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Steps towards the development of MEMS biosensor
- Engineering Approaches for the Detection and Control of Orthopaedic Biofilm Infections

The critical development components upon which the fate of the Intelligent Implant system hangs are the MEMS-based biosensors. This and the integration of the various modules are expected to consume the bulk of the developmental process. As noted above, the predominant bacterial species associated with infections in artificial joints are S. aureus and S. epidermidis, which collectively account for a the majority of all implant infections. Fortunately these staphylococci use a well-characterized peptide-based quorum sensing system that is amenable to manipulation (2). Therefore, a decision was made to focus exclusively on these organisms for the development of a first-generation MEMS biosensor. Both of these species produce a quorum-sensing peptidyl autoinducer (ligand) termed RAP (Ribonucleic acid [RNA] III-activating protein) and a cognate cell-surface based receptor termed TRAP (target of RNA III-activating protein), which becomes activated through phosphorylation on binding RAP. The TRAP activation triggers up-regulation of a secondary two-component cell-signaling system encoded by the accessory gene regulator (agr) locus. Activation of agr results in production of AIP (autoinducing peptide), which is produced by cleavage of the prepeptide AgrD by the AgrB protein and AgrC, its cognate receptor encoded within the agr locus. Binding of AIP to AgrC initiates a phosphorylation cascade that results in up-regulation of RNA III synthesis from the agr locus. The RNA III is a central pleiotropic regulator that controls the expression of numerous virulence factors. The agr locus contains divergent transcriptional systems controlled by the promoters P2 and P3 that encode RNA II and RNA III, respectively. Promoter P2 is activated by the RAP-TRAP system and P3 is activated by the AIP-AgrC system. The first MEMS biosensor is being developed to detect the earliest stage of staphylococcal intercellular communication (RAP-TRAP) to provide the greatest lead time before biofilm formation for treatment.

Interference with the RAP-TRAP signaling system by the peptide RIP (RNA III-inhibiting peptide) already has been shown to produce a beneficial effect in terms of reducing staphylococcal-based pathogenesis.3 Ribonucleic acid III-inhibiting peptide is an octapeptide that is synthesized by S. warnerii and S. xylosus, coagulase-negative staphylococci. Native and synthetic forms of RIP have been shown to competitively inhibit RAP’s binding to TRAP, making RIP highly effective in inhibiting RNA III synthesis in vitro and suppressing pathogenesis in vivo.2,16 Moreover, mice vaccinated with RAP show protection from challenge with S. aureus in direct correlation with their titers of anti-RAP antibodies.16 Therefore, we have chosen the RAP-TRAP system as a validated target for the initial focus of our MEMS-based biosensor development, with the objective of creating a biosensor based on TRAP that would detect bacterially-produced RAP (Fig 2).

Ribonucleic acid III-activating protein binding to the biosensor, just as in the bacteria, would result in a conformational switch in a chimeric TRAP molecule that would activate an enzymatic moiety triggering a signal transduction cascade within the MEMS device. This cascade would result in the release of RIP and anti-RAP antibodies from the implant’s reservoirs. The three-dimensional space proximal to the prosthesis then would be flooded with a bivalent bolus of bacterial blinding agents that would prevent toxin production, biofilm development and quorum sensing by the planktonic staphylococci present in the area. Simultaneously, a second set of reservoirs would release a cocktail of potent antibiotics including, for example, nafcillin and perhaps vancomycin to kill the planktonic staphylococci. The release of the various specific signaling inhibitors and antimicrobials would, in turn, be monitored by a second set of MEMS-based sensors to ensure that an adequate release had occurred in situ. Finally, all the activity of the various biosensors and reservoirs would be stored within a memory module embedded in the implant that would be available for uploading upon signaling from a remote hand-held unit that would be provided to the patient.

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