A more attractive model inserts transcortical, extra-articular screws into the distal part of the femur, followed by intra-articular bacterial inoculation (Fig. 1-B)51. This model is easy to reproduce, employs a loaded implant, can be scaled up or down in animal size, and can use materials made of metal alloys, polyethylene, or polymethylmethacrylate. The model is limited since the implant encounters compressive loads without torsion or tension, and it does not enter the medullary canal. An older model of PJI that does separate intra-articular and intramedullary spaces places a stemmed silicone-elastomer implant in the rabbit tibia (Fig. 1-C)52. Unfortunately, the unrepresentative nature of the material used decreases enthusiasm for this approach.
A recent development utilizes a 3-dimensional (3-D) printed Ti-6Al-4V mouse implant (Fig. 1-D) that bears load in a manner similar to that of a human tibial component, osseointegrates with and without anabolic stimulus53, successfully recreates the periprosthetic space, and provides clinically relatable end points for PJI54. This model serves as an example of how novel engineering techniques and careful design considerations can lead to more accurate preclinical orthopaedic models. A decisive advantage of this approach is the capability to properly simulate any clinically based PJI treatment. This development represents an important breakthrough since no current PJI model possesses the versatility to include clinically relatable treatments.
Implant variables, including alloy composition, surface characteristics, loading profile, porosity, and stability, must be carefully considered since they all affect bacterial activity, biofilm formation, and the immune response.
Bacterial adhesion and biofilm formation on a surface is a complex process that is directly affected by the composition and charge of the implant. Bacteria use a combination of 2 to 8-nm-long lipopolysaccharide fibers and proteinaceous adhesins55,56 to interact with implant surfaces, via a balance of attractive van der Waals forces and repelling negative charges57. On the basis of this theory, many investigators have examined how surface properties modulate the adhesive process58,59. A negatively charged implant could theoretically resist bacterial adhesion through repelling the negatively charged bacterial cell60. However, this effect is mitigated by adhesion of positively charged proteins, dead cells, and modifications developed by the bacteria themselves61. Hydrophobic and hydrophilic surfaces also affect bacterial adhesion. Such effects, specifically on polished surfaces of arthroplasty implant materials, have been investigated62. Cobalt-chromium-molybdenum, with high hydrophobicity and low surface free energy, exhibits the least biofilm coverage following a static culture. After consideration of the material properties of an implant, the next variable to focus on is surface topography.
The 3-D surface topography of an implant substantially affects bacterial biofilm formation. Surface roughness improves bacterial adhesion because of increased surface area and protection from shear forces63-65. However, the effect of roughness varies across different materials: increasing roughness increases bacterial adhesion on TiZr implants66, but not on ceramic surfaces67. Moreover, the distribution of surface peaks and valleys, but not their amplitude, is more important than roughness in affecting bacterial adhesion68,69. Exciting advances in material engineering have led to the development of micrometer and nanometer-scale surface topographies. Nanostructures of various shapes prevent bacterial adhesion in materials such as silicone and polydimethylsiloxane70,71. The ability to modify the shape and volume of these nanostructures could reveal what surface features bacteria evaluate prior to adhering72. Implant surface stiffness may also affect bacterial adhesion73.
Embarking on PJI model development requires a fundamental understanding of common mechanisms that bacteria utilize to adhere to surfaces and form biofilm. Most bacteria utilize a complex array of MSCRAMMs (microbial surface components recognizing adhesive matrix molecules) to attach to both nonorganic surfaces and human matrix proteins including fibrinogen and fibronectin74. Within MSCRAMMs are enzymes called autolysins, which facilitate attachment to plastic-like surfaces and serve as anchors that adhere bacteria to implants75,76. Following adhesion, bacterial RNA transcription changes dramatically, orienting metabolism toward anaerobic processes and production of polysaccharide intercellular adhesin (PIA), a hallmark polymer of biofilm that forms the majority of the so-called slime on medical devices77,78. Bacteria then undertake species-specific processes to identify their surroundings. In gram-positive bacteria, both S. aureus and S. epidermidis utilize the same gene, accessory gene regulator (agr) locus, to produce an autoinducing peptide that permits them to identify (via a complex process referred to as “quorum sensing”) surrounding bacteria and determine if further biofilm proliferation or colony separation and spreading is necessary79-81.
Investigators developing PJI models should utilize common clinical pathogens and be aware of the similarities and differences with regard to virulence and biofilm formation. Methicillin-sensitive and methicillin-resistant S. aureus and coagulase-negative Staphylococcus species (S. epidermidis and most other Staphylococcus species) represent the most common pathogens in clinical PJI82-84. Although these species can reside on skin, the prevalence of each is remarkably different, with healthy adults carrying up to 10 strains of S. epidermidis at a time85, while some never get colonized with S. aureus86. Furthermore, S. aureus expresses multiple virulent features, including hemolysins and leukotoxins that lyse immune cells, protein A and coagulase that provide immunological disguises, and exotoxins that damage nonimmune-based host tissues87,88. Conversely, the sole virulence factor for S. epidermidis is its ability to produce biofilm77. In addition to gram-positive bacteria, gram-negative bacteria, including Escherichia coli, Enterococcus faecalis, and Pseudomonas aeruginosa, are responsible for 10% of clinical PJI82 and can produce aggressive biofilms.
After selecting a pathogen, the next step is to select an appropriate clinical isolate. This step is critical since both genetic and molecular differences among strains of the same pathogen can substantially influence experimental results and generalizability of conclusions. While the full description of every clinical isolate is beyond the scope of this review, a summary of the most commonly utilized isolates and their relevant differences are described in the Appendix.
The method of bacterial inoculation must be carefully considered. Previous PJI models either have directly administered bacteria into the operative site or have pregrown biofilm on an implant surface prior to implantation (Table I). The first method (intraoperative contamination) is preferred since the precise number of bacteria administered is known, the method is clinically representative, and it permits investigation into how a biofilm develops on an orthopaedic implant in vivo. With regard to bacterial dose, inocula administered in current animal models have ranged from 10 to 100,000-fold higher than the estimated bacterial exposure occurring clinically (<100 colony-forming units [CFU]/m3)89. Although this discrepancy exists, it should be emphasized that the minimum bacterial exposure needed to cause clinical PJI remains unknown and is dependent on host factors. Therefore, a logical approach when developing a PJI model is to first establish PJI through a supraphysiological inoculum. On verifying that an infection does occur, subsequent experimentation while decreasing the bacteria on a logarithmic scale should be performed until the minimum inoculum needed to establish PJI is found.
A critical step in establishing a PJI model is measuring quantitative variables that can describe local and systemic host response, as well as the presence and progression of infection.
Quantitative measures of systemic response to infection can be collected through indirect and direct means. Indirect means include regular measurements of weight and body temperature. Gradual, subtle weight loss is the earliest and most reliable sign of systemic deterioration90. Normal body temperature varies among species and by sex91, and is most accurately measured in small animals either rectally or through surgically implanted transmitters92. Direct measures of systemic response mostly consist of measurements of circulating inflammatory markers (interleukin [IL]-1, IL-6, and tumor necrosis factor-α) or acute phase reactants, a process familiar to clinicians for diagnosis of PJI. Inflammatory acute reactants vary across species. C-reactive protein is strongly upregulated in humans, primates, and dogs, but only minimally increased in other species93. In the rat, alpha-1 acid glycoprotein is a suitable acute phase reactant94,95. Serum amyloid A is elevated acutely in sheep, rabbits, horses, mice, dogs, and humans, making it an attractive interspecies marker93,96-99. As in humans, acute markers can be elevated following surgery or other systemic stresses and therefore must be compared with noninfected controls.
Metrics of local host response include weight-bearing assessments, imaging evaluation, and histological examination. Radiographs are useful for establishing the clinical diagnosis of PJI100 and evaluating PJI models. A popular composite score based on radiographic findings in osteomyelitis101 can be used for PJI animal models (Fig. 2-A). Use of serial, in vivo modern imaging modalities such as densitometric analysis, computed tomography, and magnetic resonance imaging can provide a more detailed analysis of bone and soft-tissue changes. However, such methods require expensive imaging equipment and may be unable to evaluate the bone-implant interface in small-animal models because of implant artifact. The local tissue response can also be evaluated at the time of implant-bone harvest using a 4-point visual score102,103. Although simple, this scale represents the intraoperative findings found in clinical PJI, including bone loss, soft-tissue involvement, and purulent exudates (Fig. 2-B).
Quantitative measurements of in vivo bacterial activity are critical components of PJI model methodology, including confirmation of the experimental bacteria within the periprosthetic space. CFUs are the fundamental unit utilized to estimate the number of viable bacteria and are determined through placing periprosthetic samples onto a plate containing bacterial agar and counting colonies that grow overnight (Fig. 3-A). The most direct, widely accepted method for assessing bacterial load within periprosthetic tissues involves homogenizing bone and soft tissue into a fine consistency that can then be plated to count CFUs48,49,104,105. A novel, indirect method to identify periprosthetic bacteria involves bioluminescent strains that produce the light-emitting pigment luciferin (Fig. 3-B). Using sensitive light detectors, luciferin-mediated light signals can be identified through animal soft tissue to indirectly demonstrate infection49,50,106. Although promising for bacterial identification, a correlation between luminescence intensity and in vivo bacterial load is unlikely to be possible because of soft-tissue density, bacterial location, and varying bacterial metabolism, which affect how much light reaches the detector. Furthermore, luminescence cannot penetrate through cortical bone, limiting the use of this technology for intramedullary infections.
Confirmation of implant biofilm both qualitatively and quantitatively is another critical metric to achieve when validating a PJI model. A traditional quantitative method involves staining the implant with crystal violet and measuring its concentration spectrophotometrically107,108 (Fig. 4-A). This method is cheap and reproducible, but it does not distinguish bacteria from biomass. The most convincing method for demonstrating viable bacteria involves agitating109-111 the implant, plating the supernatant, and counting bacterial colonies. Distinguishing live and dead bacteria can be accomplished using confocal laser scanning microscopy (CLSM)112-114 (Fig. 4-B), in which a specimen surface is treated with a fluorescent probe and then illuminated. The main drawback to CLSM is short depth of imaging, making it difficult to visualize biofilm on rough surfaces or complex 3-D shapes. The most detailed imaging of implant biofilm is provided by scanning electron microscopy (Fig. 4-C), which permits direct observation of biofilm morphology and bacteria115,116. Scanning electron microscopy can be used quantitatively, measuring the percent coverage of an implant with biofilm47. This modality is limited by its cost, sampling issues, and inability to see beneath the external layer.
Retrieving pathogens from an animal model of PJI provides an opportunity to study how exposure to the in vivo environment affects bacterial activity. Proliferation can be compared between stock bacteria and retrieved bacteria through serial measures of bacterial turbidity in culture medium117. Similar comparisons can be made with regard to antimicrobial resistance, an aspect of bacterial virulence that can develop in vivo following exposure to antibiotics118. One simple, accurate method to quantify antibiotic sensitivity is the E-test (Fig. 4-D), which simultaneously diffuses 30 different concentrations of an antibiotic onto a plate to determine the minimum concentration needed to inhibit bacterial growth119,120. Sophisticated bacterial genome and RNA analysis can be performed on retrieved samples, with techniques available to identify gene dosage effects, essential genes for virulence, sites that could produce mutations, and potential sites for experimental plasmid insertion121-125. A drawback of repetitive sampling of the same retrieved bacterial culture is that this process gradually selects for bacteria that are defective in biofilm formation126,127. One way to compensate for this “bottle effect”128 is to separately examine bacteria from the implant surface versus bacteria from the periprosthetic tissues.
Clinically representative animal models offer an appealing opportunity to improve our fundamental understanding of PJI. Creating and appraising these models requires careful consideration of how animal choice, implant design, specific bacterial strain, and appropriate outcome measurements all relate to the clinical condition. Current PJI models possess serious limitations with regard to their ability to adequately recreate the periprosthetic environment. They also fail to regularly utilize pertinent quantitative metrics when bacterial infection is described. Moreover, current models do not exhibit sufficient versatility to serve as models for PJI treatment regimens.
In response to the shortcomings of the current literature and the urgent need for translational innovation, we have provided the first set of published criteria for establishing a pertinent model of PJI and urge interested investigators to consider following the question-based thought process outlined within this review. With increasing sophistication in animal and bacteriological genetic manipulation, 3-D printing, and quantitative detection methods, investigators have no shortage of tools necessary to design compelling animal models that could yield translational benefits.
A more detailed description of common pathogens and how to choose the right clinical isolate (including references 129 through 146, which are cited only in the Appendix) is available with the online version of this article as a data supplement at jbjs.org.
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