Metaproteomics Characterizes the Human Gingival Crevicular Fluid Microbiome Function in Periodontitis

Population Proteomics: A Tool to Gain Insights Into the Inflamed Periodontium

Periodontitis, characterized by inflammatory loss of tooth-supporting tissues associated with biofilm, is among the most prevalent chronic diseases globally, affecting approximately 50% of the adult population to a moderate extent and cases of severe periodontitis surpassing the one billion mark. Proteomics analyses of blood, serum, and oral fluids have provided valuable insights into the complex processes occurring in the inflamed periodontium. However, until now, proteome analyses have been primarily limited to small groups of diseased versus healthy individuals. The emergence of population-scale analysis of proteomic data offers opportunities to uncover disease-associated pathways, identify potential drug targets, and discover biomarkers. In this review, we will explore the applications of proteomics in population-based studies and discuss the advancements it brings to our understanding of periodontal inflammation. Additionally, we highlight the challenges posed by currently available data and offer perspectives for future applications in periodontal research. This review aims to explain the ongoing efforts in leveraging proteomics for elucidating the complexities of periodontal diseases and paving the way for clinical strategies.

Data-independent acquisition in metaproteomics

INTRODUCTION

Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data.

AREAS COVERED

This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed.

EXPERT OPINION

Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.

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.