A comparative proteomic analysis to define the influencing factors on gingival crevicular fluid using LC-MS/MS

A simplified protocol for deep quantitative proteomic analysis of gingival crevicular fluid for skeletal maturity indicators

Assessment of craniofacial skeletal maturity is of great importance in orthodontic diagnosis and treatment planning. Traditional radiographic methods suffer from clinician subjectivity and low reproducibility. Recent biochemical methods, such as the use of gingival crevicular fluid (GCF) protein biomarkers involved in bone metabolism, have provided new opportunities to assess skeletal maturity. However, mass spectrometry (MS)-based GCF proteomic analysis still faces significant challenges, including the interference of high abundance proteins, laborious sample prefractionation and relatively limited coverage of GCF proteome. To improve GCF sample processing and further discover novel biomarkers, we herein developed a single-pot, solid-phase-enhanced sample-preparation (SP3)-based high-field asymmetric waveform ion mobility spectrometry (FAIMS)-MS protocol for deep quantitative analysis of the GCF proteome for skeletal maturity indicators. SP3 combined with FAIMS could minimize sample loss and eliminate tedious and time-consuming offline fractionation, thereby simplifying GCF sample preparation and improving analytical coverage and reproducibility of the GCF proteome. A total of 5407 proteins were identified in GCF samples from prepubertal and circumpubertal groups, representing the largest dataset of human GCF proteome to date. Compared to the prepubertal group, 61 proteins were differentially expressed (31 up-regulated, 30 down-regulated) in the circumpubertal group. The six-protein marker panel, including ATP5D, CLTA, CLTB, DNM2, HSPA8 and NCK1, showed great potential to predict the circumpubertal stage (ROC-AUC 0.937), which provided new insights into skeletal maturity assessment.

Accuracy of periodontitis diagnosis obtained using multiple molecular biomarkers in oral fluids: A systematic review and meta-analysis

AIM

To determine the accuracy of biomarker combinations in gingival crevicular fluid (GCF) and saliva through meta-analysis to diagnose periodontitis in systemically healthy subjects.

METHODS

Studies on combining two or more biomarkers providing a binary classification table, sensitivity/specificity values or group sizes in subjects diagnosed with periodontitis were included. The search was performed in August 2022 through PUBMED, EMBASE, Cochrane, LILACS, SCOPUS and Web of Science. The methodological quality of the articles selected was evaluated using the QUADAS-2 checklist. Hierarchical summary receiver operating characteristic modelling was employed to perform the meta-analyses (CRD42020175021).

RESULTS

Twenty-one combinations in GCF and 47 in saliva were evaluated. Meta-analyses were possible for six salivary combinations (median sensitivity/specificity values): IL-6 with MMP-8 (86.2%/80.5%); IL-1β with IL-6 (83.0%/83.7%); IL-1β with MMP-8 (82.7%/80.8%); MIP-1α with MMP-8 (71.0%/75.6%); IL-1β, IL-6 and MMP-8 (81.8%/84.3%); and IL-1β, IL-6, MIP-1α and MMP-8 (76.6%/79.7%).

CONCLUSIONS

Two-biomarker combinations in oral fluids show high diagnostic accuracy for periodontitis, which is not substantially improved by incorporating more biomarkers. In saliva, the dual combinations of IL-1β, IL-6 and MMP-8 have an excellent ability to detect periodontitis and a good capacity to detect non-periodontitis. Because of the limited number of biomarker combinations evaluated, further research is required to corroborate these observations.

Quantitative proteomic analysis of gingival crevicular fluids to identify novel biomarkers of gingival recession in orthodontic patients

OBJECTIVE

To identify gingival recession-related biomarkers in orthodontic patients, we compared the proteome of gingival crevicular fluids (GCF) from healthy gingiva without orthodontic treatment (GH), healthy gingiva undergoing orthodontic treatment (OGH), and recessed gingiva undergoing orthodontic treatment (OGR).

METHODS

GCF samples were obtained from the anterior teeth of 15 volunteers (n = 5/group). Quantitative proteomic analysis was performed using DIA-based liquid chromatography-tandem mass spectrometry (LC-MS/MS). Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to annotate differentially expressed proteins (DEPs). Receiver-operating characteristic (ROC) analysis was performed to detect and filter biomarker candidates, while Protein-Protein Interaction (PPI) Networks were utilized to determine the interactions between these DEPs.

RESULTS

A total of 253, 238, and 101 DEPs were found in OGR vs. OGH, OGR vs. GH, and OGH vs. GH groups, respectively. Based on the Venn diagram of three groups, 128 DEPs in OGR vs. OGH group were identified as specific proteins associated with progressive gingival recession (GR) during orthodontic treatment. Molecular function analysis showed that 128 DEPs were enriched in "molecular binding", including antigen binding, RNA binding, double-stranded RNA binding, cadherin binding involved in cell-cell adhesion, vinculin binding, S100 protein binding, and Ral GTPase binding. The majority of these DEPs were also involved in cytoskeletal regulation. In addition, biological process analysis showed an enrichment in translation, while cellular component analysis indicated that 128 DEPs were related to extracellular exosome. Furthermore, Ribosome and Phagosome were the top two terms in KEGG analysis. The results of ROC analysis demonstrated that 26 proteins could be potential biomarker candidates for GR. PPI networks analysis predicted that IQGAP1, ACTN1, TLN1, VASP, FN1, FERMT3, MYO1C, RALA, RPL35, SEC61G, KPNB1, and NPM1 could be involved in the development of GR via cytoskeletal regulation.

CONCLUSIONS

In summary, we identified several GCF proteins associated with GR after orthodontic treatment. These findings could contribute to the prevention of GR in susceptible patients before the initiation of orthodontic treatment.

SIGNIFICANCE

Orthodontic patients with GR often report esthetic defects or root hypersensitivity during orthodontic treatment, especially at the anterior teeth site. GCF, rich in protein, is an easily accessible source of potential biomarkers for the diagnosis of periodontal diseases; however, little is known about the changes in GCF proteome associated with GR in orthodontic patients. In this study we firstly used DIA-based LC-MS/MS to evaluate the proteome and to identify the biomarker candidates for GR in orthodontic patients. These findings will improve our understanding of GR during orthodontic treatment, and could contribute to an earlier diagnosis, or even prevention, of GR in susceptible populations before orthodontic treatment.