A new albumin-depletion strategy improves proteomic research of gingival crevicular fluid from periodontitis patients

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.

Systems Biology in Periodontitis

Systems biology is a promising scientific discipline that allows an integrated investigation of host factors, microbial composition, biomarkers, immune response and inflammatory mediators in many conditions such as chronic diseases, cancer, neurological disorders, and periodontitis. This concept utilizes genetic decoding, bioinformatic, flux-balance analysis in a comprehensive approach. The aim of this review is to better understand the current literature on systems biology and identify a clear applicability of it to periodontitis. We will mostly focus on the association between this condition and topics such as genomics, transcriptomics, proteomics, metabolomics, as well as contextualize delivery systems for periodontitis treatment, biomarker detection in oral fluids and associated systemic conditions.

Blood Biomarkers and Serologic Immunological Profiles Related to Periodontitis in Abdominal Aortic Aneurysm Patients

BACKGROUND

Periodontitis is a chronic inflammatory gum disease associated with systemic diseases such as cardiovascular diseases.

AIM

To investigate the association of systemic blood biomarkers, C-reactive protein (CRP), levels of lipopolysaccharide (LPS), and IgG levels against periodontal pathogens Aggregatibacter actinomycetemcomitans (Aa) and Porphyromonas gingivalis (Pg) with the stability, based on the aortic diameter, the growth rate and the eligibility for surgical intervention, of patients with abdominal aortic aneurysm (AAA).

METHODS

Patients with stable AAA (n = 30) and unstable AAA (n = 31) were recruited. The anti-A. actinomycetemcomitans and anti-P. gingivalis IgG levels were analyzed by ELISA, the LPS analysis was performed by using the limulus amebocyte lysate (LAL) test, and plasma levels of CRP were determined using an immune turbidimetric method. The association between these blood systemic biomarkers, AAA features, periodontal clinical parameters and oral microbial profiles were explored. Regression models were used to test the relationship between variables.

RESULTS

The presence of antibodies against Pg and Aa, LPS and high CRP concentrations were found in all AAA patients. The IgG levels were similar in patients with stable and unstable AAA (both for Aa and Pg). Among investigated blood biomarkers, only CRP was associated with AAA stability. The amount of LPS in saliva, supra, and subgingival plaque were significantly associated with the systemic LPS (p <0.05).

CONCLUSIONS

This post-hoc study emphasizes the presence of antibodies against Pg and Aa, LPS and high CRP concentrations in all AAA patients. The presence of Pg in saliva and subgingival plaque was significantly associated with the blood LPS levels. For further studies investigating periodontitis and systemic diseases, specific predictive blood biomarkers should be considered instead of the use of antibodies alone.

A qualitative and quantitative analysis of the human gingival crevicular fluid proteome and metaproteome

Gingival crevicular fluid (GCF) is an integral part of oral fluid that plays a special role in maintaining the structure of junctional epithelium and defending against bacterial infection. In this study, we comprehensively analysed the composition of the human GCF proteome and metaproteome simultaneously to obtain multidimensional information about GCF. A total of 3680 human proteins (2540 with at least two unique peptides) were identified in the normal GCF sample, and their functions were mainly associated with immune function and inflammation. Among these proteins, 1874 proteins could be quantified by the iBAQ algorithm, and their abundances spanned a dynamic range of six orders of magnitude. For the GCF metaproteome, a total of 3082 proteins and 69 genera were found. In addition, 16 genera were not identified by GCF metagenomic analysis. Compared to the saliva metaproteome, 32 genera were found to be in common. The protein quantitative analysis showed that the abundance of GCF metaproteome contributed to approximately 4.17% of the total GCF proteome. The top three most abundant genera were Fusobacterium, Corynebacterium, and Leptotrichia. The above data will be useful for future research on GCF-related diseases.

Metaproteome and metabolome of oral microbial communities

The emergence of high-throughput technologies for the comprehensive measurement of biomolecules, also referred to as "omics" technologies, has helped us gather "big data" and characterize microbial communities. In this article, we focus on metaproteomic and metabolomic approaches that support hypothesis-driven investigations on various oral biologic samples. Proteomics reveals the working units of the oral milieu and metabolomics unveils the reactions taking place; and so these complementary techniques can unravel the functionality and underlying regulatory processes within various oral microbial communities. Current knowledge of the proteomic interplay and metabolic interactions of microorganisms within oral biofilm and salivary microbiome communities is presented and discussed, from both clinical and basic research perspectives. Communities indicative of, or from, health, caries, periodontal diseases, and endodontic lesions are represented. Challenges, future prospects, and examples of best practice are given.

Gingival Crevicular Fluid Peptidome Profiling in Healthy and in Periodontal Diseases

Given its intrinsic nature, gingival crevicular fluid (GCF) is an attractive source for the discovery of novel biomarkers of periodontal diseases. GCF contains antimicrobial peptides and small proteins which could play a role in specific immune-inflammatory responses to guarantee healthy gingival status and to prevent periodontal diseases. Presently, several proteomics studies have been performed leading to increased coverage of the GCF proteome, however fewer efforts have been done to explore its natural peptides. To fill such gap, this review provides an overview of the mass spectrometric platforms and experimental designs aimed at GCF peptidome profiling, including our own data and experiences gathered from over several years of matrix-assisted laser desorption ionization/time of flight mass spectrometry (MALDI-TOF MS) based approach in this field. These tools might be useful for capturing snapshots containing diagnostic clinical information on an individual and population scale, which may be used as a specific code not only for the diagnosis of the nature or the stage of the inflammatory process in periodontal disease, but more importantly, for its prognosis, which is still an unmet medical need. As a matter of fact, current peptidomics investigations suffer from a lack of standardized procedures, posing a serious problem for data interpretation. Descriptions of the efforts to address such concerns will be highlighted.

Host-microbe interactions: Profiles in the transcriptome, the proteome, and the metabolome

Periodontal studies using transcriptomics, proteomics, and metabolomics encompass the collection of mRNA transcripts, proteins, and small-molecule chemicals in the context of periodontal health and disease. The number of studies using these approaches has significantly increased in the last decade and they have provided new insight into the pathogenesis and host-microbe interactions that define periodontal diseases. This review provides an overview of current molecular findings using -omic approaches that underlie periodontal disease, including modulation of the host immune response, tissue homeostasis, and complex metabolic processes of the host and the oral microbiome. Integration of these -omic approaches will broaden our perspective of the molecular mechanisms involved in periodontal disease, advancing and improving the diagnosis and treatment of various stages and forms of periodontal disease.

Current Status of Proteomic Technologies for Discovering and Identifying Gingival Crevicular Fluid Biomarkers for Periodontal Disease

Periodontal disease is caused by bacteria in dental biofilms. To eliminate the bacteria, immune system cells release substances that inflame and damage the gums, periodontal ligament, or alveolar bone, leading to swollen bleeding gums, which is a sign of gingivitis. Damage from periodontal disease can cause teeth to loosen also. Studies have demonstrated the proteomic approach to be a promising tool for the discovery and identification of biochemical markers of periodontal diseases. Recently, many studies have applied expression proteomics to identify proteins whose expression levels are altered by disease. As a fluid lying in close proximity to the periodontal tissue, the gingival crevicular fluid (GCF) is the principal target in the search for periodontal disease biomarkers because its protein composition may reflect the disease pathophysiology. Biochemical marker analysis of GCF is effective for objective diagnosis in the early and advanced stages of periodontal disease. Periodontal diseases are also promising targets for proteomics, and several groups, including ours, have applied proteomics in the search for GCF biomarkers of periodontal diseases. This search is of continuing interest in the field of experimental and clinical periodontal disease research. In this article, we summarize the current situation of proteomic technologies to discover and identify GCF biomarkers for periodontal diseases.