Search for


search for

Validation of Synovial Fluid Clinical Samples for Molecular Detection of Pathogens Causing Prosthetic Joint Infection Using GAPDH Housekeeping Gene as Internal Control
Biomed Sci Letters 2023;29:220-230
Published online December 31, 2023;
© 2023 The Korean Society For Biomedical Laboratory Sciences.

Jiyoung Lee1,§,*, Eunyoung Baek1,§,**, Hyesun Ahn2,**, Youngnam Park3,****, Geehyuk Kim4,*, Sua Lim1,***, Suchan Lee2,†,**** and Sunghyun Kim5,6,†,****

1Department of Research & Development, DreamDX, Busan 46252, Korea
2Joint & Arthritis Research, Himchan Hospital, Seoul 07999, Korea
3Department of Dental Hygiene, Gimcheon University, Gimcheon 39528, Korea
4Division of Risk Assessment, Bureau of Public Health Emergency Preparedness, Korea Disease Control and Prevention Agency, Cheongju 28159, Korea
5Department of Clinical Laboratory Science, College of Health Sciences, Catholic University of Pusan, Busan 46252, Korea
6Next-Generation Industrial Field-Based Specialist Program for Molecular Diagnostics, Brain Busan 21 Plus Project, Graduate School, Catholic University of Pusan, Busan 46252, Korea
Correspondence to: Sunghyun Kim. Department of Clinical Laboratory Science, College of Health Sciences, Catholic University of Pusan, Busan 46252, Korea.
Tel: +82-51-510-0569, Fax: +82-51-510-0568, e-mail:
Suchan Lee. Joint and Arthritis Research, Himchan Hospital, 120, Sinmok-Ro, Yangcheon-Gu, Seoul 07999, Korea.
Tel: +82-2-3219-9229, Fax: +82-2-2061-8605, e-mail:
*Post-Doctor, **Senior Researcher, ***Researcher, ****Professor.
§The first authors contributed equally to this work.
Received October 25, 2023; Revised November 15, 2023; Accepted November 15, 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Identification of the pathogens causing infection is important in terms of patient's health management and infection control. Synovial fluids could be used as clinical samples to detect causative pathogens of prosthetic joint infections (PJIs) using molecular diagnostic assays, therefore, normalization and validation of clinical samples are necessary. Microbial culture is considered the gold standard for all infections, including PJIs. Recently, molecular diagnostic methods have been developed to overcome the limitation of microbial culture. Therefore, guideline for validating clinical samples to provide reliable results of molecular diagnostic assays for infectious diseases is required in clinical field. The present study aimed to develop an accurate validating method of synovial fluid clinical samples using GAPDH gene as an internal control to perform the quantitative PCR TaqMan probe assay to detect pathogens causing PJIs.
Keywords : Prosthetic joint infections (PJIs), Synovial fluid (SF), Internal control, Molecular diagnosis

Prosthetic joint infections (PJIs) are infections that usually affect joint prostheses and adjacent tissues. The microbiological basis for PJIs is clinically defined, with only a small proportion of postoperative infections undergoing microbiological sampling can identify the causative pathogen (Lamagni, 2014).

The incidence of PJIs after knee arthroplasty is estimated to be approximately 1~2% (Kapadia et al., 2016). PJI is a serious complication after implant surgery and reduces quality of life by causing functional problems in the joints after infection occurs (Lee et al., 2022; Vale et al., 2023; Karczewski et al., 2019). As knee osteoarthritis (OA) can be treated via orthopedic surgery, PJIs can be surgically treated using unicompartmental knee arthroplasty (UKA) or high tibial osteotomy (HTO). UKA offers superior survivability, while HTO can delay total knee arthroplasty (TKA) by improving joint alignment and preservation (Takeuchi et al., 2010; Fu et al., 2013). The criteria for determining PJIs are important, and the 2013 Musculoskeletal Infection Society (MSIS), 2018 International Consensus Meeting (ICM), and modified MSIS criteria are defined as the criteria for determining PJIs (Parvizi et al., 2018; Patel et al., 2023; Kuiper et al., 2022). Currently, the modified MSIS criteria, which include two major and several minor criteria, are most commonly used to determine PJIs. The modified MSIS includes positive cultures, C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) of serum, white blood cell (WBC) count, leukocyte esterase (LE), polymorphonuclear neutrophil (PMN) from synovial fluid (SF), and histological analysis (Kuiper et al., 2022; Zmistowski et al., 2014).

Particularly, microbial culture tests are considered the gold standard for diagnosing all types of infections, including PJIs (Wouthuyzen-Bakker, 2023; Tarabichi et al., 2023). Novel molecular diagnostic technologies for the identification and detection of pathogens in PJIs have recently developed as a response to the limitations of microbial cultures. These technologies include specific real-time, broad-range, and molecular-level 16S rRNA-polymerase chain reaction (PCR), and next-generation sequencing (NGS) (Gatti et al., 2022). These approaches to overcome the limitations of cultures reduce the error rate and turnaround time of diagnostic methodologies (Gatti et al., 2022).

Several studies are being conducted on the normalization and validation of synovial fluid (SF) in molecular diagnostic methods using appropriate housekeeping genes (Nazet et al., 2019; Nakasa et al., 2008; Vandesompele et al., 2002). The housekeeping genes that are commonly used include 棺-actin (Kim et al., 2012; Blaschke et al., 2000), 棺-2 microglobulin (B2M) (Zhang et al., 2002), ribosomal protein L32 (RPL32) (Zhang et al., 2005), ribosomal protein, large, P0 (RPLP0) (Nazet et al., 2019), eukaryotic translation elongation factor 1 alpha 1 (EEF1A1) (Nazet et al., 2019), TATA-Box Binding Protein (TBP) (Nazet et al., 2019; Zhang et al., 2005), peptidylprolyl isomerase B (PPIB) (Nazet et al., 2019), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Nazet et al., 2019; Zhang et al., 2002; Zhang et al., 2005), and 18S rRNA (Nazet et al., 2019). Among these, GAPDH is the most widely used (Nazet et al., 2019; Nakasa et al., 2008; Kanbe et al., 2002; Xiong et al., 2018; Kasama et al., 2000; Fang et al., 2018) and a known glycolytic enzyme that catalyzes the conversion of glyceraldehyde 3-phosphate to 3-phospho glyceroyl phosphate and induces gluconeogenesis (Nazet et al., 2019; Ghasemi et al., 2017). GAPDH has been used as the internal control for normalizing differences for a target sample, particularly, to establish the relative expression of this target gene (Kanbe et al., 2002; Xiong et al., 2018). In another study using GAPDH primers as an internal control, quantitative PCR (qPCR) was performed, and nonamplified samples were excluded from case analysis (Fang et al., 2018).

Guideline for validating clinical samples to provide reliable results of molecular diagnostic assays is required in clinical field. Therefore, in the present study, the GAPDH gene design process involved designing new forward and reverse primers based on previously published research sites (Fang et al., 2018). This study aimed to develop an accurate validating method of synovial fluid clinical samples using GAPDH gene as an internal control to perform the qPCR TaqMan probe assay to detect pathogens causing PJIs.


Collection of clinical specimens

A total of 113 SF clinical samples were collected from patients with symptomatic OA and who underwent TKA, HTO, or UKA at the Mokdong Himchan Hospital (Seoul, Republic of Korea) and Incheon Himchan Hospital (Incheon, Republic of Korea) from January 2022 to October 2023. This study was approved by the Institutional Review Board (IRB) of the Catholic University of Pusan (IRB Approval No.: CUPIRB-2022-01-006).

gDNA extraction from clinical samples

Genomic DNA (gDNA) was extracted from SF using the Puregene Blood Kit (Hilden, Germany). gDNA extraction was performed according to the manufacturer's protocol. A total of 1 mL SF was mixed with 5 mL cell lysis solution and 2 mL protein precipitation solution. The mixture was centrifuged at 2,000 횞 g for 10 min to precipitate protein pellets. The supernatants were mixed with 6 mL isopropanol and centrifuged at 2,000 횞 g for 10 min to discard the supernatant and extract a DNA pellet. After washing the DNA pellet with 6 mL of 70% ethanol, 200 關L DNA hydration solution was added to dissolve the DNA component. The samples were overnighted with gentle shaking at room temperature and subsequently transferred to an E-tube. The purity and concentration of all gDNA samples were measured using a NanoDropTM 2000 spectrophotometer (Thermo Fisher Scientific, Middlesex County, MA, USA) and stored at -20꼦 before further analysis.

Identification of microbial species isolated from SF samples

Microorganisms from the SF samples were identified using the VITEK 2 system (Biomerieux, La Balme les Grottes, France) according to the manufacturer's instructions.

Design of primer pairs and TaqMan probe

To develop a new internal control detection method for validating suitable SF samples, new primers and probes for GAPDH gene were designed. To detect GAPDH during this development process, the GAPDH sequences for accession no. J04038.1, NG_007073.2, AC006064.10, and AY340484.1 of Homo sapiens were collected from GenBank ( Using the GAPDH sequence as internal control, a pair of primers and probes was designed for regions with no mismatch through multiple sequence alignment using Clustal Omega Tools (EMBL-EBI) (Table 1). After careful analysis of the GAPDH sequences, the primers were designed as forward and reverse primers. For the GAPDH gene target, the primer-probe sequences were evaluated for secondary structures and dimer-forming potential using the Oligo Analyzer software (Commercial Park Coralville, IA, USA). Then, the primer-probe sequence sets exhibiting the least secondary structures and dimer-forming potential were selected for use in qPCR and synthesized by the Macrogen Institute (Seoul, Republic of South Korea). The primers amplified approximately 290 bp of the GAPDH-specific fragment of GAPDH. Inside this region, the GAPDH-specific fluorescent probe was designed as the TaqMan probe.

Accession numbers and gene synthesis sequence of the GAPDH gene

Target gene Organism Gene accession No. Gene synthesis sequence
(Amplicon size; 230 bp)

Limit of detection (LOD) analysis

The synthesized plasmid GAPDH gene of the Homo sapiens species was used as an analytical sample to confirm the LOD of the primers and probe designed in this study. The plasmid gene was synthesized at Macrogen Institute (Seoul, Republic of Korea) by setting a specific target region of the GAPDH sequence. The absorbance for the synthesized gene was measured using a NanoDropTM 2000 spectrophotometer (Middlesex County, MA, USA) and quantified using DNA Copy Number and Dilution Calculator, a web-based program that calculates the gene copy number based on the measured absorbance.

Primers and probe specificity tests analyzed by using qPCR TaqMan probe assay

To develop an internal control of GAPDH gene for validation of synovial fluid (SF) samples, specificity tests for target and non-target substances were conducted in this study. Optimized qPCR was performed on target and non-target substances to confirm that no substances other than the internal control GAPDH were detected in clinical SF samples. The target substance is the synthesized plasmid GAPDH gene and non-target substances are Staphylococcus epidermidis ATCC (American Type Culture Collection) 35989, S. aureus ATCC 29213, Enterococcus faecalis KCTC (Korean Collection for Type Cultures) 3511, Streptococcus dysgalactiae subsp. equismilis KCTC 3098, S. pyogenes ATCC 19615, Escherichia coli O157: H7 ATCC 35150, Acinetobacter baumannii KCTC 23254, Pseudomonas aeruginosa KCTC 22063, Enterobacter cloacae ATCC 2361, Candida parapsilosis KCTC 7653, C. tropicalis KCTC 7212, C. glabrata KCTC 7219, C. albicans ATCC 10231, and Aspergillus fumigatus KCTC 6145.

Optimization of annealing temperature for qPCR TaqMan probe assay

To confirm the detection of the GAPDH gene as internal control in SF, qPCR TaqMan probe assay was performed using the designed primers and probe in triplicate. Optimal qPCR compositions containing the designed primers and probes processed with template DNA samples were subjected to thermal cycling and fluorescence detection using the CFX-96 real-time PCR system (Hercules, CA, USA). qPCR amplification was performed in a total volume of 20.0 關L containing 2 횞 Thunderbird probe quantitative PCR mixture (Toyobo, Osaka, Japan), a forward primer, a reverse primer, the template DNA. Distilled water was added to make a final volume of 20.0 關L. Thermal cycling was performed at 95꼦 for 10 min, followed by 40 cycles at 95꼦 for 15 s, 58꼦 for 30 s, and 72꼦 for 30 s.

Statistical analysis

To determine differential expression levels of DNA, statistical analysis was performed using GraphPad Prism version 8 (La Jolla, CA, USA). The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to evaluate the ability of SF DNA against the GAPDH gene as an internal control. The P-value <0.05 was considered to indicate a statistically significant difference.


Clinical samples collected from the hospitals

This study analyzed a total of 113 clinical SF samples collected from January 2022 to October 2023 from patients (49 male and 64 female) with a mean age of 68.0짹13.35 years (range, 20~88 years). After aseptically preparing the joint area, the SF was aseptically aspirated from the joint cavity using a sterile needle. For this study, 1 mL SF was required and stored at -80꼦 until further analysis (Table 2).

Clinical data on patients and PCR and microbiology culture test results

Gender Age (Mean 짹 SD, years) Surgical type
Male Female TKA HTO UKA Non-surgery group
PJI (n=4, 3.5%) 3 (6.1%) 1 (1.6%) 55.5짹20.65 3 (5.9%) 0 (0%) 0 (0%) 1 (1.8%)
Non-PJI (n=109, 96.5%) 46 (93.9%) 63 (98.4) 68.5짹12.77 48 (94.1%) 1 (100%) 6 (100%) 54 (98.2%)
Total patients (n=113, 100%) 49 (100%) 64 (100%) 68.0짹13.35 51 (100%) 1 (100%) 6 (100%) 55 (100%)

PJI, Prosthetic joint infection; SD, Standard deviation; TKA, Total knee arthroplasty; HTO, High tibial osteotomy; UKA, Unicompartmental knee arthroplasty

Amplification of GAPDH gene as internal control

To detect the GAPDH gene as an internal control in clinical SF samples, qPCR TaqMan probe assay was performed using the primers and probes designed in this study. The amplification signal was measured via qPCR using the CFX-96 real-time PCR system (Bio-Rad, Hercules, CA, USA). The cut-off value for the GAPDH gene was based on the Ct value of 30, and clinical synovial samples with Ct values below 30 were classified as GAPDH-positive, whereas samples with Ct values above 30 were classified as GAPDH-negative.

qPCR TaqMan probe assay results for the GAPDH gene

To confirm the sensitivity of primers and probe designed in this study, the GAPDH plasmid was recombined via gene synthesis (Macrogen, Seoul, Republic of Korea) (Table 1). The recombinant plasmid was diluted 10 times in eight steps from 1 횞 108 copies/關L to 1 횞 101 copies/關L through serial dilution, and qPCR was performed in triplicate for each step. A standard curve was created based on the Ct value for amplification for each sample in eight steps, and each sample was quantified. The qPCR results showed that the R2 value for the GAPDH plasmid was 0.994, indicating good linearity; however, the amplification efficiency was somewhat low at 71%. In addition, the detection limit for the target was confirmed to be 1 횞 102 copies/關L. However, when the Ct value was verified in the clinical samples, Ct values over 30 were analyzed as inadequate results. Therefore, in this study, the GAPDH-negative cut-off value was set to 돟30 (Fig. 1, Table 3).

Fig. 1. Quantitative PCR TaqMan probe assay for GAPDH gene with concentration from 1 횞 108 copies/關L to 1 횞 101 copies/關L by 10-fold serial dilution. (A) Amplification curve (X-axis: Cycles, Y-axis: RFU, Relative Fluorescence Unit) and (B) Standard curve (X-axis: Log Starting Quantity, Y-axis: Cq, quantification cycle) of the target GAPDH gene plasmids.

Mean Ct values for qPCR carried out on serial dilutions of plasmid DNA of the GAPDH gene. SD is standard deviation

No. GAPDH plasmid
Plasmid DNA content Mean Ct value SD Log value Copies per reaction (copies/關L)
1 1.782 ng 8.99 0.10 8.24 1.74횞108
2 178.2 pg 14.59 0.08 6.93 8.58횞106
3 17.82 pg 19.06 0.02 5.89 7.78횞105
4 1.782 pg 23.88 0.15 4.77 5.83횞104
5 178.2 fg 27.23 0.06 3.98 9.66횞103
6 17.82 fg 31.16 0.03 3.07 1.17횞103
7 1.782 fg 34.97 0.11 2.18 1.51횞102
8 178.2 ag 40.00 0.00 1.00 1.01횞101

Cross-reactivity test of the quantitative assessment of target DNA

When detecting target strains by qPCR, no crosswith bacteria and fungi, which are infection-causing strains. To establish GAPDH as an internal control in clinical SF samples, the primers and probe designed in this study were used to verify its effectiveness and specificity. To determine the effectiveness and specificity of GAPDH, we used the GAPDH plasmid gene as positive control and 14 reference strains identified as the dominant species causing PJIs in clinical SF samples as negative control. Therefore, 14 reference strains were used as negative controls for qPCR. Among the nine bacterial reference strains included S. epidermidis ATCC 35989, S. aureus ATCC 29213, E. faecalis KCTC 3511, S. dysgalactiae subsp. equismilis KCTC 3098, S. pyogenes ATCC 19615, E. coli O157: H7 ATCC 35150, A. baumannii KCTC 23254, P. aeruginosa KCTC 22063, E. cloacae ATCC 2361, C. parapsilosis KCTC 7653, C. tropicalis KCTC 7212, C. glabrata KCTC 7219, C. albicans ATCC 10231, and A. fumigatus KCTC 6145. GAPDH gene was thus amplified by qPCR; however, the 14 dominant bacterial and fungal reference strains that caused PJI were not (Fig. 2 and Table 4).

Fig. 2. Amplification plot of qPCR for the target synthesized GAPDH gene and negative sample including nine bacterial and five fungal reference strains. Amplification curves (X-axis: cycle, Y-axis: RFU, relative fluorescence unit) of the GAPDH plasmid gene as positive control and nine bacterial and five fungal reference strains as negative control and non-template control (NTC). Nine bacterial reference strains included S. epidermidis, S. aureus, E. faecalis, S. dysgalactiae, S. pyogenes, E. coli, A. baumannii, P. aeruginosa and E. cloacae. Five fungal reference strains included C. parapsilosis, C. tropicalis, C. glabrata, C. albicans, A. fumigatus.

Result of qPCR for GAPDH plasmid gene and reference strains in this study

Gene and reference strains Control type Real-Time PCR using design of primers and probe in This Study
Mean Ct value Standard deviation
GAPDH gene The synthesized GAPDH gene (This study) Internal control 3.94 0.45
Bacterial strains ATCC 35989 Staphylococcus epidermidis Negative control N/A*
ATCC 29213 Staphylococcus aureus Negative control N/A*
KCTC 3511 Enterococcus faecalis Negative control N/A*
KCTC 3098 Streptococcus dysgalactiae subsp. equismilis Negative control N/A*
ATCC 19615 Streptococcus pyogenes Negative control N/A*
ATCC 35150 Escherichia coli O157: H7 Negative control N/A*
KCTC 23254 Acinetobacter baumannii Negative control N/A*
KCTC 22063 Pseudomonas aeruginosa Negative control N/A*
ATCC 2361 Enterobacter cloacae Negative control N/A*
Fungal strains KCTC 7653 Candida parapsilosis Negative control N/A*
KCTC 7212 Candida tropicalis Negative control N/A*
KCTC 7219 Candida glabrata Negative control N/A*
ATCC 10231 Candida albicans Negative control N/A*
KCTC 6145 Aspergillus fumigatus Negative control N/A*

*Not detection

쟕he following abbreviations are used: KCTC, Korean Collection for Type Cultures, Taejon, Korea; ATCC, American Type Culture Collection, Rockville, MD, USA

Analysis of the VITEK 2 system

Identification of microorganisms in SF clinical samples collected from the Mokdong and Incheon Himchan Hospital was confirmed using the VITEK2 system (La Balme les Grottes, France) and the identification results were classified as culture-positive (CP) or culture-negative (CN). Out of 113 samples, 11 were culture-positive (CP) and 102 were culture-negative (CN). All 11 CP (100%, 11/11) samples were below the GAPDH cut-off value. However, 58 (56.9%, 58/102) CN samples were below the GAPDH cut-off value, whereas 44 (43.1%, 44/102) were above the GAPDH cut-off value. The bacterial species identified in the 11 CP samples were two cases of S. aureus, two of S. epidermidis, two of S. dysgalactiae, two of E. coli, one of S. marcescens, one of S. anginosus, and one of Klebsiella aerogenes (Fig. 3 and Table 5). Therefore, clinical SF samples from all 113 patients were categorized according to hospital, microbiology culture test results, PJI results, and GAPDH positivity. GAPDH-positive samples with a Ct value < 30 was confirmed in 100% of both the CP (n = 11) and PJI groups (n = 4), whereas 51.3% (n = 58) and 57.5% (n = 65) of the CN and non-PJI groups, respectively, were GAPDH-positive. However, no GAPDH-negative samples with Ct value 돟 30 were found in the CP and PJI groups, whereas 44 cases in both the CN and non-PJI groups were GAPDH-negative, accounting for 39% of all patients (Tables 6 and 7).

Fig. 3. Flowchart detailing enrolment and microorganism results and GAPDH results of study samples in PJI and non-PJI groups.

Identification of microorganisms in clinical synovial fluid samples

No. Microorganism culture tests and identification using the VITEK2 system Numbers of colonies in microbial culture
Mokdong Himchan Hospital 1 Staphylococcus aureus (Bacteria) Many
2 Staphylococcus aureus (Bacteria) Many
3 Streptococcus dysgalactiae (Bacteria) Many
4 Escherichia coli (Bacteria) Moderate
5 Staphylococcus epidermidis (Bacteria) Few
6 Staphylococcus epidermidis (Bacteria) Few
7 Serratia marcescens (Bacteria) Moderate
8 Streptococcus dysgalactiae (Bacteria) Many
9 Streptococcus anginosus (Bacteria) Few
Incheon Himchan Hospital 10 Klebsiella aerogenes (Bacteria) Few
11 Escherichia coli (Bacteria) Moderate

Clinical data on patients and qPCR and microbial culture test results

Microbiology culture test GAPDH
Positive Negative Positive Negative
PJI (n=4) 4 0 4 0
Non-PJI (n=109) 7 102 65 44
Total patients (n=113) 11 102 69 44

PJI, Prosthetic joint infection; SD, Standard deviation; TKA, Total knee arthroplasty; HTO, High tibial osteotomy; UKA, Unicompartmental knee arthroplasty

Summary of clinical sample for GAPDH gene

GAPDH positive (Ct < 30) (n=69, 100%) GAPDH negative (Ct 돟 30) (n=44, 100%) Total patients (n=113, 100%)
Hospital samples Mokdong Himchan Hospital (number of patients) 31 (44.9%) 32 (72.7%) 63 (55.8%)
Incheon Himchan Hospital (number of patients) 38 (55.1%) 12 (27.3%) 50 (44.2%)
Microbial culture tests results Culture positive, CP (number of patients) 11 (9.7%) 0 (0.0%) 11 (9.7%)
Culture negative, CN (number of patients) 58 (51.3%) 44 (39.0%) 102 (90.3%)
PJI results PJI (number of patients) 4 (3.5%) 0 (0.0%) 4 (3.5%)
non-PJI (number of patients) 65 (57.5%) 44 (39.0%) 109 (96.5%)

Comparison of GAPDH level as an internal control between PJI, non-PJI, CP, and CN groups

To determine differences between PJI and non-PJI groups for GAPDH, we compared Ct values of the target DNA extracted from the SF samples. We found that the AUC and P-values of the Ct values for GAPDH gene in SF were AUC 0.8257 (*P = 0.0322). In addition, the comparison between the CP and CN groups resulted in AUC 0.8975 (***P < 0.0001) (Fig. 4).

Fig. 4. The GAPDH gene for (A) PJI and non-PJI groups and (B) culture-positive (CP) and culture-negative (CN) groups in synovial fluid.

This study aimed to use GAPDH gene as an internal control for the validation of SF clinical samples. The process for developing internal controls in clinical studies is based on the theory that housekeeping genes have constant expression levels in all samples (Zhang et al., 2005). Inflammation in the joint area can develop into degenerative inflammation or infectious inflammation caused by aging or autoimmune disease. Then, GAPDH increases due to an increase in inflammation-related white blood cells and exfoliative cells (Cheon, 2002; Zainuddin et al., 2010). Therefore, housekeeping genes can be used as an internal control in molecular diagnostic techniques (Nazet et al., 2019; Ghasemi et al., 2017) for the normalization and validation of clinical samples. Several studies have investigated housekeeping genes, such as 棺-actin (ACTB) (Kim et al., 2012; Blaschke et al., 2000), B2M (Zhang et al., 2002), RPL32 (Zhang et al., 2005), RPLP0 (Nazet et al., 2019), EEF1A1 (Nazet et al., 2019), TBP (Nazet et al., 2019; Zhang et al., 2005), PPIB (Nazet et al., 2019), GAPDH (Nazet et al., 2019; Zhang et al., 2002; Zhang et al., 2005), and 18S rRNA (Nazet et al., 2019), with GAPDH being the most widely used (Nazet et al., 2019; Nakasa et al., 2008; Kanbe et al., 2002; Xiong et al., 2018; Kasama et al., 2000; Fang et al., 2018). In this study, primers and probes were designed for GAPDH to develop a new internal control GAPDH biomarker for SF samples. To detect GAPDH gene at this process of development, we designed new modified primers and probe that differed from the known GAPDH primers.

To confirm the specificity of the GAPDH gene set designed in this study, the GAPDH plasmid gene synthesized by the Macrogen Institute (Seoul, Republic of Korea) was used as a positive control. The cross-reactivity against the causative bacteria and fungi that cause PJIs was confirmed as a negative control. Subsequently, as a result of the cross-reactivity experiment, we found that amplification occurred only from the synthesized GAPDH plasmid (Fig. 2 and Table 4). To determine the sensitivity, 10-fold serial dilutions were performed. The minimum detection limit satisfying the GAPDH cut-off 30 criteria was 9.66 횞 103 copies/關L (mean Ct value 27.23, standard deviation 0.06) (Table 3 and Fig. 1).

In a total of 113 samples collected from patients at the Mokdong Himchan Hospital and Incheon Himchan Hospital, 4 were classified as PJI and 109 as non-PJI based on the presence or absence of infection. Additionally, the microbiology culture tests conducted using the VITEK2 system found 11 CP and 102 CN cases (Fig. 3 and Table 7). Microorganism identification revealed that all 11 CP samples (100%, 11/11) were GAPDH-positive. Of the CN samples, 58 (56.9%, 58/102) were GAPDH-positive, and 44 (43.1%, 44/102) were GAPDH-negative. Specifically, the bacterial species identified in the 11 CP samples were S. aureus (2 cases), S. epidermidis (2 cases), S. dysgalactiae (2 cases), E. coli (2 cases), S. marcescens (1 case), S. anginosus (1 case), and K. aerogenes (1 case) (Table 5). Furthermore, the microbial identification in this study showed similar results as those in a previous study (Rho et al., 2020).

Additionally, the P-value and AUC value for the PJI and non-PJI groups and CP and CN groups for the Ct values of the GAPDH biomarker were AUC 0.8257 (P = 0.0322) and AUC 0.8975 (P < 0.0001), respectively (Fig. 4). In general, the expression of housekeeping genes used as internal controls in SF was constant (Kanbe et al., 2002). However, in the study, certain samples were not amplified by internal reference control (GAPDH) in SF, and these samples were excluded from data analysis (Fang et al., 2018). Therefore, in our study, we designed the use of GAPDH gene as an internal control in and performed qPCR based on it. Further study will be conducted on the 69 GAPDH-positive cases (61.0%, 69/113). Our findings indicate that clinical SF samples could be validated using the developed GAPDH biomarker prior to the identification of causative bacteria and fungi.

When bacteria or fungi are detected, false positives may occur not only due to infection but also due to contam-ination by skin flora during sample collection, so these should be excluded. Therefore, to detect prosthetic joint infections (PJIs), the new detection kit of PJI pathogen is necessary, and normalization and validation processes of clinical samples are required.


This work was supported by Busan Techno Park by the Korea government (Busan).


There are no conflicts of interest (both financial and personal) to declare.

  1. Blaschke V, Reich K, Blaschke S, et al. Rapid quantitation of proinflammatory and chemoattractant cytokine expression in small tissue samples and monocyte-derived dendritic cells: validation of a new real-time RT-PCR technology. J Immunol Methods. 2000. 246: 79-90.
    Pubmed CrossRef
  2. Cheon YP. Expression of doc-1 in pregnant uterus of the mouse. Kor J Fertil Steril. 2002. 29: 295-302.
  3. Fang XY, Li WB, Zhang CF, et al. Detecting the presence of bacterial DNA and RNA by polymerase chain reaction to diagnose suspected periprosthetic joint infection after antibiotic therapy. Orthop Surg. 2018. 10: 40-46.
    Pubmed KoreaMed CrossRef
  4. Fu D, Li G, Chen K, et al. Comparison of high tibial osteotomy and unicompartmental knee arthroplasty in the treatment of unicompartmental osteoarthritis a meta-analysis. J Arthroplasty. 2013. 28: 759-765.
    Pubmed CrossRef
  5. Gatti G, Taddei F, Brandolini M, et al. Molecular approach for the laboratory diagnosis of periprosthetic joint infections. Microorganisms. 2022. 10: 1573.
    Pubmed KoreaMed CrossRef
  6. Ghasemi S, Mirshokraei P, Hassanpour H, et al. Identification of reliable reference genes for quantitative real-time PCR in equine fibroblast-like synoviocytes treated by doxycycline. J Equine Vet Sci. 2017. 50: 44-51.
  7. Kanbe K, Takagishi K, Chen Q. Stimulation of matrix metalloprotease 3 release from human chondrocytes by the interaction of stromal cell-derived factor 1 and CXC chemokine receptor 4. Arthritis Rheum. 2002. 46: 130-137.
    Pubmed CrossRef
  8. Kapadia BH, Berg RA, Daley JA, et al. Periprosthetic joint infection. Lancet. 2016. 387: 386-394.
    Pubmed CrossRef
  9. Karczewski D, Winkler T, Renz N, et al. A standardized interdisciplinary algorithm for the treatment of prosthetic joint infections. Bone Joint J. 2019. 101-B: 132-139.
    Pubmed CrossRef
  10. Kasama T, Kobayashi K, Yajima N, et al. Expression of vascular endothelial growth factor by synovial fluid neutrophils in rheumatoid arthritis (RA). Clin Exp Immunol. 2000. 121: 533-538.
    Pubmed KoreaMed CrossRef
  11. Kim KW, Kim HR, Park JY, et al. Interleukin-22 promotes osteoclastogenesis in rheumatoid arthritis through induction of RANKL in human synovial fibroblasts. Arthritis Rheum. 2012. 64: 1015-1023.
    Pubmed CrossRef
  12. Kuiper JW, Verberne SJ, van Egmond PW, et al. Are accuracy studies for periprosthetic joint infection diagnosis inherently flawed? And what to do with schr철dinger's hips? A prospective analysis of the alpha defensin lateral-flow test in chronic painful hip arthroplasties. Hip Pelvis. 2022. 34: 236-244.
    Pubmed KoreaMed CrossRef
  13. Lamagni T. Epidemiology and burden of prosthetic joint infections. J Antimicrob Chemother. 2014. 69: i5-i10.
    Pubmed CrossRef
  14. Lee JY, Park HC, Bae JY, et al. Current diagnostic methods for periprosthetic joint infection. Biomed Sci Letters. 2022. 28: 1-8.
  15. Nakasa T, Miyaki S, Okubo A, et al. Expression of microRNA-146 in rheumatoid arthritis synovial tissue. Arthritis Rheum. 2008. 58: 1284-1292.
    Pubmed KoreaMed CrossRef
  16. Nazet U, Schr철der A, Gr채ssel S, et al. Housekeeping gene validation for RT-qPCR studies on synovial fibroblasts derived from healthy and osteoarthritic patients with focus on mechanical loading. PLos One. 2019. 14: e0225790.
    Pubmed KoreaMed CrossRef
  17. Parvizi J, Tan TL, Goswami K, et al. The 2018 Definition of Periprosthetic Hip and Knee Infection: An Evidence-Based and Validated Criteria. J Arthroplasty. 2018. 33: 1309-1314.
    Pubmed CrossRef
  18. Patel M, Gazendam A, Wood TJ, et al. The quality of diagnostic studies used for the diagnostic criteria of periprosthetic joint infections. Eur J Orthop Surg Traumatol. 2023. 33: 2035-2048.
    Pubmed CrossRef
  19. Rho KH, Jeong HR, Kim SH, et al. The Korean surgical site infection surveillance system report, 2018. Korean J Healthc Assoc Infect Control Prev. 2020. 25: 128-136.
  20. Takeuchi R, Umemoto Y, Aratake M, et al. A mid term comparison of open wedge high tibial osteotomy vs unicompartmental knee arthroplasty for medial compartment osteoarthritis of the knee. J Orthop Surg Res. 2010. 5: 65.
    Pubmed KoreaMed CrossRef
  21. Tarabichi S, Goh GS, Zanna L, et al. Time to positivity of cultures obtained for periprosthetic joint infection. J Bone Joint Surg Am. 2023. 1105: 107-112.
    Pubmed CrossRef
  22. Vale JS, Castelo FS, Barros BS, et al. Synovial fluid biomarkers for the diagnosis of periprosthetic joint infection-a systematic review and meta-analysis of their diagnostic accuracy according to different definitions. J Arthroplasty. 2023. 14: S0883-540300650-2.
  23. Vandesompele J, De Preter K, Pattyn F, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002. 3: research0034.
    Pubmed KoreaMed CrossRef
  24. Wouthuyzen-Bakker M. Cultures in periprosthetic joint infections, the imperfect gold standard?. EFFORT Open Rev. 2023. 8: 175-179.
    Pubmed KoreaMed CrossRef
  25. Xiong H, Li W, Li J, et al. Elevated leptin levels in temporomandibular joint osteoarthritis promote proinflammatory cytokine IL-6 expression in synovial fibroblasts. J Oral Pathol Med. 2018. 48: 251-259.
    Pubmed CrossRef
  26. Zhang H, Liew CC, Marshall KW. Microarray analysis reveals the involvement of beta-2 microglobulin (B2M) in human osteoarthritis. Osteoarthritis Cartilage. 2002. 10: 950-960.
    Pubmed CrossRef
  27. Zhang X, Ding L, Sandford AJ. Selection of reference genes for gene expression studies in human neutrophils by real-time PCR. BMC Mol Biol. 2005. 6: 4.
    Pubmed KoreaMed CrossRef
  28. Zmistowski B, Della Valle C, Bauer TW, et al. Diagnosis of periprosthetic joint infection. J Orthop Res. 2014. 32: S98-S107.
  29. Zainuddin A, Chua KH, Abdul Rahim N, et al. Effect of experimental treatment on GAPDH mRNA expression as a housekeeping gene in human diploid fibroblasts. BMC Mol Biol. 2010. 11: 59.
    Pubmed KoreaMed CrossRef