Human transcriptomic response to periprosthetic joint infection

Masters, Thao L.; Bhagwate, Aditya; Dehankar, Mrunal; Greenwood‐Quaintance, Kerryl E.; Abdel, Matthew P.; Mandrekar, Jay; Patel, Robin

doi:10.1016/j.gene.2022.146400

Cited by 12 publications

(18 citation statements)

References 67 publications

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“…RNA extraction and sequencing of the samples studied here were previously performed 26,38,39 . Raw, gene-level count data from RNA sequencing were filtered to remove gene artifacts by selecting genes with at least 30 raw counts in 75% (70) of the 93 samples, leaving 11,274 genes remaining.…”

Section: Methodsmentioning

confidence: 99%

“…Nevertheless, determining the cause of arthroplasty failure remains challenging in some cases. Novel multi-omics approaches to microbial detection have been identified as potential avenues to aid in the diagnosis of PJIF cases and NIAF cases and among subgroups thereof [26][27][28][29][30][31][32] . One possibility involving multi-omics-based approaches is to simultaneously assess for microorganisms and the host response, recapitulating the traditional diagnostic framework with a single test that seeks to find the microorganism(s) involved and differentially assess the host response during PJIF and NIAF scenarios and even, hypothetically, subsets thereof.…”

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confidence: 99%

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

Fisher¹,

Krull²,

Bhagwate³

et al. 2022

Journal of Bone and Joint Surgery

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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.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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

Fisher¹,

Krull²,

Bhagwate³

et al. 2022

Journal of Bone and Joint Surgery

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“…To do so, results were compared to previous transcriptomic and proteomic analyses of sonicate fluid samples conducted by our group. Transcriptomic analysis of eight S. aureus PJI and forty NIAF sonicate fluid samples, including the eight that underwent LC-MS/MS here, has been recently reported [40] . Of the thirty-five DAPs found via LC-MS/MS, 18 had differentially expressed transcripts via RNA-sequencing.…”

Section: Further Validation Of Differentially Abundant Proteinsmentioning

confidence: 99%

“…The copyright holder for this this version posted December 30, 2022. ; https://doi.org/10.1101/2022.12.28.22284010 doi: medRxiv preprint Multi-omics approaches have recently been investigated as potential alternatives to overcome limitations of currently used diagnostic tools [26,29,[37][38][39][40] . Previous studies have shown that liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based profiling may be able to differentiate synovial fluid samples from PJI and NIAF patients [31,41] .…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies have shown that liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based profiling may be able to differentiate synovial fluid samples from PJI and NIAF patients [31,41] . Analysis of sonicate fluid, a sample-type that directly interrogates the site of infection -the implant surface -, may hypothetically allow for enhanced differentiation of infected and non-infected individuals and potentially PJI biomarker discovery [37,40,[42][43][44] . Mass spectrometry-based proteomic analysis provides an unbiased and in-depth overview of the protein expression alterations in biological samples.…”

Section: Introductionmentioning

confidence: 99%

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Mass Spectrometry-Based Proteomic Profiling of Sonicate Fluid DifferentiatesStaphylococcus aureusPeriprosthetic Joint Infection from Non-Infectious Failure: A pilot study

Fisher

Mangalaparthi²,

Greenwood‐Quaintance

et al. 2022

Preprint

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Purpose: This study aims 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 investigate novel biomarkers to increase diagnostic accuracy. Experimental Design: In this pilot study, eight sonicate fluid samples (four from NIAF and four from Staphylococcus 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 quantified in any three samples from each group and 35 statistically significant differentially abundant proteins (DAPs) were found (2-sample t-test p-values ≤0.05 and log2fold-change values ≥2 or ≤-2). Gene ontology pathway analysis found that microbial defense responses, specifically those related to neutrophil activation, were increased in S. aureus PJI compared to NIAF samples. Conclusion and Clinical Relevance: Proteomic profiling of sonicate fluid using LC-MS/MS, alone or in combination with complementary protein analyses, differentiated S. aureus PJI and NIAF in this pilot study.

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

Bell,

Cann,

Mishra

et al. 2024

Journal Orthopaedic Research

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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.

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Human transcriptomic response to periprosthetic joint infection

Cited by 12 publications

References 67 publications

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

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

Mass Spectrometry-Based Proteomic Profiling of Sonicate Fluid DifferentiatesStaphylococcus aureusPeriprosthetic Joint Infection from Non-Infectious Failure: A pilot study

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

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