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Journal of Advanced Research logoLink to Journal of Advanced Research
. 2025 Oct 27;85:495–506. doi: 10.1016/j.jare.2025.10.059

Advanced immunodiagnostics for periodontal disease: Targeted detection of Kgp and RgpB using A7 and G1 antibody set

Hyeran Kim a,b, Kyeonghye Guk a, Soyeon Yi a, Eunkyeong Jung a, Eun-Kyung Lim a,c,d, Hwangseo Park e, Suhyeon Kim a,f, Jinsol Choi a,g, Kyu-Sun Lee a, Il Keun Kwon b, Taejoon Kang a,d,⁎, Juyeon Jung a,c,d,⁎
PMCID: PMC13316592  PMID: 41161486

Graphical abstract

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Keywords: Periodontal disease, Porphyromonas gingivalis, Gingipain, Monoclonal antibody, Lateral flow assay

Highlights

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    Monoclonal antibodies (A7, G1) specifically target Kgp and RgpB linked to periodontitis progression.

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    A7 and G1-based lateral flow assay enables rapid, non-invasive gingipain detection.

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    Clinical validation with 224 saliva samples showed 99.2% sensitivity and 100% specificity.

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    The assay may transform periodontal diagnostics with real-time, point-of-care detection.

Abstract

Introduction

Periodontal disease, primarily caused by Porphyromonas gingivalis, leads to chronic inflammation and eventual tooth loss, with systemic health implications. Current diagnostic methods rely on clinical assessment, which is insufficiently sensitive for detecting early-stage disease and cannot directly identify specific pathogens or virulence factors. Consequently, developing rapid, sensitive, and specific diagnostic tools targeting key virulence factors such as gingipains (Kgp and RgpB) is critically needed.

Objectives

This study aimed to develop monoclonal antibodies (A7 and G1) specifically targeting Kgp and RgpB for rapid, sensitive, and precise periodontal disease diagnostics using various immunodiagnostic platforms.

Methods

We generated high-affinity monoclonal antibodies through phage display technology, confirmed their specificity via surface plasmon resonance (SPR) and docking simulations, and evaluated their performance across dot blot, enzyme-linked immunosorbent assay (ELISA), colorimetric detection with Au nanoparticles (NPs), and lateral flow assays (LFAs). Clinical validation was conducted on 224 saliva samples from periodontal disease patients and healthy controls.

Results

A7 and G1 antibodies exhibited high specificity and binding affinity to Kgp and RgpB, with equilibrium dissociation constants (KD) ranging from nanomolar to sub-nanomolar. Among the diagnostic methods tested, the LFA demonstrated outstanding performance, accurately detecting Kgp and RgpB at picomolar levels, while the Au NP-based assay enabled successful visual detection. Clinical validation of the LFA showed remarkable sensitivity of 99.2% and specificity of 100%, effectively distinguishing patients from controls.

Conclusion

The developed A7 and G1 antibody-based LFA represents a significant advancement in periodontal disease diagnostics. Offering rapid, accurate, and sensitive detection directly from saliva samples, this method holds great promise for clinical use, facilitating early diagnosis and timely intervention to improve patient outcomes.

Introduction

Periodontal disease is a chronic inflammatory disorder affecting the tissues that support the teeth [1]. It is one of the primary causes of tooth loss in adults [2]. The disease progressively destroys the periodontal ligaments and alveolar bone, manifesting in forms that range from mild gingivitis to severe periodontitis [3]. If periodontitis is not adequately treated, it can result in not only tooth loss but also a range of systemic health issues, including cardiovascular disease, diabetes, and respiratory infections [4,5]. Despite its high prevalence and significant clinical impact, periodontal disease often remains asymptomatic until its advanced stages [6]. Therefore, early diagnosis and intervention are of paramount importance to prevent irreversible damage.

The standard diagnostic methods for periodontal disease include clinical assessment, periodontal probing, and radiography [1]. However, these methods are oriented toward identifying established tissue destruction, which limits their capacity to detect the early stages of disease [1,7]. To overcome these limitations, there is a need for rapid and accurate diagnostic tools that can identify periodontal disease at an early stage, ideally before clinical signs appear. Saliva is emerging as an easily accessible biofluid that contains a wealth of biological information, including microbial, inflammatory, and host response markers associated with periodontal disease [7,8]. Therefore, the development of simple, accurate and cost-effective technologies to detect biomarkers of periodontal disease in saliva can provide clinicians with meaningful information about the presence and severity of disease, with the potential to improve patient outcomes [[7], [8], [9]].

Among the plaque microbiomes associated with periodontal disease, Porphyromonas gingivalis has been identified as a major pathogen that promotes the progression of the disease [10]. P. gingivalis is highly adept at evading host immune defenses, compromising tissue integrity, and promoting the destruction of periodontal tissues, including alveolar bone [10]. Therefore, early detection and monitoring of P. gingivalis is essential for the effective management of periodontal disease [10,11]. The extracellular cysteine proteases of P. gingivalis, known as gingipains, primarily determine its pathogenicity. These enzymes fall into two categories: Arg-gingipain (Rgp), which cleaves substrates at arginine residues, and Lys-gingipain (Kgp), which cleaves at lysine residues [12,13]. Rgp has been demonstrated to cleave a variety of host proteins and immune molecules, including collagen and interleukins [12,13]. Kgp has been demonstrated to cleave fibrinogen, IL-6, and the adherence junctions of epithelial cells [14]. Additionally, it has been observed to bind hemoglobin, which serves as the organism's source of iron [15]. Given their role in host cell adhesion, epithelial barrier disruption, and immune system dysfunction, Rgp and Kgp are important targets for diagnostic and therapeutic research in periodontal disease [[12], [13], [14], [15], [16]]. Consequently, the selective detection of these proteases can provide valuable insights into the activity and severity of periodontal disease caused by P. gingivalis infection.

Currently, the detection of P. gingivalis relies on culture techniques, enzyme-linked immunosorbent assay (ELISA), and polymerase chain reaction (PCR) [10,17,18]. While these methods are effective in identifying the bacteria, they are limited in their ability to specifically detect Rgp and Kgp [10,18]. To address these limitations, there is a need for the development of a rapid, accurate, and minimally invasive biological diagnostic tool that can directly detect Kgp and Rgp. In the present study, we developed a pair of monoclonal antibodies (A7 and G1) specific to Kgp and RgpB. These antibodies showed high specificity and affinity for Kgp and RgpB, thereby facilitating accurate diagnosis of periodontal disease. We confirmed the binding properties of the antibodies using surface plasmon resonance (SPR) and docking simulations. Moreover, the developed antibodies were applied to various diagnostic platforms, including dot blot, ELISA, visual detection using Au nanoparticles (NPs), and lateral flow immunoassay (LFA), to evaluate their potential for detecting P. gingivalis protease. In particular, the developed LFA system is capable of rapidly detecting Kgp and RgpB in patient saliva samples, which is expected to have a high potential for practical application in the diagnosis of periodontal disease. The results of the 224 clinical saliva samples (126 positive and 98 negative) demonstrated that the LFA assay accurately detected periodontal disease with a sensitivity of 99.2 % and a specificity of 100 %. This evidence supports that the LFA system developed in this study is capable of diagnosing periodontal disease with a high degree of reliability in a genuine clinical context. We anticipate that the A7 and G1 antibody-based P. gingivalis protease detection platform will enhance patient outcomes in periodontal disease management by providing real-time insight into the disease and enabling prompt therapeutic intervention.

Results

Development of A7 and G1 antibodies

Monoclonal antibodies possess high affinity and specificity for antigens, making them a valuable tool in the diagnosis of a range of diseases, including infectious, cancer and autoimmune diseases [[19], [20], [21], [22], [23]]. Antibody-based immunodiagnostic technologies offer the advantage of facilitating the easy detection of disease-related biomarkers in several biological samples, including blood, urine, and saliva [[19], [20], [21], [22], [23]]. This is due to the simplicity of the sample preparation process and the speed of the detection method. In the diagnosis of periodontal disease, antibody-based immunodiagnostic technology can be a valuable adjunct to conventional clinical evaluation and radiography by providing a non-invasive, rapid and accurate approach.

A large human fragment antigen-binding (Fab) phage display library and a biopanning protocol were employed to generate A7 and G1 monoclonal antibodies that specifically recognize Kgp and RgpB [22,23]. Following the third and fourth rounds of panning, the Kgp- and RgpB-specific binders were selected from the enriched pool, respectively (Fig. 1A). These panning rounds were pivotal in the process of enhancing the specificity and affinity of the binders. The binding affinity for Kgp and RgpB was confirmed by soluble Fab expression of randomly selected clones. Fig. 1B shows the results of the monoclonal phage ELISA assay after four rounds of panning. The clones #31 and 37 were selected based on their strong binding affinity for Kgp and RgpB, and the presence of unique clones was confirmed by DNA sequencing. Subsequently, the Fab form of A7 and G1 was converted to the full immunoglobulin G (IgG) form, which was then used for the diagnosis of Kgp and RgpB (Fig. 1C). Fig. 1D illustrates the amino acid sequences of the A7 and G1 antibodies to the Kgp and RgpB antigens, with the complementary determining regions (CDRs) highlighted in red. CDRs are of particular importance in the recognition and binding of antibodies to antigens, and thus in determining the specificity and affinity of antibodies.

Fig. 1.

Fig. 1

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Development of A7 and G1 Antibodies. (A) ELISA binding analysis of phage-displayed antibodies from the panning rounds against Kgp and RgpB. Data represents as mean ± standard deviation of three measurements. (B) Screening for Kgp and RgpB specificity of monoclonal phages from fourth round of panning by ELISA. Each experiment was repeated two times independently with similar results. (C) SDS-PAGE analysis of purified A7 and G1 antibodies (IgG) showing successful expression of both heavy (HC) and light chains (LC). M, R, and NR denote protein marker, reducing and non-reducing conditions, respectively. (D) Amino acid sequences of A7 and G1 antibodies. The CDRs are highlighted in red.

Characterization of A7 and G1 antibodies

Following the production of the A7 and G1 antibodies, the binding affinity and kinetics of these antibodies with Kgp and RgpB were evaluated using SPR. The SPR analysis employed a 1:1 Langmuir binding model, and the resulting real-time binding sensograms and calculated kinetic parameters are presented in Fig. 2. The binding constant (Kon) of A7 antibody for Kgp was determined to be 5.671 × 105 1/Ms, while the dissociation constant (Koff) was found to be 2.547 × 10−3 1/s. Consequently, the equilibrium dissociation constant (KD) was calculated to be 4.490 × 10−9 M. The KD of A7 antibody for RgpB was determined to be 5.313 × 10−10 M, based on the measured Kon of 1.891 × 10⁶ 1/Ms and the Koff of 1.005 × 10−3 1/s. The KD values of G1 antibody for Kgp and RgpB were determined to be 5.943 × 10−8 M and 7.979 × 10−9 M, respectively. Overall, both the A7 and G1 antibodies exhibited high binding affinity for the Kgp and RgpB.

Fig. 2.

Fig. 2

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Binding Kinetics of A7 and G1 Antibodies to Kgp and RgpB. (A) SPR sensorgrams showing the binding kinetics of A7 antibody to Kgp across a range of concentrations (50 nM to 3.125 nM, 2-fold dilutions) (B) SPR sensorgrams showing the binding kinetics of A7 antibody to RgpB across a range of concentrations (2.5 nM to 0.156 nM, 2-fold dilutions) (C) SPR sensorgrams showing the binding kinetics of G1 antibody to Kgp across a range of concentrations (500 nM to 31.25 nM, 2-fold dilutions) (D) SPR sensorgrams showing the binding kinetics of G1 antibody to RgpB across a range of concentrations (50 nM to 3.125 nM, 2-fold dilutions). (e) The kinetic parameters of A7 and G1 antibodies for Kgp and RgpB, including Kon, Koff, and KD.

The subsequent analysis focused on the structural and energetic properties of the A7 and G1 antibodies in relation to their binding to the Kgp and RgpB. These analyses are crucial for elucidating the mechanisms underlying antibody-antigen interactions and the specific processes that contribute to the stability of the complexes. Fig. 3 depicts the binding configurations and ROSETTA energy outcomes for the four antibody-antigen complexes. The A7-RgpB complex exhibited the lowest ROSETTA energy, which was consistent with the sub-nanomolar binding affinity between them, as determined by SPR. Further structural characterization of the A7-RgpB complex revealed that RgpB binds to and stabilizes A7 via multiple intermolecular hydrogen bonds. Specifically, the backbone aminocarbonyl atom of RgpB (Ala151) forms a hydrogen bond with the Tyr54 (H-Y54) side chain of the A7 heavy chain, and the backbone amide nitrogen (Gly153) forms a hydrogen bond with the Asp52 (H-D52) side chain. In protein interactions, hydrogen bonds involving the backbone groups of interfacial residues are significant contributors to the stability of the complex. Consequently, these hydrogen bonds play a pivotal role in the stabilization of the A7-RgpB complex and contribute considerably to the maintenance of its structural robustness. Furthermore, additional hydrogen bonds were observed between the phenolic side chain of H-Y59 of A7 and the hydroxyl group of Ser156 of RgpB. These bonds may contribute to the overall stability of the complex. Additionally, the A7-RgpB complex appears to be further stabilized by van der Waals interactions between Pro155 of RgpB and Arg91, Asn93, and Leu94 of the A7 light chain. In contrast, the remaining three antibody-antigen complexes exhibited comparable ROSETTA energies. The calculated structures revealed the presence of two intermolecular hydrogen bonds in the A7-Kgp and G1-Kgp complexes, respectively, and three hydrogen bonds in the G1-RgpB complex. The G1-RgpB complex exhibited three hydrogen bonds analogous to those observed in the A7-RgpB complex. However, it is notable that these hydrogen bonds were exclusively present in the side chains and did not involve backbone groups. The absence of backbone groups may represent a limiting factor for the binding affinity of the G1-RgpB complex. It is postulated that this limitation is responsible for the lower KD value of the G1-RgpB complex in comparison to the A7-RgpB complex.

Fig. 3.

Fig. 3

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Structural Analysis of A7 and G1 Antibodies to Kgp and RgpB. (A-D) Structural models of (A) the A7 antibody bound to Kgp, (B) the A7 antibody bound to RgpB, (C) the G1 antibody bound to Kgp, and (D) the G1 antibody bound to RgpB. The carbon atoms of A7 and G1 antibodies are shown in green, while those of Kgp and RgpB are shown in black. Intermolecular hydrogen bonds are shown as dotted lines. ROSETTA energy for the four antibody-antigen complexes is written at the bottom.

Detection of Kgp and RgpB using A7 and G1 antibodies

The developed antibodies were utilized in several experimental procedures to detect Kgp and RgpB. Initially, the antibodies were evaluated through dot blot tests utilizing a nitrocellulose (NC) membrane (Fig. 4A, B). To this end, recombinant Kgp and RgpB domains were spotted at various concentrations on a NC membrane, and the binding patterns were analyzed using the A7 and G1 antibodies. RgpA and bovine serum albumin (BSA) were employed as controls to confirm the absence of non-specific binding. The dot blot analysis demonstrated that the intensity of the dots decreased in proportion to the decrease in concentration of Kgp and RgpB, thereby providing evidence that the antibodies bind to Kgp and RgpB in a concentration-dependent manner. In contrast, no dots were observed with RgpA and BSA, regardless of concentration, indicating that the A7 and G1 antibodies exhibit a high degree of specificity for Kgp and RgpB, with minimal non-specific binding.

Fig. 4.

Fig. 4

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Detection of Kgp and RgpB using A7 and G1 Antibodies. (A, B) Dot blot results for Kgp and RgpB detection using (A) A7 and (B) G1 antibodies at various concentrations. The intensity of the dots decreases with lower protein concentrations. RgpA and BSA were used as controls, showing no dots regardless of concentration. Each experiment was repeated three times independently with similar results. (C, D) Sandwich ELISA schematic (inset) and results for (C) Kgp and (D) RgpB detection. The fluorescence intensity increases linearly with higher protein concentrations. Data represents as mean ± standard deviation of three measurements. (E, F) Visual colorimetric detection of Kgp and RgpB using (E) A7-Au NP and (F) G1-Au NP probes. The solution color changes from red to purple with increasing Kgp and RgpB concentration due to NP aggregation. (G, H) Quantitative analysis of the colorimetric assays for Kgp and RgpB using (G) A7-Au NP and (H) G1-Au NP probes, showing the relationship between protein concentration and Ab600/Ab520. Data represents as mean ± standard deviation of three measurements.

Subsequently, a sandwich ELISA system for the detection of Kgp and RgpB was developed using the A7 and G1 antibody pairs (Fig. 4C, D). The sandwich ELISA is a widely utilized analytical platform for protein detection. In this system, the G1 antibody serves as the capture antibody, being immobilized on the assay plate and selectively capturing Kgp and RgpB. Next, an A7 antibody conjugated with a Cy3 fluorescent dye is employed as a detection antibody, binding to the captured Kgp and RgpB and thereby inducing a fluorescent signal. The results of the sandwich ELISA clearly demonstrated changes in the fluorescence signal in accordance with the concentration of Kgp and RgpB. Furthermore, the consistency of the signal at each concentration was confirmed. Notably, Kgp and RgpB could be successfully detected even at concentrations as low as pM. It was thus verified that the A7 and G1 antibodies exhibit high specificity and sensitivity for Kgp and RgpB, thereby enhancing the reliability and efficiency of protein detection.

We further performed an experiment to detect Kgp and RgpB by visual inspection, with A7 and G1 antibody-conjugated Au NPs (Fig. 4E, F). First, the characterization of the synthesized Au NPs and antibody-Au NPs was conducted using transmission electron microscopy (TEM), dynamic light scattering (DLS), and ultraviolet–visible (UV–Vis) spectrophotometry (Supplementary Fig. 1). The TEM image demonstrates the presence of monodisperse Au NPs with an average particle size of 40 nm. Additionally, it illustrates the formation of a thin protein layer around the Au NPs subsequent to the binding of the A7 and G1 antibodies. This thin layer is indicative of a successful antibody binding to the Au NPs. The DLS analysis yielded similar results, confirming that the hydrodynamic diameter of the Au NPs increased from 41.7 nm to 47.5 nm (A7-Au NPs) and 46.9 nm (G1-Au NPs) following antibody conjugation, as observed in the TEM analysis. The UV–Vis spectra exhibited a maximum absorbance at a wavelength of 524 nm for the Au NPs alone, while the Au NPs bound to A7 shifted to 536 nm and those bound to G1 shifted to 534 nm. Following the fabrication of the antibody-Au NP probes, the mixture of each probe and gingipains was prepared and a color change was observed. Consequently, the color of the antibody-Au NP probes altered from red to purple in the presence of Kgp and RgpB. Additionally, the UV–Vis absorption spectra demonstrated a decline in the peak at 520 nm and an augmentation in absorbance within the 600 nm region as the concentration of Kgp and RgpB increased (Supplementary Fig. 2). These spectral and color changes are a consequence of the interaction with Kgp and RgpB proteins, which resulted in the particles binding to each other and promoting aggregation. To perform a quantitative analysis, the ratio of absorbance at two wavelengths (Ab600/Ab520) was calculated according to the protein concentration (Fig. 4G, H). The Ab600/Ab520 values demonstrated a relationship with protein concentration, which substantiates the ability to detect Kgp and RgpB visually through A7-Au NPs and G1-Au NPs. Overall, these results prove the high sensitivity and specificity of A7 and G1 antibodies in detecting Kgp and RgpB across multiple platforms.

Diagnosis of periodontal disease using A7 and G1 antibody-based LFA

LFAs are a widely utilized form of diagnostics, offering rapid, specific, and intuitive results [22]. A LFA was developed for the detection of Kgp and RgpB, two major virulence factors of P. gingivalis, using monoclonal A7 and G1 antibodies and applied to the diagnosis of periodontal disease. The assembly of the LFA strip and optimization of the test components were performed, resulting in the conclusion that the G1 antibody is employed as the capture antibody in a test line on a NC membrane and the A7 antibody is conjugated with colloidal Au NPs as the detection antibody (Supplementary Fig. 3). In detail, the A7-Au NP conjugate was dispensed onto a glass fiber pad, which served as the conjugation pad. The G1 antibody was diluted in physiological saline to a concentration of 1.7 mg/mL and dispensed onto the NC membrane of the test line (T line). The anti-human IgG antibody was diluted to a concentration of 1 mg/mL and applied to the control line (C line). Once the LFA was assembled, a sample of Kgp or RgpB was applied to the sample pad. The antigen then migrated along the strip, binding to the A7-Au NP conjugate and being captured by the G1 antibody on the T line, resulting in the formation of a visible red line. The C line consistently produces a red line, irrespective of the antigen, thereby demonstrating that the kit is functioning correctly. Thus, the presence of Kgp or RgpB in the sample is indicated by the appearance of two red lines, one on the T line and one on the C line.

Fig. 5A-D illustrates the efficacy of the LFA, which was developed using A7 and G1 antibodies for the detection of Kgp and RgpB proteins. To assess the sensitivity of the LFA, a series of tests were conducted using a range of protein concentrations. Fig. 5A, C depicts the physical results of the LFA strips. As the concentration of Kgp and RgpB increases, the intensity of the T line on the LFA strips becomes more pronounced. Fig. 5B, D illustrate the quantitative relationship between the logarithmic concentration of Kgp and RgpB and the intensity of the T line. The results demonstrated a robust linear correlation between antigen concentration and signal intensity, as indicated by the high R2 values (0.9958 for Kgp and 0.997 for RgpB). These findings suggest that the A7 and G1 antibody-based LFA is capable of accurately detecting low concentrations of Kgp and RgpB, which makes it a promising tool for the early detection of periodontal disease. Moreover, the reproducibility and quantification of T line intensities over a range of concentrations illustrate the potential of the assay to be employed in a clinical setting for diagnostic and monitoring purposes. To further validate the utility of the LFA, we spiked Kgp and RgpB in saliva samples from normal individuals and attempted to detect these proteins (Supplementary Fig. 4). As anticipated, the LFA demonstrated the capacity to accurately detect Kgp and RgpB, without interference from other salivary components. Moreover, no cross-reactivity with MMP-8, IL-1β, and RgpA was observed, further confirming the high specificity of the LFA. We also demonstrated that the developed LFA can detect Kgp and RgpB in saliva and gingival crevicular fluid (GCF) (Supplementary Fig. 5). Because the use of native GCF was not feasible due to the extremely limited obtainable volume, technical complexity of collection, and potential risk of contamination with saliva, plaque, or blood, artificial GCF was used for the spike-and-recovery assays. Although artificial GCF provides a consistent and reproducible matrix for evaluating analytical performance, it does not fully replicate the protein composition or viscosity of native clinical GCF. Despite this limitation, the spike-and-recovery assays showed acceptable linearity and recovery rates across the tested concentration range. The ability of the LFA to detect Kgp and RgpB directly in complex biological samples highlights its potential as a diagnostic tool for periodontal disease, providing rapid results without the need for specialized laboratory equipment. In this regard, LFA may play an essential role in the early diagnosis and management of periodontal disease, allowing for timely intervention and treatment, and improving patient outcomes.

Fig. 5.

Fig. 5

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Diagnosis of Periodontal Disease using A7 and G1 Antibody-based LFA. (A) LFA results for Kgp detection using A7 and G1 antibody-based LFA. The T and C lines are visible, with the intensity of the T line increasing as Kgp concentration increases. (B) Quantitative analysis of T line intensity for Kgp detection, demonstrating a linear relationship with Kgp concentration (R2 = 0.9958). Data represents as mean ± standard deviation of three measurements. (C) LFA results for RgpB detection using A7 and G1 antibody-based LFA. The T and C lines are visible, with the intensity of the T line increasing as RgpB concentration increases. (D) Quantitative analysis of T line intensity for RgpB detection, demonstrating a linear relationship with RgpB concentration (R2 = 0.997). Data represents as mean ± standard deviation of three measurements. (E) Box plot summarizing the relative signal intensity of LFA for 224 clinical saliva samples. Green points represent the average intensity from three measurements of each positive sample and black points represent for negative sample. eThe central box spans from the first quartile (Q1) to the third quartile (Q3), with a horizontal line at the median (Q2). Whiskers represent the range of data points. Magenta cut-off line represents the threshold for differentiating between positive and negative samples. (F) ROC curve for the LFA system based on clinical samples, with an AUC value of 0.999, indicating excellent diagnostic performance.

Additionally, the A7 and G1-based LFA was evaluated using clinical saliva samples. Among the clinical saliva samples analyzed, the control (negative) group had a mean age of 25.5 ± 9.8 years, with 65 % females and 35 % males. In contrast, the periodontitis (positive) group had a mean age of 65.4 ± 11.8 years, with 65 % females and 35 % males. These demographic trends align with the known epidemiological characteristics of periodontitis, which is more prevalent in older adults and shows no significant sex bias [2]. Fig. 5E illustrates the results of periodontitis diagnosis using a total of 224 human saliva samples (126 positive and 98 negative). The relative signal intensity is defined as the ratio of the T line intensity of the clinical samples and the intensity of the blank samples. The cut-off value was determined to be 1.175. The LFA images for all samples are presented in Supplementary Fig. 6. The LFA strap diagnostics exhibited a false-negative (FN) for only one sample (P24) out of 224 clinical samples. These results were then compared to the hospital diagnostic status of periodontitis to assess the clinical sensitivity and specificity of the developed LFA. The radiographs of patients with periodontal disease and healthy controls are shown in Supplementary Fig. 7, 8. The sensitivity and specificity of the test is calculated using the following formula:

Sensitivity (%) = (TP/(TP + FN)) × 100
Specificity (%) = (TN/(TN + FP)) × 100

TP, TN, and FP are true-positive, true-negative, false-positive, respectively. The LFA method exhibited clinical sensitivity of 99.2 % and specificity of 100 % for periodontal disease from saliva samples. Fig. 5F depicts the receiver operating characteristic (ROC) curve of the LFA system for periodontitis. A ROC curve graphically represents the relationship between a diagnostic technique's sensitivity and specificity, quantifying the overall performance of the tool with an area under the curve (AUC) value. A diagnostic tool that is closer to the upper left corner of the ROC curve performs better. Fig. 5F demonstrates that the A7- and G1-based LFA is capable of diagnosing periodontitis with high precision. The AUC value was found to be 0.999, indicating that the LFA is a highly reliable tool for discriminating periodontitis patients from non-patients. Furthermore, to evaluate the LFA's performance across different stages of periodontitis, we stratified the results based on salivary MMP-8 concentrations, a biomarker for disease severity [24]. The LFA signal intensity showed a strong positive correlation with MMP-8 levels measured by ELISA (R = 0.8721, p < 0.0001), confirming its utility in classifying disease severity (Supplementary Fig. 9). In conclusion, this LFA can be used for the detection of Kgp and RgpB in saliva with a point-of-care (POC) setting, which may play an important role in the diagnosis of periodontal disease and evaluation of treatment effectiveness. The dot blot and G1-Au NP probe results of the clinical saliva samples were also in accordance with the diagnosis of periodontitis in the hospital, thereby further confirming the ability of Kgp and RgpB detection based on A7 and G1 antibodies (Supplementary Fig. 10–13).

Discussion

Periodontal disease represents a significant cause of tooth loss in adults [1]. Despite its prevalence and severity, clinicians still face challenges in simple biological diagnosis, which impedes effective management strategies. Conventional diagnostic techniques, such as clinical examination and radiography, are inadequate for detecting the initial stages of periodontal disease, as they are only capable of identifying tissue destruction after it has already occurred. Periodontal disease is closely associated with P. gingivalis. This pathogen is dominant in the onset and progression of the disease, thereby serving as a biomarker for periodontitis [[25], [26], [27]]. In this study, we developed monoclonal antibodies, A7 and G1, which target the key virulence factors of P. gingivalis, Kgp and RgpB. We applied these antibodies to various diagnostic methods, enabling rapid and accurate detection of Kgp and RgpB. In particular, the A7 and G1-based LFA demonstrated excellent assay performance with high reproducibility (coefficient of variation (CV) = 5.05 % based on 15 independent replicates, Supplementary Fig. 14). We validated it in 224 saliva samples and achieved 99.2 % sensitivity, 100 % specificity, and an AUC of 0.999, demonstrating outstanding clinical accuracy. These results indicate that the antibody-based LFA can be employed as a reliable diagnostic tool in clinical settings, representing a potential breakthrough in the management of periodontal disease.

At present, a number of technologies are available for the detection of P. gingivalis; however, none are specifically designed to detect Kgp and RgpB. As P. gingivalis can also be found in the oral cavities of individuals without periodontitis, it is difficult to accurately diagnose periodontitis by detecting only this bacterium. Targeting the primary toxic factors that drive periodontitis progression can enhance diagnostic accuracy. Kgp and RgpB are cysteine proteases uniquely expressed by P. gingivalis [28,29]. PCR confirmed this specificity, as it amplified only P. gingivalis DNA without cross-reactivity (Supplementary Fig. 15). Kgp is one of the principal pathogens implicated in the initiation of periodontitis [26]. Note that RgpB plays a more significant role than RgpA for the diagnosis of periodontitis [30]. Both RgpA and RgpB are gingipain proteins of P. gingivalis, which possess the function of cleaving arginine [30]. However, due to the structural and biochemical differences between these two enzymes, RgpB is considered to be a more clinically important marker [30]. RgpB plays a significant role in the maintenance of P. gingivalis pathogenicity by contributing to its penetration into tissues and evasion of the host's immune defences [30,31]. Moreover, RgpB interacts with a variety of host proteins, resulting in a robust inflammatory response in periodontal tissues and accelerating the progression of periodontitis [30,31]. Conversely, RgpA displays relatively low activity with regard to tissue destruction and the induction of inflammatory responses [30]. Furthermore, previous research demonstrated that RgpB is more highly expressed on the surface of P. gingivalis, facilitating its detection in saliva samples [12]. This indicates that the detection of RgpB in saliva samples exhibits greater sensitivity and specificity than the detection of RgpA. The PCR and ELISA results showed a strong correlation between P. gingivalis copy number and gingipain protein expression, underscoring the value of gingipain-targeted assays for precise diagnosis (Supplementary Fig. 16) [32]. Since the present technology is capable of detecting Kgp and RgpB specifically and simply, it may facilitate a more accurate diagnosis of periodontitis.

While various methods for detecting periodontitis pathogens exist, they often face a trade-off between accuracy and practicality for POC use. Highly accurate molecular methods like PCR and immunoassays such as ELISA are limited by their need for laboratory infrastructure, specialized personnel, and long turnaround times [[33], [34], [35]]. Conversely, conventional LFAs, while rapid and user-friendly, typically suffer from lower sensitivity and limited quantitative capability [36,37]. Emerging imaging modalities are also promising but face their own constraints in practicality for routine diagnosis [38]. This established diagnostic gap highlights the clinical need for a tool that combines high performance with POC convenience. In this context, the developed antibody-based LFA presents a significant advantage by integrating high sensitivity and specificity with the speed and simplicity required for effective chairside diagnostics.

Nevertheless, this study is not without limitations. Firstly, while the developed LFA and antibody-based diagnostic tools have demonstrated promising results in clinical trials, their performance should be further verified through large-scale clinical trials. In particular, the study needs to be expanded to include patients from a variety of populations and at different stages of periodontal disease. Secondly, this study concentrated on specific proteins (Kgp and RgpB), but given the complex etiology of periodontitis, combined detection with other related biomarkers is likely to further enhance the sensitivity and specificity of diagnosis. Therefore, future studies will validate the efficacy of the developed diagnostic technology using a broader spectrum of clinical samples and investigate the potential of applying this technology to other oral diseases beyond periodontal disease. We will enhance the diagnostic precision by integrating additional biomarkers and develop it into an integrated diagnostic platform that can facilitate personalized treatment strategies.

Methods

Ethical statements

This study met the criteria for IRB exemption for human derived materials research and received an IRB exemption confirmation number (P01-202410-02-004), and the overall procedures followed institutional guidelines. The biospecimens and data used for this study were provided by the Biobank of Seoul National University Dental Hospital and the Biobank of Pusan National University Dental Hospital, both members of the Korea Biobank Network (project No.2024ER050700). All specimens were anonymized with a unique number, and access was restricted to only relevant researchers.

Materials

Unless otherwise indicated, all chemicals were procured from Sigma-Aldrich (St Louis, MO, USA). Recombinant P. gingivalis Kgp and RgpB antigens were purchased from Cusabio (Houston, TX, USA). The Biacore reagents and Cy3 mono NHS ester were procured from Cytiva (Marlborough, MA, USA). The NC membrane (CNPF-SN12, 10 μm pore size), pertinent to the LFA strips, was obtained from MDI (Camp Hill, PA, USA). The polyester conjugate pad, sample pad, and absorbent pads were purchased from Boreda Biotech (Gyeonggi-do, Republic of Korea). Prevotella intermedia (ATCC 25611), P. gingivalis (ATCC 33277), Aggregatibacter actinomycetemcomitans (ATCC 33384), and Streptococcus mutans (ATCC 25175) were purchased from the American Type Culture Collection (ATCC). Artificial GCF was purchased from Chemazone (Ave, Canada).

Development of A7 and G1 antibodies

A human Fab synthetic library comprising 1012 distinct mutated sequences across six CDRs was employed for antibody selection and four rounds of bio panning were conducted [[20], [21], [22]]. In brief, 100 μg/mL of purified Kgp and RgpB antigens were coated on an immune tube for 12 h at 4 °C. Subsequently, blocking was performed with 5 % skim milk (Thermo Fisher Scientific, Waltham, MA, USA) in Dulbecco's phosphate-buffered saline (DPBS) for 1 h at 37 °C. One mL of Fab phage library was added to the blocked immune tube for 2 h at 37 °C to allow for binding. The tube was then washed 10 times with DPBS containing 0.05 % Tween 20 (Thermo Fisher Scientific). The remaining phage was eluted with 1 mL of Tris-glycine-HCl (0.1 M, pH 2.5), followed by a 10 min incubation period. The eluted phage was neutralized with 1 M Tris-HCl (pH 9.0) and infected with 10 mL of TG1 cells optical density (OD) of 0.6 (OD600 = 0.6) for 30 min at 37 °C. When the number of panning was increased, the panning method remained consistent, and the number of washes was increased by a factor of ten. The clones that had been amplified in the fourth panning were randomly selected, and single colonies were cultured to an OD of 0.6. They were then incubated with isopropyl-β-D-thiogalactopyranoside (IPTG) and cultured at 37 °C for 12 h. The supernatant was subjected to indirect ELISA to select Kgp- and RgpB-specific Fab colonies. The absorbance at 450 nm was measured using a microplate reader (Tecan for life sciences, Zurich, Switzerland) to determine the success of the selection process [39,40].

Selected Kgp- and RgpB-specific Fab fragments were amplified by overlap extension PCR. The VK and VH with leader sequences were cloned into the HindIII-BsiWI and ApaI-EcoRI sites, respectively, in the antibody expression vector (pdCMVdhfrC), which contains the human constant region of the γ1 heavy chain (Cγ1) and Cκ gene. For whole antibody expression, the antibodies were transfected in Expi 293F™ cells using the 293 Transfection ExpiFectamine™ reagent (Thermo Fisher Scientific), following the manufacturer's instructions. The Expi 293F cells were cultivated in Ex-cell 293 serum-free medium for an additional five days at 37 °C under 8 % CO2 conditions. The IgG was purified from the collected supernatant using affinity chromatography on protein A (Millipore, Burlington, MA, USA). The purified antibody was subsequently neutralized and dialyzed with DPBS. Purified antibodies were analyzed by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) under both reducing and non-reducing conditions.

Characterization of A7 and G1 antibodies

SPR measurements were conducted on a Biacore T200 [41]. The running buffer was HBS-EP+ (0.01 M HEPES pH 7.4, 0.15 M NaCl, 3 mM EDTA, and 0.05 % surfactant P20), while the washing solution was 50 mM NaOH and the regeneration solution was 10 mM glycine-HCl (pH 1.5). All solutions were freshly prepared and filtered through a 0.22 μm pore-size filter. The antibodies were immobilized on the CM5 sensor chip via amine coupling, in accordance with the manufacturer's instructions, resulting in an immobilization level of approximately 200 RU. Following the immobilization process, 2-fold dilutions of Kgp and RgpB were introduced into the chip at a flow rate of 30 μL/min, with an association time of 300 s and a dissociation time of 600 s. A sensorgram was obtained by plotting the SPR angle against time. The antibody- Kgp and RgpB binding events were analyzed from a series of sensorgrams collected at different Kgp and RgpB concentrations, and kinetic constants were calculated using the 1:1 Langmuir binding model with the Biacore T200 Evaluation Software.

Homology modeling of A7 and G1 antibodies and docking simulations of Kgp and RgpB in the CDRs of antibodies

The three-dimensional structures of A7 and G1 antibodies were derived through homology modeling using the latest version of the MODELLER program (version 10.5) [41]. The X-ray crystal structures of the Fab fragments from the D12 antibody (PDB entry: 8AS0) and a phage-derived antibody (PDB entry: 2QQN) were employed as structural templates for the A7 and G1 antibodies, respectively. The high degree of amino acid sequence identity between the A7 and G1 antibodies and their respective templates (88.4 % and 87.2 %, respectively) led to the expectation of high-quality structures through homology modeling. In the homology modeling process, we employed conjugate gradient methods for structural optimization and molecular dynamics simulations to minimize spatial restraint violations. The atomic coordinates for the gap regions were optimized from a randomized and distorted structure to connect the two anchor points, as executed by the MODELLER program. Loop structures were modeled using an enumeration algorithm to enhance the accuracy of the predicted structure.

To estimate the binding affinities and binding modes of A7 and G1 antibodies with respect to the target proteins, docking simulations were performed using the high-resolution X-ray crystal structures of Kgp (PDB entry: 4RBM) and RgpB (PDB entry: 1CVR). These simulations were conducted with the RosettaDock program, which employs a multi-scale Monte Carlo-based algorithm. To obtain the putative antibody-antigen complexes, each antibody was translated and rotated to align with the fixed structure of the antigen. The search for preferred binding modes entailed the refinement of the side-chain positions of A7 and G1 antibodies through rotamer packing and explicit minimization of rigid-body displacements, employing gradient-based methods. The binding energy evaluation employed a comprehensive function that incorporated van der Waals forces, electrostatic interactions with a relatively low weighting, implicit Gaussian solvation, orientation-dependent hydrogen bonding, and side-chain rotamer probabilities. From the 1,000 binding configurations generated during the docking simulations, the configuration with the lowest binding energy was selected as the putative structural model for each antibody-antigen complex.

Dot blot

A series of recombinant protein concentrations (Kgp, RgpB, RgpA, and BSA) were applied to a NC membrane (Bio-Rad, Hercules, CA, USA) in the center [23]. Subsequently, the membranes were blocked with 5 % dry milk in TTBS (50 mM Tris, 0.5 M NaCl, 0.05 % Tween-20, pH 7.4) for 1h at 25 °C. Next, the membrane was incubated with the A7 and G1 antibodies for 1 h at 25 °C in TTBS. Following five washes with TTBS, the bound antibody was detected using goat anti-human IgG Fc cross-adsorbed horseradish peroxidase (HRP) (31413, Thermo Fisher Scientific). The membrane was then washed five times with TTBS and subsequently detected with West femto sensitivity substrate reagents (34094, Thermo fisher Scientific). The dot blot assay of the clinical sample was analyzed using the same procedure.

ELISA

Each well of a 96-well ELISA plate was coated with 100 µL of G1 antibody (1 µg/mL) in 50 mM sodium carbonate buffer (pH 9.6) for 12 h at 4°C. A 200 µL aliquot of blocking buffer was added to each well and incubated for 1 h at 37°C. The solution was then removed, and the plate was washed with 0.05 % PBST. Following the addition of 100 µL of a solution of recombinant Kgp or RgpB, which had been serially diluted with phosphate-buffered saline (PBS), and subsequent incubation for 1 h at 37 °C, the supernatant was removed from each well, and the plate was washed three times with 0.05 % PBST [21]. Each well was incubated for 1 h with a solution of 100 µL of A7 antibody covalently coupled with the Cy3 mono NHS ester. Following three washes, the fluorescence intensities (excitation at λ = 560 nm and emission at λ = 590 nm) were measured using a microplate reader (Tecan for life sciences, Mannedorf, Switzerland). A fluorescence image was acquired using the Amersham Typhoon scanner (Cytiva).

The presence of MMP-8 in clinical samples was confirmed using a commercially available ELISA kit (Human MMP8 ELISA, Thermo Fisher Scientific), following the manufacturer’s instructions. A concentration-dependent increase in gingipain protein was quantified in culture supernatants (104 – 106 CFU/mL) using a sandwich ELISA with G1 antibody as the capture antibody and A7 antibody as the detection antibody. In brief, 96-well plates were coated with G1 antibody (1 µg/mL in 50 mM sodium carbonate buffer, pH 9.6) overnight at 4 °C, blocked for 1 h at 37 °C, and incubated with culture supernatants. Plates were subsequently treated with A7 antibody, followed by HRP-conjugated anti-mouse antibody. Color development was performed with TMB substrate, and the reaction was terminated with 1 M H2SO4. Absorbance was measured at 450 nm using a microplate reader (Tecan for life sciences).

PCR

Oral bacterial species (P. intermedia, A. actinomycetemcomitans, and S. mutans) were cultured under standard conditions alongside Porphyromonas gingivalis (ATCC 33277). PCR was performed using genomic DNA extracted from cells with the Genomic DNA Isolation Kit (Qiagen, MD, USA). Reactions were conducted in triplicate with Power SYBR Green PCR Master Mix (Applied Biosystems, MA, USA) on a CFX96 Real-Time PCR Detection System (Bio-Rad). The following primer sets were employed: 16S rRNA (F: TGTAGATGACTGATGGTGAAA, R: ACTGTTAGCAACTACCGATGT), kgp (F: AGCTGACAAAGGTGGAGACCAAAGG, R: TGTGGCATGAGTTTTTCGGAACCGT), and rgpB (F: CGCTGATGAAACGAACTTGA, R: CTTCGAATACCATGCGGTT). Specific amplification of P. gingivalis DNA was confirmed by agarose gel electrophoresis, which yielded distinct bands for kgp and rgpB, whereas no amplification was observed in non-P. gingivalis samples.

Colorimetric detection of Kgp and RgpB using A7-Au NP and G1-Au NP probes

To prepare A7 or G1-conjugated Au NPs, the antibody (10 μg) was added to a mixture of 1 mL of a 40 nm Au NP colloid and 0.1 mL of a 0.1 M borate buffer solution at a pH of 8.5. Subsequently, 0.1 mL of 1 % BSA was added to the mixture, which was then incubated for an additional 45 min at 25 °C to block the surface of the Au NPs. Next, the mixture was subjected to centrifugation (7,000 × g for 20 min at 4 °C) to remove the supernatant. The centrifugation process was repeated on two occasions. The final conjugates were resuspended in deionized water and stored at 4 °C until required for use. A variety of techniques were employed to characterize antibody-Au NPs, including UV–Vis spectroscopy (NanoDrop, Thermo Fisher Scientific), DLS (ELS-Z, Otsuka Electronics, Osaka, Japan), and TEM (Tecnai G2 Spirit Twin, FEI Company, Hillsboro, OR, USA) [22].

For the detection of Kgp and RgpB, 100 μL of a 5-fold concentrated A7 or G1-Au NPs solution was added to 50 μL of a different dilution of Kgp and RgpB solution. This mixture was then incubated for 30 min at 37 °C. Subsequently, a discernible alteration in color was observed, indicative of Kgp and RgpB presence. Ultimately, the corresponding UV–Vis absorption spectra of the mixture were obtained in the wavelength range of 400 – 700 nm using a microplate reader (Tecan for life sciences). The clinical sample was analyzed using the same procedure.

Diagnosis of periodontal disease using A7 and G1 antibody-based LFA

The LFA test strip is comprised of four components: a sample pad, an NC membrane, a conjugation pad, and an absorbance pad [22]. The sample pad was saturated with a PBS solution containing 1 % (w/v) BSA and 0.2 % Tween 20. The pad was then allowed to dry at 37 °C and stored in a desiccator for future use. The A7-Au NP probes were added to the conjugation pad. The G1 antibody (1.7 mg/mL) and goat anti-mouse IgG (1 mg/mL) were immobilized on the NC membrane as the test and control lines, respectively. Once the NC membrane had dried, it was blocked with a PBS solution containing sucrose, 0.06 % BSA, and Tween 20. Once all the components had been assembled on the backing card, the test strip was assembled on a strip cassette. During the assays, 100 µL of the Kgp and RgpB samples were directly applied to the sample pad, and this volume was distributed across the entire strip within 10 min. The LFA was also evaluated using mock and clinical samples. A total of 224 saliva samples were collected from individuals diagnosed with periodontitis (n = 126) and from orally healthy individuals (n = 96). Patients with periodontitis were diagnosed and categorized based on clinical examination and dental X-ray (radiographs). Prior to sample collection, participants were instructed to refrain from eating, drinking anything other than water, and performing oral hygiene procedures for 1 h. The collected saliva samples were centrifuged at 1,000 × g for 10 min to remove debris, and the collected samples were frozen at −80 °C until use.

Statistics and reproducibility

The clinical sample size was determined according to the Korean Food & Drug Administration (KFDA). No data was excluded from the analysis. Samples were randomly allocated. The investigators were blinded during data acquisition and analysis of clinical samples. The data was presented as the mean value from each sample with its standard deviation using GraphPad Prism 10. All experiments, except antibody screening (n = 2), were repeated 3 times. ROC curves were generated using GraphPad Prism 10, employing the Wilson/Brown method with a 95 % confidence interval.

Data availability

Source data are provided with this paper.

Ethical statements

This study met the criteria for IRB exemption for human derived materials research and received an IRB exemption confirmation number (P01-202410-02-004), and the overall procedures followed institutional guidelines. The biospecimens and data used for this study were provided by the Biobank of Seoul National University Dental Hospital and the Biobank of Pusan National University Dental Hospital, both members of the Korea Biobank Network (project No.2024ER050700). All specimens were anonymized with a unique number, and access was restricted to only relevant researchers.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was supported by NRF and NST grants funded by Korea government (MSIT) (2021M3H4A1A02051048, 2023R1A2C2005185, RS-2024-00438316, RS-2024-00348576, RS-2024-00459749, RS-2025-00554718, 2020R1A5A1018052, and GTL25061-000), KEIT grants funded by Korea government (MOTIE) (RS-2022-00154853, RS-2024-00432382, and RS-2024-00403563), KEITI grant funded by Korea government (ME) (2021003370003), KHIDI grant funded by Korea government (MOHW) (RS-2025-02213315), IPET grant funded by Korea government (MAFRA) (RS-2024-00401639), and KRIBB Research Initiative Program (KGM1322511, KGM1032511).

Author contributions

Hyeran Kim and Kyeonghye Guk designed and conducted the experiments, analyzed data, and prepared the initial draft of the manuscript. Soyeon Yi and Eunkyeong Jung assisted in experimental execution and data analysis. Eun-Kyung Lim, Kyu-Sun Lee, and Il Keun Kwon provided supervision and contributed to the interpretation of the results. Hwangseo Park supported the computational modeling. Suhyeon Kim and Jinsol Choi contributed to the technical methodology. Taejoon Kang and Juyeon Jung supervised the overall project, conceptualized the research, secured funding, and revised the manuscript. All authors reviewed and approved the final version of the manuscript.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jare.2025.10.059.

Contributor Information

Taejoon Kang, Email: kangtaejoon@kribb.re.kr.

Juyeon Jung, Email: jjung@kribb.re.kr.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary Data 1
mmc1.pdf (2.1MB, pdf)

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Associated Data

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Supplementary Materials

Supplementary Data 1
mmc1.pdf (2.1MB, pdf)

Data Availability Statement

Source data are provided with this paper.

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