Osteomyelitis is a devastating disease caused by microbial infection of bone. While the frequency of infection following elective orthopedic surgery is low, rates of reinfection are disturbingly high. Staphylococcus aureus is responsible for the majority of chronic osteomyelitis cases and is often considered to be incurable due to bacterial persistence deep within bone. Unfortunately, there is no consensus on clinical classifications of osteomyelitis and the ensuing treatment algorithm. Given the high patient morbidity, mortality, and economic burden caused by osteomyelitis, it is important to elucidate mechanisms of bone infection to inform novel strategies for prevention and curative treatment. Recent discoveries in this field have identified three distinct reservoirs of bacterial biofilm including: Staphylococcal abscess communities in the local soft tissue and bone marrow, glycocalyx formation on implant hardware and necrotic tissue, and colonization of the osteocyte-lacuno canalicular network (OLCN) of cortical bone. In contrast, S. aureus intracellular persistence in bone cells has not been substantiated in vivo, which challenges this mode of chronic osteomyelitis. There have also been major advances in our understanding of the immune proteome against S. aureus, from clinical studies of serum antibodies and media enriched for newly synthesized antibodies (MENSA), which may provide new opportunities for osteomyelitis diagnosis, prognosis, and vaccine development. Finally, novel therapies such as antimicrobial implant coatings and antibiotic impregnated 3D-printed scaffolds represent promising strategies for preventing and managing this devastating disease. Here, we review these recent advances and highlight translational opportunities towards a cure.

Osteomyelitis is an infection of bone that can result from contiguous spread from surrounding tissue, direct bone trauma due to surgery or injury, or haematogenous spread from systemic bacteraemia. It remains a significant health-care burden with a prevalence of ~22 cases per 100,000 person-years in the United States, and its incidence has been rising over time, especially in the elderly and individuals with diabetes 1 . Although it is a heterogeneous disease, subset classifications include implant-associated osteomyelitis (including peri-prosthetic joint infection (PJI) and instrumented spinal infections), fracture-related infection, acute haematogenous osteomyelitis, diabetic foot infection, septic arthritis and native spinal osteomyelitis.Crucial to expanding our understanding of osteomyelitis and advancing treatment algorithms has been the application of animal models, which illustrate the interaction between the pathogen and cells of both the immune and skeletal systems in a manner that in vitro models cannot yet replicate. Animal models are available to study virtually all aspects of skeletal infection, and typically involve inoculation of bacteria at the time of implant placement (Fig. 1). They can vary in complexity from simple models where metal implants are placed under the skin (for example, tissue cage 2 ) or into cortical bone (for example, metal wire 3 ) versus more complex models that mimic functional orthopaedic devices 4 . Additionally, approaches have been developed to induce non-implant infections by haematogenous inoculation into the tail vein 5 , direct inoculation into vertebral bodies or intervertebral discs 6 to induce vertebral osteomyelitis, or inoculation into the foot pad of diabetic obese rodents to induce diabetic foot infection 7 .As disease pathogenesis differs across different infection classes, so does microbial aetiology. Many different microorganisms have been implicated in skeletal infection, and the most common, along with their incidence and tropism, are shown in Table 1. In general, Staphylococcus aureus and coagulase-negative staphylococci (CoNS), such as Staphylococcus epidermidis and Staphylococcus lugdunensis, are responsible for up to two-thirds of all skeletal infections, with S. aureus being the most prevalent single pathogen. Additionally, antimicrobial resistance remains a challenge in osteomyelitis treatment with up to 50% of cases of S. aureus osteomyelitis caused by methicillin-resistant S. aureus (MRSA) strains 8 . Other less commonly identified pathogens include Enterococcus spp., Pseudomonas aeruginosa, Escherichia coli and Cutibacterium acnes (Table 1). Most cases of osteomyelitis are monomicrobial; however,

S. aureus biofilm creates a favorable environment that increases antibiotic resistance, impairs host immunity, and increases tolerance to nutritional deprivation. Secreted proteins from bacterial cells within the biofilm and the quorum-sensing agr system contribute to immune evasion. Additional immunoevasive properties of S. aureus include the formation of staphylococcal abscess communities (SACs) and canalicular invasion. Novel approaches to target biofilm and increase resistance to implant colonization include novel antibiotic therapy, immunotherapy, and local implant treatments. Challenges remain given the diverse mechanisms developed by S. aureus to alter the host immune responses. Further understanding of these processes should provide novel therapeutic mechanisms to enhance eradication after PJI.

BackgroundEstimates of relationships among Staphylococcus species have been hampered by poor and inconsistent resolution of phylogenies based largely on single gene analyses incorporating only a limited taxon sample. As such, the evolutionary relationships and hierarchical classification schemes among species have not been confidently established. Here, we address these points through analyses of DNA sequence data from multiple loci (16S rRNA gene, dnaJ, rpoB, and tuf gene fragments) using multiple Bayesian and maximum likelihood phylogenetic approaches that incorporate nearly all recognized Staphylococcus taxa.ResultsWe estimated the phylogeny of fifty-seven Staphylococcus taxa using partitioned-model Bayesian and maximum likelihood analysis, as well as Bayesian gene-tree species-tree methods. Regardless of methodology, we found broad agreement among methods that the current cluster groups require revision, although there was some disagreement among methods in resolution of higher order relationships. Based on our phylogenetic estimates, we propose a refined classification for Staphylococcus with species being classified into 15 cluster groups (based on molecular data) that adhere to six species groups (based on phenotypic properties).ConclusionsOur findings are in general agreement with gene tree-based reports of the staphylococcal phylogeny, although we identify multiple previously unreported relationships among species. Our results support the general importance of such multilocus assessments as a standard in microbial studies to more robustly infer relationships among recognized and newly discovered lineages.