Human transcriptomic response to periprosthetic joint infection

Immune response against antibiotic-resistant and antibiotic-sensitive staphylococcus aureus in a rat model of implant infection

Little is known about the in-vivo dynamics of biofilms associated with medical-device infections and their interplay with systemic inflammation, local immune responses, and tissue healing processes. There may be an opportunity to tailor therapeutic strategies to target these dynamics to improve treatment outcomes. We investigated immune responses to a methicillin-susceptible (ATCC 12600) and a multi-drug resistant (L1101) S. aureus strain using a rat subcutaneous implant model, analyzing local and systemic inflammation through 19 gene expressions over 21 days. Our goals were to identify differences in the immune response due to infection and also with respect to the two strains. We observed that systemic inflammation, indicated by α-2-macroglobulin, was elevated in the initial stages (up to day 7). Local inflammatory cytokine levels (IL-6, TNF-α, IL-6, TNF-α, IL-1β, IL10, IL-17, IL12a, IL12b, IFNG) varied by strain, typically higher against the clinical strain. Infections generally hindered early macrophage (MCSF1) and T-cell (CD4, CD5, CD6, CD8A) recruitment, particularly in cases involving the clinical strain. Conversely, a better healing response was observed in the infection of the more susceptible ATCC 12600 strain (VEGF, CXCR1, CXCR2, MMP-1, MMP-3, MMP-13). These results are crucial for understanding immune responses to such infections, guiding therapeutic strategies.

Investigating the Impact of IL-6 and CXCL8 on Neurodegeneration and Cognitive Decline in Alzheimer Disease

BACKGROUND

Alzheimer disease (AD) is a progressive neurodegenerative disorder primarily affecting the elderly, characterized by severe cognitive impairment and memory loss. Emerging evidence suggests that neuroinflammation plays a significant role in AD pathogenesis, with cytokines like interleukin-6 (IL-6) and C-X-C motif chemokine ligand 8 (CXCL8) contributing to the disease progression.

METHODS

We utilized Gene Expression Omnibus datasets to identify IL-6 and CXCL8 as pivotal inflammatory markers in AD. In vitro experiments were conducted using SK-N-BE(2)-M17 and THP-1 cell lines treated with IL-6 and CXCL8 to model AD. Additionally, in vivo tests on Amyloid Precursor Protein/Presenilin 1 (APP/PS1) AD mouse models were performed to assess the impact of these cytokines on cognitive functions and brain pathology.

RESULTS

The results indicated a significant decrease in cell viability, increased apoptosis, and elevated inflammatory factor secretion following IL-6 and CXCL8 treatment in vitro. In vivo, AD mouse models treated with these cytokines exhibited exacerbated emotional distress, decreased social interaction, impaired cognitive functions, and increased amyloid protein deposition in neural tissues.

CONCLUSIONS

The study highlights the detrimental effects of IL-6 and CXCL8 on neuronal health and cognitive functions in AD. These findings suggest that targeting these cytokines could offer potential therapeutic interventions for improving patient outcomes in Alzheimer disease.

Genomic Insights into Host Susceptibility to Periprosthetic Joint Infections: A Comprehensive Literature Review

Periprosthetic joint infection (PJI) is a multifactorial disease, and the risk of contracting infection is determined by the complex interplays between environmental and host-related factors. While research has shown that certain individuals may have a genetic predisposition for PJI, the existing literature is scarce, and the heterogeneity in the assessed genes limits its clinical applicability. Our review on genetic susceptibility for PJI has the following two objectives: (1) Explore the potential risk of developing PJI based on specific genetic polymorphisms or allelic variations; and (2) Characterize the regulatory cascades involved in the risk of developing PJI. This review focused on clinical studies investigating the association between genetic mutations or variations with the development of PJI. The genes investigated in these studies included toll-like receptors and humoral pattern recognition molecules, cytokines, chemokines, mannose-binding lectin (MBL), bone metabolism molecules, and human leukocyte antigen. Among these genes, polymorphisms in IL-1, MBL, vitamin D receptors, HLA-C, and HLA-DQ might have a relevant impact on the development of PJI. The literature surrounding this topic is limited, but emerging transcriptomic and genome-wide association studies hold promise for identifying at-risk genes. This advancement could pave the way for incorporating genetic testing into preoperative risk stratification, enhancing personalized patient care.

β-Defensin versus conventional markers of inflammation in periprosthetic joint infection: a retrospective study

Background

Diagnosing periprosthetic joint infection (PJI) remains a significant challenge for healthcare professionals. Commonly utilized inflammatory markers include erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and white blood cells (WBC). Human β-defensin 1 (β-defensin) is an antimicrobial peptide elevated in infection, yet its diagnostic value for PJI has not been explored. The purpose of this study was to evaluate the efficacy of synovial β-defensin as a diagnostic marker for PJI and to compare its performance with ESR, serum CRP, and WBC.

Methods

We conducted a single-center retrospective study from October 2022 to June 2023. A total of 105 joint fluid samples from revision patients at the Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra were collected intraoperatively (71 hips, 34 knees) and frozen. According to MSIS criteria, 64 patients were defined as positive for PJI and the remaining 41 were negative. Synovial β-defensin levels were quantified using ELISA, serum CRP levels by immunoturbidimetry, and blood ESR and WBC were analyzed. Sensitivity and specificity were determined using ROC curves, and diagnostic performance was compared using the area under the curve (AUC). Cut-off values for diagnosing PJI were established.

Results

Levels of synovial β-defensin, ESR, serum CRP, and WBC were significantly higher in the PJI group compared to the non-PJI (P < 0.0001). The AUCs were 0.948 for β-defensin, 0.884 for ESR, 0.902 for CRP, and 0.767 for WBC, with a combined AUC of 0.994. Sensitivity/specificity for β-defensin, ESR, CRP, and WBC were 0.966/0.830, 0.887/0.791, 0.930/0.771, and 0.820/0.682, respectively. Optimal predictive cut-off values were 1105.8 pg/mL for β-defensin, 11.5 mm/h for ESR, 5.55 mg/L for CRP, and 7.3 × 103/mm3 for WBC.

Conclusion

The synovial β-defensin assay demonstrated greater sensitivity and specificity for the diagnosis of PJI compared to ESR, serum CRP and WBC. Therefore, β-defensin shows promise as a diagnostic marker for PJI. Simultaneous determination of all markers may increase diagnostic confidence.

Staphyloccocus aureus biofilm, in absence of planktonic bacteria, produces factors that activate counterbalancing inflammatory and immune-suppressive genes in human monocytes

Staphyloccocus aureus (S. aureus) is a major bacterial pathogen in orthopedic periprosthetic joint infection (PJI). S. aureus forms biofilms that promote persistent infection by shielding bacteria from immune cells and inducing an antibiotic-tolerant metabolic state. We developed an in vitro system to study S. aureus biofilm interactions with primary human monocytes in the absence of planktonic bacteria. In line with previous in vivo data, S. aureus biofilm induced expression of inflammatory genes such as TNF and IL1B, and their anti-inflammatory counter-regulator IL10. S. aureus biofilm also activated expression of PD-1 ligands, and IL-1RA, molecules that have the potential to suppress T cell function or differentiation of protective Th17 cells. Gene induction did not require monocyte:biofilm contact and was mediated by a soluble factor(s) produced by biofilm-encased bacteria that was heat resistant and >3 kD in size. Activation of suppressive genes by biofilm was sensitive to suppression by Jak kinase inhibition. These results support an evolving paradigm that biofilm plays an active role in modulating immune responses, and suggest this occurs via production of a soluble vita-pathogen-associated molecular pattern, a molecule that signals microbial viability. Induction of T cell suppressive genes by S. aureus biofilm provides insights into mechanisms that can suppress T cell immunity in PJI.

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.

Mycobacterium tuberculosis-THP-1 like macrophages protein-protein interaction map revealed through dual RNA-seq analysis and a computational approach

Introduction. Infection caused by Mycobacterium tuberculosis (M. tb) is still a leading cause of mortality worldwide with estimated 1.4 million deaths annually.Hypothesis/Gap statement. Despite macrophages' ability to kill bacterium, M. tb can grow inside these innate immune cells and the exploration of the infection has traditionally been characterized by a one-sided relationship, concentrating solely on the host or examining the pathogen in isolation.Aim. Because of only a handful of M. tb-host interactions have been experimentally characterized, our main goal is to predict protein-protein interactions during the early phases of the infection.Methodology. In this work, we performed an integrative computational approach that exploits differentially expressed genes obtained from Dual RNA-seq analysis combined with known domain-domain interactions.Results. A total of 2381 and 7214 genes were identified as differentially expressed in M. tb and in THP-1-like macrophages, respectively, revealing different transcriptional profiles in response to infection. Over 48 h of infection, the host-pathogen network revealed 25 016 PPIs. Analysis of the resulting predicted network based on cellular localization information of M. tb proteins, indicated the implication of interacting nodes including the bacterial PE/PPE/PE_PGRS family. In addition, M. tb proteins interacted with host proteins involved in NF-kB signalling pathway as well as interfering with the host apoptosis ability via the potential interaction of M. tb TB16.3 with human TAB1 and M. tb GroEL2 with host protein kinase C delta, respectively.Conclusion. The prediction of the full range of interactions between M. tb and host will contribute to better understanding of the pathogenesis of this bacterium and may provide advanced approaches to explore new therapeutic targets against tuberculosis.

Diagnostic Value of Synovial Fluid Biomarkers for Periprosthetic Joint Infection: A Prospective, Double-blind Trial

BACKGROUND This prospective, double-blind study investigated the clinical diagnostic value of synovial fluid S100 calcium-binding protein A8 (S100A8) and S100 calcium-binding protein A9 (S100A9) in periprosthetic joint infection (PJI) and investigated the subtypes of a-defensin that have diagnostic value for PJI. MATERIAL AND METHODS Synovial fluid samples were collected from 82 patients with suspected PJI after total joint arthroplasty. Patients were divided into a PJI group (n=39) and non-PJI group (n=43). Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry was used to determine S100A8, S100A9, alpha-defensin, and internal reference standards in synovial fluid. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficiency of S100A8, S100A9, and alpha-defensin for PJI, as well as the diagnostic value in combination with common biomarkers of infection. RESULTS S100A8, 3 variants of S100A9, and 3 alpha-defensins (human neutrophil peptides [HNP]1-3) in synovial fluid were significantly higher in the PJI group than in the non-PJI group (P<0.001). The sensitivity, specificity, and the area under ROC curve (AUC) for diagnosing PJI were 97.4%, 86.0%, and 0.964 (95% CI: 0.929-0.998), respectively, for synovial fluid S100A8; 87.2%, 88.4% and 0.902 (95% CI: 0.823-0.980), respectively, for S100A9; and 89.7%, 83.7%, and 0.933 (95% CI: 0.884-0.982), respectively, for HNP1-3. The diagnostic efficiency was improved when combined with synovial fluid white blood cell count and percentage of polymorphonuclear neutrophils. CONCLUSIONS Synovial fluid S100A8, S100A9, and HNP1-3 have satisfactory diagnostic efficiency for the diagnosis of PJI, which will help clinicians to accurately diagnose PJI.

Mass spectrometry-based proteomic profiling of sonicate fluid differentiates Staphylococcus aureus periprosthetic joint infection from non-infectious failure: A pilot study

PURPOSE

This pilot study aimed to use proteomic profiling of sonicate fluid samples to compare host response during Staphylococcus aureus-associated periprosthetic joint infection (PJI) and non-infected arthroplasty failure (NIAF) and identify potential novel biomarkers differentiating the two.

EXPERIMENTAL DESIGN

In this pilot study, eight sonicate fluid samples (four from NIAF and four from S. aureus PJI) were studied. Samples were reduced, alkylated, and trypsinized overnight, followed by analysis using liquid chromatography-tandem mass spectrometry (LC-MS/MS) on a high-resolution Orbitrap Eclipse mass spectrometer. MaxQuant software suite was used for protein identification, filtering, and label-free quantitation.

RESULTS

Principal component analysis of the identified proteins clearly separated S. aureus PJI and NIAF samples. Overall, 810 proteins were identified based on their detection in at least three out of four samples from each group; 35 statistically significant differentially abundant proteins (DAPs) were found (two-sample t-test p-values ≤0.05 and log2 fold-change values ≥2 or ≤-2). Gene ontology pathway analysis found that microbial defense responses, specifically those related to neutrophil activation, to be increased in S. aureus PJI compared to NIAF samples.

CONCLUSION AND CLINICAL RELEVANCE

Proteomic profiling of sonicate fluid using LC-MS/MS differentiated S. aureus PJI and NIAF in this pilot study. Further work is needed using a larger sample size and including non-S. aureus PJI and a diversty of NIAF-types.

Profiling the Immune Response to Periprosthetic Joint Infection and Non-Infectious Arthroplasty Failure

Arthroplasty failure is a major complication of joint replacement surgery. It can be caused by periprosthetic joint infection (PJI) or non-infectious etiologies, and often requires surgical intervention and (in select scenarios) resection and reimplantation of implanted devices. Fast and accurate diagnosis of PJI and non-infectious arthroplasty failure (NIAF) is critical to direct medical and surgical treatment; differentiation of PJI from NIAF may, however, be unclear in some cases. Traditional culture, nucleic acid amplification tests, metagenomic, and metatranscriptomic techniques for microbial detection have had success in differentiating the two entities, although microbiologically negative apparent PJI remains a challenge. Single host biomarkers or, alternatively, more advanced immune response profiling-based approaches may be applied to differentiate PJI from NIAF, overcoming limitations of microbial-based detection methods and possibly, especially with newer approaches, augmenting them. In this review, current approaches to arthroplasty failure diagnosis are briefly overviewed, followed by a review of host-based approaches for differentiation of PJI from NIAF, including exciting futuristic combinational multi-omics methodologies that may both detect pathogens and assess biological responses, illuminating causes of arthroplasty failure.

Messages from the Seventh International Conference on Clinical Metagenomics (ICCMg7)

Clinical metagenomics (CMg), referring to the application of metagenomic sequencing of clinical samples to obtain clinically relevant information for the diagnosis and management of infectious diseases, has been rapidly evolving these last years. Following this trend, we held the seventh International Conference on Clinical Metagenomics (ICCMg7) in Geneva in October 2022. During the two-day conference, cutting-edge advances and new discoveries using CMg were presented which we summarize in the present paper. During this ICCMg7, we kept on following the progresses achieved worldwide that cover reproducibility in CMg, the advent of new technologies applied to the field of infectious diseases, innovative research in the field of the gut microbiota, and finally the expansion of CMg in the fields of clinical epidemiology with surveillance studies on emerging and known pathogens, but also on antibiotic resistance genes, in the environment and in the animal reservoirs.

Sonicate Fluid Cellularity Predicted by Transcriptomic Deconvolution Differentiates Infectious from Non-Infectious Arthroplasty Failure

BACKGROUND

Although cellularity is traditionally assessed morphologically, deep sequencing approaches being used for microorganism detection may be able to provide information about cellularity. We hypothesized that cellularity predicted using CIBERSORTx (Stanford University), a transcriptomic-based cellular deconvolution tool, would differentiate between infectious and non-infectious arthroplasty failure.

METHODS

CIBERSORTx-derived cellularity profiles of 93 sonicate fluid samples, including 53 from subjects who underwent failed arthroplasties due to periprosthetic joint infection (PJI) (abbreviated for the purpose of this study as PJIF) and 40 from subjects who had undergone non-infectious arthroplasty failure (abbreviated NIAF) that had been subjected to bulk RNA sequencing were evaluated.

RESULTS

Samples from PJIF and NIAF subjects were differentially clustered by principal component analysis based on the cellularity profile. Twelve of the 22 individual predicted cellular fractions were differentially expressed in the PJIF cases compared with the NIAF cases, including increased predicted neutrophils (mean and standard error, 9.73% ± 1.06% and 0.81% ± 0.60%), activated mast cells (17.12% ± 1.51% and 4.11% ± 0.44%), and eosinophils (1.96% ± 0.37% and 0.42% ± 0.21%), and decreased predicted M0 macrophages (21.33% ± 1.51% and 39.75% ± 2.45%), M2 macrophages (3.56% ± 0.52% and 8.70% ± 1.08%), and regulatory T cells (1.57% ± 0.23% and 3.20% ± 0.34%). The predicted total granulocyte fraction was elevated in the PJIF cases (32.97% ± 2.13% and 11.76% ± 1.61%), and the samples from the NIAF cases had elevated predicted total macrophage and monocyte (34.71% ± 1.71% and 55.34% ± 2.37%) and total B cell fractions (5.89% ± 0.30% and 8.62% ± 0.86%). Receiver operating characteristic curve analysis identified predicted total granulocytes, neutrophils, and activated mast cells as highly able to differentiate between the PJIF cases and the NIAF cases. Within the PJIF cases, the total granulocyte, total macrophage and monocyte, M0 macrophage, and M2 macrophage fractions were differentially expressed in Staphylococcus aureus compared with Staphylococcus epidermidis -associated samples. Within the NIAF cases, the predicted total B cell, naïve B cell, plasma cell, and M2 macrophage fractions were differentially expressed among different causes of failure.

CONCLUSIONS

CIBERSORTx can predict the cellularity of sonicate fluid using transcriptomic data, allowing for the evaluation of the underlying immune response during the PJIF and NIAF cases, without a need to phenotypically assess cell composition.