Joint Fluid Multi-Omics Improves Diagnostic Confidence During Evaluation of Children with Presumed Septic Arthritis

doi:10.21203/rs.3.rs-4688699/v1

Download PDF

Research Article

Joint Fluid Multi-Omics Improves Diagnostic Confidence During Evaluation of Children with Presumed Septic Arthritis

https://doi.org/10.21203/rs.3.rs-4688699/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Objective: To demonstrate the potential clinical value of multi-omics approach to guide clinical decision-making for children suspected to have presumed septic arthritis.

Study design: Twelve children with suspected septic arthritis were prospectively enrolled to conduct standard of care culture and polymerase chain reaction (PCR) testing of knee joint fluid specimen. A rapid multi-omics approach that combines metagenomics and host-immune response proteomics and single cell transcriptomics of the synovial fluid was explored to enhance the diagnostic confidence for children with both culture- and PCR-negative results.

Results: Ten children had suspected primary septic arthritis and two had acute hematogenous osteomyelitis (AHO) diagnosis. Joint fluid cultures were positive for 6/12 (50%) patient, whereas remaining 50% samples were both culture- and PCR-negative. Metagenomics sequencing results were 100% concordant with the culture. Many inflammatory cytokines including IL-4, IL-17A, MCP-1, IL-8, IL-2, IL-1b, IL-6, TNF-a, etc. were significantly elevated in the joint-fluid of children with bacteremia as compared to the infection negative subset. Interestingly, children without any bacteremia exhibited strong signature of autoantibodies (IgGs) in the joint-fluid. These autoantibodies were targeted to several nuclear i.e. dsDNA, histones, Jo-1, scl-70, Ro/SS-A, SmDs, CENP-A and non-nuclear antigens i.e. Albumin, Collagens, Myosin, Laminin, etc.. Furthermore, single cell transcriptomics confirmed the abundance of autoreactive CD8+ T cells and B cells in autoantibody positive subject as potential drivers of autoimmune pathology. The results from all three assays, including 16S sequencing (p=0.006), cytokine profile (p=0.009) and autoantibody profile (p=0.02) were significantly differential between children with infection vs those without. Out of six infection negative children, multiomics analysis characterized two children with inflammatory arthritis with potential Juvenile idiopathic arthritis (JIA) and one child with Post-Streptococcal Reactive Arthritis. These three children were immediately referred to Rheumatology clinic for further investigation. Overall, this improved the diagnostic confidence for 9 of 12 (75%) children and provided crucial data to further guide the diagnosis especially in the infection negative subset.

Conclusions: The multiomics approach rapidly stratifies children with bacterial infections and potentially reactive or autoimmune inflammatory conditions, and thus help guide the appropriate evaluation and treatment strategies in a higher percentage of cases than previously possible using standard evaluation strategies.

Metagenomics

Infection

Next-generation sequencing (NGS)

Cytokine

Septic arthritis

Streptococcus

Pediatric

Autoimmune

Definitive diagnosis of acute bacterial arthritis is challenging due to limitations of our current capabilities of assessment using clinical judgment and basic laboratory testing (1). There is a modest rate of inappropriate initial treatment of children with reactive or inflammatory conditions as if they had infection given the short timeframe available to diagnose and treat infection (2). In one study, 34 of 355 (9.6%) children who had been treated for acute bacterial arthritis were subsequently found to have noninfectious conditions, including viral conditions, Post-Streptococcal Reactive Arthritis (PSRA), or inflammatory arthritis (2). This implies that there were unnecessary invasive procedures and antibiotic treatment courses, which were likely balanced against the risk of adverse outcomes from unrecognized and untreated bacterial arthritis. There is also a concern for substantial delay in the correct assessment and treatment of children with inflammatory conditions not only during the antibiotic treatment period but also while awaiting evaluation by pediatric rheumatology.

While polymerase chain reaction (PCR) techniques have improved our ability to confirm acute bacterial arthritis and identify causative pathogens, there are limitations of this technology, including lengthy turnaround time for 16S PCR and less than perfect sensitivity. We have previously reported joint fluid culture positivity ranging from 31.0–35.1% and PCR positivity ranging from 47.1–54.1% for an overall rate of confirmed bacterial detection of approximately 50%(2) (3). Children for whom no bacterial pathogens are identified, from blood or joint fluid cultures or PCR, are labeled as presumed primary septic arthritis, and remain a clinical dilemma. Their ultimate diagnosis is often guided by joint fluid nucleated cell counts, the segmented neutrophil differential, inflammatory marker trends and clinical improvement to normalcy within an appropriate timeframe following surgical and antibiotic treatment. However, there is a wide range of nucleated cell counts, and to a lesser extent segmented neutrophil differentials, among children with either confirmed or presumed primary septic arthritis. It is therefore desirable to explore new technologies to provide better guidance to clinicians who evaluate children with presumed primary septic arthritis, particularly to help recognize those children for who there is increased likelihood that the joint involvement is inflammatory in nature.

Multi-omics is an integrated approach to explore biological phenomena incorporating several data types, including genomics, transcriptomics, proteomics, and metabolomics. Clinical applications of multi-omics are rapidly expanding given the recent focus of National Institutes of Health funding, the progressive decline in costs, and the increased efficiency of these technologies. Metagenomics Next-Generation Sequencing (mNGS) offers sensitive, quick, and culture-free identification of pathogens which are difficult to grow or identify in joint fluid (4). The small sample volume requirement makes this especially attractive for use in young children and infants (5–7). Furthermore, an ability to rapidly identify protein products (cytokines or antibodies) of a host response to infection or inflammation through proteomics has the potential to improve our confidence and dramatically change the management of these children. The purpose of this study is to demonstrate the feasibility and potential clinical value of multi-omics, incorporating mNGS, cytokine/antibody proteomics, and single cell transcriptomics to guide clinical decision-making for children suspected to have presumed septic arthritis based on the joint fluid obtained during aspiration. Additionally, while single cell transcriptomics is neither economically nor technically feasible for routine clinical testing at present, but present study demonstrates the potential value of this technology for in-depth molecular characterization of disease mechanisms at single cell level in near future.

Study design, patient recruitment and sampling

Following Institutional Review Board approval (STU-2021-0970), twelve children with initial concern for septic arthritis were enrolled by written consent before surgery (Table 1). Apart from routine joint fluid specimen processing, an additional 1-1.5 ml of joint fluid was collected from each participant in a sterile collection tube and stored at -80°C. Patient demographics, clinical data, and laboratory data were also collected (Table 1). The analyzing laboratory was blinded to the patient´s characteristics.

Sample Analysis

Two hundred and fifty microliters of joint fluid were used for DNA extraction using the Zymo Research MicroBIOMICS kit. Fifteen and ten microliters of joint fluid were used for cytokine and antibody profiling assays, respectively. DNA quality and quantity assessment were done with Tapestation and Qubit. The single cell RNA sequencing 3’ assay was performed on 8-10K freshly collected primary single cells using a 10X Genomics protocol. Cell count and viability assessment was done using Countess and Luna instruments. All primary specimens and extracted DNAs were stored at -80°C.

16S sequencing assays

We performed two different 16S rRNA gene sequencing methods. The first assay generates bacterial taxonomy based on the v3-v4 hypervariable region (469bp) sequence of the bacterial 16S rRNA gene whereas the second assay generates real-time, high resolution, species level taxonomy based on the entire sequence (1.5Kb) of the bacterial 16S rRNA gene. 16S rRNA v3-v4 sequencing was performed on MiSeqDx using the Zymo Research Quick-16S NGS Library Prep Kit (Catalog No D6400). Subsequently, full length 16S rRNA gene sequencing was performed using long-read sequencing technology (Oxford Nanopore, United Kingdom). All eleven samples were sequenced on one R9.4.1 flow cell for 48 hours on the MinION (Oxford Nanopore Technologies). RAW quality pass FASTQ files were used for taxonomic classification and downstream analysis.

Sequencing data analysis

Samples with more than 50-K QC pass short sequencing reads from MiSeqDx were used for 16S operational taxonomic unit (OTU) analysis. Taxonomic classification and OTUs abundance analysis was performed using the CLC Bio microbial genomics module (https://www.qiagenbioinformatics.com/plugins/clc-microbial-genomics-module/). Individual sample reads were annotated with the Greengenes v13 database using a 97% similarity index.

Nanopore 16S data was analyzed using the EPI2ME pipeline and WIMP workflow from Oxford Nanopore Technology. Human sequences were excluded with the NCBI BMTagger Human Contamination Screening Tool (ftp://ftp.ncbi.nlm.nih.gov/pub/agarwala/bmtagger/). OTUs were based on 97% sequence similarly. Taxonomic classification and OTUs abundance analyses were performed on the filtered sequences with the computational tool MetaPhlAn. Since taxonomic abundance analysis often results in a significant portion of the reads being classified into an unknown group, the unidentified sequences were run through the de novo assembly algorithm Velvet. Assembled contigs were mapped to National Center for Biotechnology Information (NCBI) bacterial reference genome database.

Quality control included positive and negative external controls. All eight bacteria in the Zymo community control (D6300) sample were retrieved in the expected abundance ratio, whereas the water sample, as a negative control, generated < 500 non-specific sequencing reads. Therefore, OTUs with > 500 sequencing reads were only considered for downstream analysis. The total observed reads for a given species were normalized with the total number of reads to calculate relative abundances for comparison. Bioinformatics analyses and data visualization were performed using the R statistical package. Raw FASTQ files from Illumina and Nanopore sequencing have been deposited in the BioProject database (NCBI) with accession number PRJNA 1097662.

Immune profiling assays

i.48-plex cytokine assay

This assay was used to measure quantitative level of multiple cytokines, chemokines, and growth factors in the joint fluid. It is a 48-plex magnetic microspheres (bead) based Luminex assay (Bio-Plex pro Human cytokine Assay, Bio-Rad) run on a Bio-Plex 200-flow cytometer and quantified utilizing xMAP technology (Luminex Corp., Austin, TX, USA). Fifteen microliters of joint fluid were used for the cytokine assay. The cytokine concentration (pg/mL) was determined by fluorescence. A standard curve was used to calculate the quantitative range of various proteins following a previously published protocol (8).

ii.Autoantibody Protein array

Our laboratory has developed a multiplex protein array to screen autoantibody responses against common autoantigens implicated in various rheumatic and autoimmune conditions (9, 10). Using the same protocol, IgG and IgM reactivity against 120 common autoantigens were tested with a microfluidic antigen array previously developed by our group (10). Ten microliters of joint fluid were used for antibody assay. The net fluorescent signal intensity of each antigen was normalized by a robust linear model using internal positive controls and standardized for heat map presentations, which were generated by the R heatmap package (11).

Single cell RNA sequencing and analysis

Joint-fluid specimens were immediately transported to the Microbiome Research Laboratory at UT Southwestern Medical Center for single cell assay. We used 100 µl of joint-fluid specimen from one infection positive (JF7) and two infection negative (JF8 & JF12) for single cell RNA sequencing assay. The cell viability score was > 80%. The 10x Genomics Chromium controller instrument was used for Gel Bead- in Emulsion (GEMs) preparation. Chromium Next GEM Single Cell 3’ Kit v3.1 (PC-1000269), Chromium Next GEM Chip G Single Cell Kit (PC-1000127) and Dual Index Kit TT Set A Kit (PC-1000215) were used for Single-Cell Library Preparation. Complementary DNA (cDNA)and final barcoded sequencing libraries were generated as per the manufacturer’s specifications and their quality and concentration was assessed using the Bioanalyzer 2100 and qPCR, respectively. Quality pass single cell libraries were sequenced on NovaSeq6K sequencer using the paired end 150 bp sequencing kit. About 30–40 K sequencing reads were generated per cell. Unique Molecular Identifier counts for each cellular barcode were quantified and used to estimate the number of cells successfully captured and sequenced. Cell Ranger Single-cell Software Suit (v 5.0.0) was used for sample demultiplexing and single-cell 3’ gene counting. SoupX R package (constantAmateur/SoupX: R package to quantify and remove cell free mRNAs from droplet based scRNA-seq data (github.com)) was used for ambient RNA reads cleaning. Doublets were identified using Scrublet (Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic DataScienceDirect) and removed from downstream analysis.

Statistical Analysis

Quantitative data were expressed by the median and interquartile range (IQR 25%-75%) and compared using the U-Mann Whitney test. Spearman correlation was used for correlation analysis between statistically significant variables. Blood biomarkers from patients diagnosed with concomitant osteomyelitis were excluded from the analysis. Differences with a p < 0.05 were considered statistically significant. All statistical analyses were performed using R (version 4.3).

Patient characteristics and standard of care results

Demographics, clinical characteristics, and standard of care results of the twelve enrolled children are summarized in Table I. Ten out of twelve joint fluid samples corresponded to suspected primary septic arthritis. Two children presented as acute hematogenous osteomyelitis (AHO) with contiguous joint involvement (JF10 and JF11), which was affirmed by positive blood cultures for Staphylococcus aureus and Magnetic Resonance Imaging (MRI) findings.

Overall 6/12 (50%) joint fluid samples were PCR and/or culture positive with standard-of-care results. Streptococcus pyogenes infection was confirmed in four and Staphylococcus aureus identified in two subjects. The remaining 6/12 (50%) samples were negative for infection by the standard of care methods as well as via metagenomics. Of these, two cases were classified as presumed acute bacterial arthritis with no pathogen identified in the joint fluid (JF6, JF9) based on the absolute neutrophil count in the joint fluid (> 50,000 cells/mm³) and other clinical data (12). The remaining four (JF5, JF8, JF11, JF12) were classified as undetermined. Overall clinical characteristics of infection-positive vs infection-negative subsets were similar, except PCT (procalcitonin) level which was significantly higher (p = 0.04) in infection positive group (Suppl. Table 1).

Rapid pathogen identification and taxonomic classification in the joint fluid

16S v3-v4 amplicon as well as full length 16S rRNA gene sequencing analysis on joint fluid specimens detected the presence of Streptococcus DNA signatures in four children with suspected primary septic arthritis and Staphylococcus DNA signatures in two children (Table 1, Fig. 1A). No significant pathogen bacterial signature was detected in the remaining six children (50%). The median [IQR] number of 16S sequencing total read counts in the joint fluid was significantly lower in those without infection (1,998.5 [815-3,710.75] vs 26,366 [23,935 − 31,505]; -13.19-fold-change, p = 0.003) (Suppl. Figure 1).

Different cytokine profile of infection positive verse infection negative children

Joint fluid cytokine analysis revealed significant differences in the innate immune response between children with and without bacterial identification (Fig. 1B). The level of 36 inflammatory cytokines such as IL-4, GM-CSF, IL-12p70, IL-17A, MCP-1, IL-5, IL-8, IL-2, IL-1b, IL-6, TNF-a etc. were significantly elevated in children with positive identification of bacteria as compared to those without (Suppl. Table 2). Twenty two of 48 cytokines (46%) correlated significantly with the number of 16S sequencing reads. The strongest correlations were with LIF (r = 0.84, p = 0.001), IL-2Ra (r = 0.82, p = 0.002), MIP-1a (r = 0.79, p = 0.004) and GRO-a (r = 0.78, p = 0.004). Other cytokines with undetectable values in the negative group were GM-CSF, b-NGF, IL-5 and IL-12p70. The comparison of cytokine profiles between the Streptococcus pyogenes positive verses the Staphylococcus aureus positive group showed higher levels of Macrophage migration inhibitory factor (MIF), IL-15, SDF-1a, and IL-6 in the latter group, these changes were significant for IL-16 (Suppl. Figure 1).

Autoantibody (IgG) signatures in infection negative subset

The heatmap in Fig. 1C presents IgG antibody levels against 120 different autoantigens for all twelve children. As shown, children with negative mNGS results had a higher prevalence and titers of autoantibodies targeted against several nuclear and non-nuclear autoantigens previously implicated in Systemic Lupus Erythematosus (SLE), Rheumatoid arthritis, etc.. Thirty three autoantibodies were significantly elevated in the infection negative group (Suppl. Table 2). Autoantibodies targeting Albumin, Factor 1 and Nup62 protein showed significant enrichment in infection negative subset (Suppl. Figure 2). Most strikingly, JF8 and JF12 exhibited the strongest autoantibody signatures with very high concentrations of several anti-nuclear antibodies (ANAs) including those targeted to dsDNA, histones, Jo-1, scl-70, Ro/SS-A, SmDs, smRNPs, and CENP-A. Similarly, JF5 & JF6 presented strong titers of anti- U1-snRNPs, complement, myosin, laminin, nucleosome and Sm autoantibodies, which represent non-nuclear protein targets previously reported in patients with autoimmune conditions. Interestingly, 3 of 6 infection negative subjects showed significantly elevated level of autoantibodies targeting collagen proteins (i.e. Collagen I, II, III, IV) (Suppl. Figure 2).

Single cell RNA sequencing showed abundance of autoreactive T and B cells in infection negative children

To unravel the immune cell composition of joint fluid in cases of infection positive and infection negative diagnoses, we performed single cell RNA sequencing analysis in 100 µl of joint fluid specimen of JF7 (infection positive case) and JF8 & JF12 (infection negative but autoantibody positive cases). As shown in Fig. 1D, single cell transcriptomics analysis detected 15,629 neutrophils (88%) 1,550 monocytes (16%), 238 macrophages (2%), 123 T cells (1%) and < 100 (1%) Dendritic Cells and Natural Killer cells in JF7 specimen. On the other hand, JF8 joint fluid was significantly (~ 30-fold) enriched for T and B cells. We detected 4,920 neutrophils (49%) 332 monocytes (3%), 103 macrophages (1%), 3,286 T cells (33%), 282 B cells (3%), 157 Dendritic Cells (2%) and < 100 (1%) other cell types. Similarly, JF12 sample showed significant signature of T cells [3003 cells (53%)], B cells [132 cells (2%)], Monocytes [1369 cells (24%)] and only 10% (597) Neutrophils. Consistent with autoantibody data, single cell sequencing detected presence of antibody producing CD19 + B cells and abundance of autoreactive and apoptosis resistant CD8 + CCR7 + T cell subset in JF8 and JF12 subjects as compared to JF7 (Fig. 2, Suppl. Figure 4&5). As shown in Fig. 2, JF8 & JF12 subjects exhibited overall higher diversity and abundance of adaptive immune cells in joint-fluid as compared to infection positive sample (JF7).

In this study, we demonstrate the potential advantage of multi-omics approach, including genomics, proteomics, and transcriptomics, of joint fluid in the diagnosis of suspected septic arthritis. Our preliminary data favorably show a proof-of-concept that multiomics method is promising for greater discriminatory capability and improved characterization of culture-negative acute arthritis, as occurred in 6 of 12 (50%) children studied. This multi-omics approach identified: two cases of potential Juvenile Idiopathic Arthritis (JIA) and one children with Post-Streptococcal reactive arthritis (PSRA). While less than perfect, the improved diagnostic confidence for 9 of 12 (75%) children is a substantial advancement over existing capability.

The joint fluid samples with bacteria exhibited significantly higher expression of pro-inflammatory cytokines, including IL-1b, IL-6, IL-4, IL-2Ra, and GRO-a consistent with published literature (13) (14, 15). One of the most strongly elevated cytokines was GRO-alpha, which is also known as CXCL1, a chemoattractant that recruits neutrophils to the site of infection (16). Interestingly, it was exclusively expressed in joint fluids with confirmed bacterial presence. Among elevated cytokines, IP-10, IL-1B, IL-18 and IL-6 are several well-established markers of pathogenic S. pyogenes induced early innate immune response (17, 18). Interestingly, IL-15 and IL-16, which is implicated in Staphylococcus aureus induced arthritis (19), was significantly elevated in Staphylococcus aureus positive joint fluid (JF-3 & JF-10) as compared to samples positive for Streptococcus pyogenes. Overall, the cytokine data clearly distinguished between children with and without bacterial infection. The observed poor joint-fluid culture-positivity in septic arthritis cohort (6/12 or 50%) is consistent with previous reports (20, 21). The mNGS findings were 100% concordant with hospital PCR testing.

Six children with no bacterial identification by any standard of care testing methods or mNGS (JF5, JF6, JF8, JF9, JF11, JF12), left us with a common diagnostic dilemma. The normal level of cytokines in this subset was consistent with negative mNGS results and indicated potential non-infectious conditions consistent with literature (22). Autoantibodies are established biomarkers of rheumatic and autoimmune diseases, such as systemic lupus erythematosus (SLE)(23). The observed autoantibody signatures in infection negative group suggest possibility of an autoimmune pathology in this subset as published literature report association such antibodies with established autoimmune and rheumatic conditions i.e. SLE (SmD, U1-snRNPs, dsDNA Albumin) (24), arthritis (Albumin)(25), myositis (Nup62)(26) and collagen-induced arthritis (Collagen II, Collagen III & IV)(27). In this context, sample JF8, JF9 and JF12 showed highest levels of these autoantibodies suggesting increased possibility of potential autoimmune pathology in these three subjects. It is known that a person can exhibit autoantibodies several years prior to the onset of a clinical autoimmune disease (28). Consistent with this, antibody data was corroborated in three children during their follow up. Two of these subjects (JF8 & JF12) of these confirmed to be JIA by rheumatology. One subject (JF11) was confirmed as PSRA as evidenced by elevated Anti-Streptolysin O and Anti-DNAse B titers. The strong autoantibody signatures in JF8 and JF12 were further supported by single cell RNA sequencing data showing abundance of autoreactive T and B cells in these subjects. Observed enrichment of CD8 + CCR7 + T cells in JF8 and JF12 joint fluid is consistent with the literature that suggest role of CD8 + T cells in induction, progression and pathogenesis of autoimmune diseases(29).

Altogether these findings suggest increased probability of adaptive immune system related abnormalities driving the clinical process among the infection negative subset of the children. Given that the multi-omics tests utilized in this study have the potential for rapid turnaround (within 48 hours of specimen procurement), this approach may offer considerable improvement over current method (30). For the child with JIA (JF8 & JF12) with single cell sequencing analysis consistent with a strong antibody profile, antibiotics would not have been administered, and the child would have had an expedited rheumatology consultation. Similarly, JF11 would have undergone testing for Anti-Streptolysin O and Anti-DNAse B titer. This capacity is particularly important in young children with acute mono-arthritis as clinical and laboratory parameters do not reliably differentiate between infectious, inflammatory, or reactive arthritis, which necessitates ongoing follow-up and presumptive antibiotic therapy while observing for resolution or evolution toward alternative diagnostic considerations (31).

Present study is limited by small sample size and heterogeneity of demographic variables. Despite this, it clearly demonstrates the potential benefit of multi-omics approach, especially autoantibody assay in culture negative cases to immediately discern between groups of children with bacterial presence in the joint fluid and those with potential autoimmune conditions. This ability to rapidly characterize the joint fluid of children beyond routinely used parameters has tremendous potential for clinical application. The small sample size and missing clinical data may have contributed to the lack of statistical significance of some known biomarkers of acute bacterial arthritis. Another limitation is previous antibiotic administration prior to treatment, which may have impact on clinical read outs as well as immune profile. We were not able to profile cytokine and antibody assays in serum and compare the data to joint-fluid, due to unavailability of blood specimens in most of these cases. An ongoing follow up in the subset of patients with autoimmune phenotypes is underway. Our current clinical impression of this subset is that it represents variations of presumed primary septic arthritis. JF-5 is suspected to be infection from Kingella kingae given the young age of the child, male sex, indolent presentation with minimally elevated inflammatory markers, and history of antibiotic pre-treatment with amoxicillin. He had a rapid clinical response to treatment with clindamycin and ceftriaxone intravenously followed by oral trimethoprim sulfamethoxazole and has had no recurrence. Similarly, JF-6 and JF-9 are thought to be presumed primary septic arthritis as they had clinical and laboratory improvement to normalcy within the course of treatment with antibiotics and no recurrence in the period of follow-up. They were also within the age range in which Kingella is commonly seen so their antibiotic regimen was like that of JF-5. The infection negative samples showed small number of 16s sequencing reads mapping to non-pathogenic organisms such as Phyllobacterium, Rhizobiaceae and Micrococcus, which may be sequencing artifacts or contaminants, so need further investigation.

In summary, our preliminary analysis support the idea that cytokine and antibody signatures in joint fluid combined with 16S sequencing and single cell analysis have a promising role in the quick evaluation and management of suspected primary septic arthritis and AHO. Beyond rapidly confirming or eliminating infectious etiologies, a multi-omics approach may reveal potential developing inflammatory or reactive conditions. Given the current availability and rapid results turnaround of this technology, there is value in sharing these results with the clinical and scientific community to allow for early adoption and dissemination. Further studies with larger cohorts of children are needed to confirm and expand upon our findings.

The multiomics approach, especially the comprehensive autoantibody ssceening in joint-fluid, rapidly identify children with potentially reactive or autoimmune inflammatory conditions, and thus help timely guide the appropriate evaluation and treatment strategies in a higher percentage of cases than previously possible using standard evaluation strategies. Based on the present finding, we suggest to consider rapid autoantibody screening of joint fluid, especially in case of culture/PCR negative cases.

AHO, Acute Hematogenous Osteomyelitis

DNA, Deoxyribonucleic acid

IL, Interleukin

JF, Joint Fluid

mNGS, Metagenomics Next-Generation Sequencing

NCBI, National Center for Biotechnology Information

OTU, Operational Taxonomic Unit

PCR, Polymerase Chain Reaction

PSRA, Post-Streptococcal Reactive Arthritis

RNA, Ribonucleic acid

Ethics approval and consent to participate

This research involved human subjects. The study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from the parents or guardians of the children who served as subjects of the investigation. All families had knowledge that the findings would be published in a scientific journal. The Institutional Review Board at UT Southwestern Medical Center approved this study (IRB# STU-2021-0970).

Consent for publication

All participants and investigators/ co-authors have agreed and provided the consent for publication of the present manuscript.

Availability of data and material

All 16S rRNA raw sequence data were deposited in the National Center for Biotechnology Information (NCBI) sequence read archive (SRA) under the BioProject ID -PRJNA1097662 and is available at the following URL: https://urldefense.com/v3/http://www.ncbi.nlm.nih.gov/bioproject/1097662_- BioProject - NCBI (nih.gov). Single cell data, cytokine and antibody arrays are available from the Microbiome, Microarray and Immunophenotyping Core at UT Southwestern Medical Center upon request. Further array data from our study are included in Supplemental information.

Competing interests: None

Funding: This study was funded by the Hartwell Foundation grant to Dr. Prithvi Raj.

Authors' contributions

YPL: Interpretation of data, Investigation, Manuscript writing and editing,NGT: Particpant consent and sample aquisition, BZ: Single cell sequencing assay and analysi, IR: Autoantibody CAA: Sequencing and data análisis, CZ: Cytokine and Antibody data statistics and methods, YL: 16S sequencing and análisis, method, PS: Cytokine assay and analysis, NSCvO:Manuscript editing and interpretation, SM: Autoantibody data interpretation, discusión, manuscript editing, LVH: Methodology, Investigation, Resources; LAC: Patient enrollment, Clinical diagnosis, Data Interpretation, P.R., Resource, Study Conceptualization, investigation, data analysis, manuscript writing & editing. All authors have read and agreed to the publication of this manuscript.

Acknowledgements

Authors are grateful to The Hartwell Foundation for funding this research study. Authors gratefully acknowledge and thank study participants and their families for providing invaluable specimens to conduct the present research. Authors also acknowledge Microbiome, Microarray and Genomics Core Facility in the department of Immunology at UT Southwestern Medical Center.

van der Laan L, Gaines N, Van Horn N, Jo C, Ma Y, Copley LA. Comparison of Procalcitonin With Commonly Used Biomarkers and Algorithms for Evaluating Suspected Pediatric Musculoskeletal Infection in the Emergency Department. J Pediatr Orthop. 2023;43(2):e168-e73.
Klosterman MM, Villani MC, Hamilton EC, Jo C, Copley LA. Primary Septic Arthritis in Children Demonstrates Presumed and Confirmed Varieties Which Require Age-specific Evaluation and Treatment Strategies. J Pediatr Orthop. 2022;42(1):e27-e33.
Carter K, Doern C, Jo CH, Copley LA. The Clinical Usefulness of Polymerase Chain Reaction as a Supplemental Diagnostic Tool in the Evaluation and the Treatment of Children With Septic Arthritis. J Pediatr Orthop. 2016;36(2):167 − 72.
Su M, Satola SW, Read TD. Genome-Based Prediction of Bacterial Antibiotic Resistance. J Clin Microbiol. 2019;57(3).
Hogan CA, Yang S, Garner OB, Green DA, Gomez CA, Dien Bard J, et al. Clinical Impact of Metagenomic Next-Generation Sequencing of Plasma Cell-Free DNA for the Diagnosis of Infectious Diseases: A Multicenter Retrospective Cohort Study. Clin Infect Dis. 2021;72(2):239 − 45.
Castillo DJ, Rifkin RF, Cowan DA, Potgieter M. The Healthy Human Blood Microbiome: Fact or Fiction? Front Cell Infect Microbiol. 2019;9:148.
Garza DR, Dutilh BE. From cultured to uncultured genome sequences: metagenomics and modeling microbial ecosystems. Cell Mol Life Sci. 2015;72(22):4287 − 308.
Muthukumar A, Narasimhan M, Li QZ, Mahimainathan L, Hitto I, Fuda F, et al. In-Depth Evaluation of a Case of Presumed Myocarditis After the Second Dose of COVID-19 mRNA Vaccine. Circulation. 2021;144(6):487 − 98.
Raj P, Song R, Zhu H, Riediger L, Jun DJ, Liang C, et al. Deep sequencing reveals a DAP1 regulatory haplotype that potentiates autoimmunity in systemic lupus erythematosus. Genome Biol. 2020;21(1):281.
Pichilingue-Reto P, Raj P, Li QZ, Dozmorov I, Karp DR, Wakeland EK, et al. Serum IgG Profiling of Toddlers Reveals a Subgroup with Elevated Seropositive Antibodies to Viruses Correlating with Increased Vaccine and Autoantigen Responses. J Clin Immunol. 2021;41(5):1031-47.
Ghosh N, Postow M, Zhu C, Jannat-Khah D, Li QZ, Vitone G, et al. Lower baseline autoantibody levels are associated with immune-related adverse events from immune checkpoint inhibition. J Immunother Cancer. 2022;10(1).
Woods CR, Bradley JS, Chatterjee A, Kronman MP, Arnold SR, Robinson J, et al. Clinical Practice Guideline by the Pediatric Infectious Diseases Society (PIDS) and the Infectious Diseases Society of America (IDSA): 2023 Guideline on Diagnosis and Management of Acute Bacterial Arthritis in Pediatrics. Journal of the Pediatric Infectious Diseases Society. 2023;13(1):1–59.
Chen J, Jin Y, Li C, Gan Y, Li J, Chen S, et al. Evaluation of soluble CD25 as a clinical and autoimmune biomarker in primary Sjögren's syndrome. Clin Exp Rheumatol. 2020;38 Suppl 126(4):142-9.
Russell SE, Moore AC, Fallon PG, Walsh PT. Soluble IL-2Rα (sCD25) exacerbates autoimmunity and enhances the development of Th17 responses in mice. PLoS One. 2012;7(10):e47748.
Zhang RJ, Zhang X, Chen J, Shao M, Yang Y, Balaubramaniam B, et al. Serum soluble CD25 as a risk factor of renal impairment in systemic lupus erythematosus - a prospective cohort study. Lupus. 2018;27(7):1100-6.
Zhou C, Gao Y, Ding P, Wu T, Ji G. The role of CXCL family members in different diseases. Cell Death Discov. 2023;9(1):212.
Anderson J, Imran S, Frost HR, Azzopardi KI, Jalali S, Novakovic B, et al. Immune signature of acute pharyngitis in a Streptococcus pyogenes human challenge trial. Nat Commun. 2022;13(1):769.
LaRock CN, Nizet V. Inflammasome/IL-1β Responses to Streptococcal Pathogens. Front Immunol. 2015;6:518.
Bergmann B, Jirholt P, Henning P, Lindholm C, Ohlsson C, McInnes IB, et al. Antibiotics with Interleukin-15 Inhibition Reduce Joint Inflammation and Bone Erosions but Not Cartilage Destruction in Staphylococcus aureus-Induced Arthritis. Infect Immun. 2018;86(5).
Spyridakis E, Gerber JS, Schriver E, Grundmeier RW, Porsch EA, St Geme JW, et al. Clinical Features and Outcomes of Children with Culture-Negative Septic Arthritis. J Pediatric Infect Dis Soc. 2019;8(3):228 − 34.
Cohen E, Katz T, Rahamim E, Bulkowstein S, Weisel Y, Leibovitz R, et al. Septic arthritis in children: Updated epidemiologic, microbiologic, clinical and therapeutic correlations. Pediatr Neonatol. 2020;61(3):325 − 30.
Yang L, Chen Z, Guo H, Wang Z, Sun K, Yang X, et al. Extensive cytokine analysis in synovial fluid of osteoarthritis patients. Cytokine. 2021;143:155546.
Li QZ, Zhou J, Wandstrat AE, Carr-Johnson F, Branch V, Karp DR, et al. Protein array autoantibody profiles for insights into systemic lupus erythematosus and incomplete lupus syndromes. ClinExpImmunol. 2007;147(1):60–70.
Didier K, Bolko L, Giusti D, Toquet S, Robbins A, Antonicelli F, et al. Autoantibodies Associated With Connective Tissue Diseases: What Meaning for Clinicians? Front Immunol. 2018;9:541.
Nehring J, Schirmbeck LA, Friebus-Kardash J, Dubler D, Huynh-Do U, Chizzolini C, et al. Autoantibodies Against Albumin in Patients With Systemic Lupus Erythematosus. Front Immunol. 2018;9:2090.
Senécal JL, Isabelle C, Fritzler MJ, Targoff IN, Goldstein R, Gagné M, et al. An autoimmune myositis-overlap syndrome associated with autoantibodies to nuclear pore complexes: description and long-term follow-up of the anti-Nup syndrome. Medicine (Baltimore). 2014;93(24):383 − 94.
Hashida R, Shimozuru Y, Chang J, Agosto-Marlin I, Waritani T, Terato K. New Studies of Pathogenesis of Rheumatoid Arthritis with Collagen-Induced and Collagen Antibody-Induced Arthritis Models: New Insight Involving Bacteria Flora. Autoimmune Dis. 2021;2021:7385106.
Arbuckle MR, McClain MT, Rubertone MV, Scofield RH, Dennis GJ, James JA, et al. Development of autoantibodies before the clinical onset of systemic lupus erythematosus. NEnglJMed. 2003;349(16):1526-33.
Gravano DM, Hoyer KK. Promotion and prevention of autoimmune disease by CD8 + T cells. J Autoimmun. 2013;45:68–79.
Gamalero L, Ferrara G, Giani T, Cimaz R. Acute Arthritis in Children: How to Discern between Septic and Non-Septic Arthritis? Children (Basel). 2021;8(10).
Thomas M, Bonacorsi S, Simon AL, Mallet C, Lorrot M, Faye A, et al. Acute monoarthritis in young children: comparing the characteristics of patients with juvenile idiopathic arthritis versus septic and undifferentiated arthritis. Sci Rep. 2021;11(1):3422.

Table 1 is available in the Supplementary Files section.

Download PDF

Reviewers agreed at journal
20 Jul, 2024
Reviewers invited by journal
17 Jul, 2024
Editor assigned by journal
09 Jul, 2024
First submitted to journal
05 Jul, 2024

You are reading this latest preprint version

Joint Fluid Multi-Omics Improves Diagnostic Confidence During Evaluation of Children with Presumed Septic Arthritis

Status:

Version 1

Abstract

Figures

INTRODUCTION

METHODS

Study design, patient recruitment and sampling

Sample Analysis

16S sequencing assays

Sequencing data analysis

Immune profiling assays

i.48-plex cytokine assay

ii.Autoantibody Protein array

Single cell RNA sequencing and analysis

Statistical Analysis

RESULTS

Patient characteristics and standard of care results

Rapid pathogen identification and taxonomic classification in the joint fluid

Different cytokine profile of infection positive verse infection negative children

Autoantibody (IgG) signatures in infection negative subset

DISCUSSION

CONCLUSIONS

Abbreviations

Declarations

References

Tables

Supplementary Files

Status:

Version 1