Deciphering the low abundance microbiota of presumed aseptic hip and knee implants

Unveiling Diversity and Function: Venom-Associated Microbes in Two Spiders, Heteropoda venatoria and Chilobrachys guangxiensis

Spiders are natural predators of agricultural pests, primarily due to the potent venom in their venom glands. Spider venom is compositionally complex and holds research value. This study analyzes the diversity of symbiotic bacteria in spider venom glands and venom, as well as the biological activity of culturable symbiotic bacteria. Focusing on the venom glands and venom of Heteropoda venatoria and Chilobrachys guangxiensis, we identified a diverse array of microorganisms. High-throughput sequencing detected 2151 amplicon sequence variants (ASVs), spanning 31 phyla, 75 classes, and 617 genera. A total of 125 strains of cultivable bacteria were isolated. Using the Oxford cup method, crude extracts from 46 of these strains exhibited inhibitory effects against at least one indicator bacterium. MTT (Thiazolyl blue) assays revealed that the crude extracts from 43 strains had inhibitory effects on tumor cell line MGC-803 growth. Additionally, DAPI (4',6-diamidino-2'-phenylindole) staining and flow cytometry were employed to detect cell apoptosis. The anti-inflammatory activity of nine bacterial strains was assessed using a NO assay kit and enzyme-linked immunosorbent assay (ELISA). This study further investigated the biological activity of venom, exploring the relationship between the venom and the functional activity of venom-associated bacteria.

In vitro models for studying implant-associated biofilms - A review from the perspective of bioengineering 3D microenvironments

Biofilm research has grown exponentially over the last decades, arguably due to their contribution to hospital acquired infections when they form on foreign body surfaces such as catheters and implants. Yet, translation of the knowledge acquired in the laboratory to the clinic has been slow and/or often it is not attempted by research teams to walk the talk of what is defined as 'bench to bedside'. We therefore reviewed the biofilm literature to better understand this gap. Our search revealed substantial development with respect to adapting surfaces and media used in models to mimic the clinical settings, however many of the in vitro models were too simplistic, often discounting the composition and properties of the host microenvironment and overlooking the biofilm-implant-host interactions. Failure to capture the physiological growth conditions of biofilms in vivo results in major differences between lab-grown- and clinically-relevant biofilms, particularly with respect to phenotypic profiles, virulence, and antimicrobial resistance, and they essentially impede bench-to-bedside translatability. In this review, we describe the complexity of the biological processes at the biofilm-implant-host interfaces, discuss the prerequisite for the development and characterization of biofilm models that better mimic the clinical scenario, and propose an interdisciplinary outlook of how to bioengineer biofilms in vitro by converging tissue engineering concepts and tools.

Increased risk of periprosthetic joint infection after traumatic injury in joint revision patients

BACKGROUND

Periprosthetic joint infection (PJI) is a serious complication after total joint arthroplasty (TJA). Although some risk factors of PJI were well studied, the association between trauma and PJI remains unknown in revision patients.

MATERIALS AND METHODS

Between 2015 and 2018, a total of 71 patients with trauma history before revisions (trauma cohort) were propensity score matched (PSM) at a ratio of 1 to 5 with a control cohort of revision patients without a history of trauma. Then, the cumulative incidence rate of PJI within 3 years after operation between the two groups was compared. The secondary endpoints were aseptic revisions within 3 postoperative years, complications up to 30 postoperative days, and readmission up to 90 days. During a minimal 3-year follow-up, the survival was comparatively analyzed between the trauma cohort and the control cohort.

RESULTS

The cumulative incidence of PJI was 40.85% in patients with trauma history against 27.04% in the controls (P = 0.02). Correspondingly, the cumulative incidence of aseptic re-revisions was 12.68% in patients with trauma history compared with 5.07% in the control cohort (P = 0.028). Cox regression revealed that trauma history was a risk factor of PJI (HR, 1.533 [95%CI, (1.019,2.306)]; P = 0.04) and aseptic re-revisions (HR, 3.285 [95%CI, (1.790,6.028)]; P < 0.0001).

CONCLUSIONS

Our study demonstrated that revision patients with trauma history carried a higher risk of PJI compared to those without trauma history. Moreover, after revisions, the trauma patients were still at higher risk for treatment failure due to PJI, periprosthetic joint fracture, and mechanical complications.

Characterization of periprosthetic environment microbiome in patients after total joint arthroplasty and its potential correlation with inflammation

AIMS

Periprosthetic joint infection (PJI) is one of the most serious complications after total joint arthroplasty (TJA) but the characterization of the periprosthetic environment microbiome after TJA remains unknown. Here, we performed a prospective study based on metagenomic next-generation sequencing to explore the periprosthetic microbiota in patients with suspected PJI.

METHODS

We recruited 28 patients with culture-positive PJI, 14 patients with culture-negative PJI, and 35 patients without PJI, which was followed by joint aspiration, untargeted metagenomic next-generation sequencing (mNGS), and bioinformatics analysis. Our results showed that the periprosthetic environment microbiome was significantly different between the PJI group and the non-PJI group. Then, we built a "typing system" for the periprosthetic microbiota based on the RandomForest Model. After that, the 'typing system' was verified externally.

RESULTS

We found the periprosthetic microbiota can be classified into four types generally: "Staphylococcus type," "Pseudomonas type," "Escherichia type," and "Cutibacterium type." Importantly, these four types of microbiotas had different clinical signatures, and the patients with the former two microbiota types showed obvious inflammatory responses compared to the latter ones. Based on the 2014 Musculoskeletal Infection Society (MSIS) criteria, clinical PJI was more likely to be confirmed when the former two types were encountered. In addition, the Staphylococcus spp. with compositional changes were correlated with C-reactive protein levels, the erythrocyte sedimentation rate, and the synovial fluid white blood cell count and granulocyte percentage.

CONCLUSIONS

Our study shed light on the characterization of the periprosthetic environment microbiome in patients after TJA. Based on the RandomForest model, we established a basic "typing system" for the microbiota in the periprosthetic environment. This work can provide a reference for future studies about the characterization of periprosthetic microbiota in periprosthetic joint infection patients.

Human Knee Has A Distinct Microbiome: Implications for Periprosthetic Joint Infection

INTRODUCTION

Pathogens causing prosthetic joint infection are thought to gain access to the knee during surgery or from a remote site in the body. Recent studies have shown that there is a distinct microbiome in various sites of the body. This prospective study, and first of its kind, was set up to investigate the presence of possible microbiome in human knee and compare the profile in different knee conditions.

METHODS

This transversal study prospectively obtained synovial fluid from 65 knees (55 patients) with various conditions that included normal knee, osteoarthritis, aseptic revision, and those undergoing revision for periprosthetic joint infection (PJI). The contralateral knee of patients who had a PJI were also aspirated to compare the composition of the PJI knee with uninfected contralateral knee. A minimum of 3 milliliters (ml) of synovial fluid was collected per joint. Then, the samples were aliquoted for culture and next generation sequencing (NGS) analysis.

RESULTS

The highest number of species was found in native osteoarthritic knees (P≤0.035). Cutibacterium, Staphylococcus, and Paracoccus species were dominant in native non-osteoarthritic knees, and meanwhile a markedly high abundance of Proteobacteria was observed in the osteoarthritic joints. Moreover, the contralateral and aseptic revision knees showed a similar trend in bacterial composition (P=0.75). The NGS analysis of patients who had PJI diagnosis, confirmed the culture results.

DISCUSSION/CONCLUSION

Distinct knee microbiome profiles can be detected in patients who have osteoarthritis and other knee conditions. The distinct microbiome in the knee joint and the close host-microbe relationships within the knee joint may play a decisive role in the development of osteoarthritis and periprosthetic joint infection.