. The data obtained indicated that the reactor system repeatably produced mature Staphylococcus aureus and Pseudomonas aeruginosa biofilms on the silica beads, with an average of 5.53 and 6.21 log 10 colony-forming units per mm 2 , respectively. The bead substrates are easily manipulable for in vitro or in vivo applications, thus improving translatability. Taken together, the bead biofilm reactor presented herein may be a useful system for repeatably growing established biofilms on silica beads that could be used for susceptibility testing and as initial inocula in future animal models of trauma-related injuries.
Keyphrases
- candida albicans
- biofilm formation
- pseudomonas aeruginosa
- staphylococcus aureus
- wastewater treatment
- high throughput
- anaerobic digestion
- cystic fibrosis
- climate change
- spinal cord injury
- acinetobacter baumannii
- risk assessment
- electronic health record
- drinking water
- human health
- current status
- escherichia coli
- methicillin resistant staphylococcus aureus
- big data
- deep learning
- heavy metals
- artificial intelligence
- drug induced
- structural basis