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Using SWATH-MS to Identify New Molecular Biomarkers in Saliva for Detecting Periodontitis and its Response to Treatment

ABSTRACT: Over the last years, proteomic techniques have been demonstrated to be valuable tools for analysing the periodontal proteome in saliva samples, contributing to identifying extensive and comprehensive protein profiles associated with periodontitis. However, the periodontal proteome has not yet been studied using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Consequently, the objective of the present longitudinal research was to study the periodontal proteome in saliva in three clinical conditions (periodontal health, untreated periodontitis, and treated periodontitis), applying SWATH-MS to 1) analyse the periodontal proteome structure in the three clinical groups, 2) compare the differential protein expression between the three clinical groups, and 3) evaluate the diagnostic accuracy of the proteins quantified to identify new biomarkers with a high capacity to discriminate the three clinical conditions. A total of 85 subjects, 44 periodontally healthy and 41 with generalised untreated periodontitis in stages III-IV, were included, and saliva samples were collected. The diagnosis of periodontal status was conducted by two experienced dentists using clinical and radiographic criteria. The definition of periodontal status was established following the Classification of Periodontal Diseases and Conditions published in 2018. In the untreated periodontitis group, 38 underwent conventional non-surgical treatment and were evaluated clinically after two months (treated periodontitis group). In the revaluation, 36 individuals showed significant clinical improvement in the mean full-mouth probing pocket depth (PPD) value, and saliva samples were collected from them.

INSTRUMENT(S): TripleTOF 6600

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Saliva

DISEASE(S): Periodontitis

SUBMITTER: Susana Bravo

LAB HEAD: Susana Belén Bravo López

PROVIDER: PXD043491 | Pride | 2025-04-23

REPOSITORIES: Pride

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Project description:Chronic lymphocytic leukaemia (CLL) is renowned for its variable clinical course and response to therapy, suggesting a role for precision medicine. However, the molecular basis of CLL variability remains incompletely understood. Recent developments in data independent acquisition (DIA) -MS technologies, such as SWATH (Sequential Windowed Acquisition of all THeoretical fragments), provide an opportunity to study the pathophysiology of CLL at the proteome level. This report describes the sample replication and data handling requirements for SWATH-MS analysis of clinical samples. A CLL-specific spectral library compromising over 1,500,000 spectra with digital information for over 150,000 peptides has been generated to enable the quantification of up to 7736 proteins. To assess the reproducibility of the assay, SWATH-MS analysis was performed on 6 cryopreserved CLL patient samples, incorporating biological (IGHV mutational status), sample preparation and MS technical replicates. Quantitative information was obtained for 5169 proteins (<1% FDR) across 54 SWATH-MS acquisitions. These analyses showed that SWATH-MS is a highly reproducible technique. However, replicate sample preparations on different days was identified as the main source of variation. To overcome the effect of variation between sample preparation batches, different computational approaches for batch correction were tested. All approaches successfully removed technical variability whilst retaining proteomic differences between clinical subgroups. Functional enrichment analysis of proteins associated to IGHV mutational status highlighted the importance of metabolic remodelling in the biology of CLL and overlapped significantly with previous studies based on gene expression profiling. Finally, an approach to perform statistical power analysis in proteomics studies was developed. This approach could be used to drive the design of future CLL SWATH-MS studies. These fundamental requirements for DIA approaches should be widely applicable to other clinical proteomics studies.

Project description:Periodontitis is characterized by the loss of the supporting tissues of the teeth in an inflammatory-infectious context. The diagnosis relies on clinical and X-ray examination. Unfortunately, clinical signs of tissue destruction occur late in the disease progression. Therefore, it is mandatory to identify reliable biomarkers to facilitate a better and earlier management of this disease. To this end, saliva represents a promising fluid for identification of biomarkers as metabolomic fingerprints. The present study used high-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to identify the metabolic signature of active periodontitis. The metabolome of stimulated saliva of 26 patients with generalized periodontitis (18 chronic and 8 aggressive) was compared to that of 25 healthy controls. Principal Components Analysis (PCA), performed with clinical variables, indicated that the patient population was homogeneous, demonstrating a strong correlation between the clinical and the radiological variables used to assess the loss of periodontal tissues and criteria of active disease. Orthogonal Projection to Latent Structure (OPLS) analysis showed that patients with periodontitis can be discriminated from controls on the basis of metabolite concentrations in saliva with satisfactory explained variance (R2X = 0.81 and R2Y = 0.61) and predictability (Q2Y = 0.49, CV-AUROC = 0.94). Interestingly, this discrimination was irrespective of the type of generalized periodontitis, i.e. chronic or aggressive. Among the main discriminating metabolites were short chain fatty acids as butyrate, observed in higher concentrations, and lactate, gamma-amino-butyrate, methanol, and threonine observed in lower concentrations in periodontitis. The association of lactate, GABA, and butyrate to generate an aggregated variable reached the best positive predictive value for diagnosis of periodontitis. In conclusion, this pilot study showed that 1H-NMR spectroscopy analysis of saliva could differentiate patients with periodontitis from controls. Therefore, this simple, robust, non-invasive method, may offer a significant help for early diagnosis and follow-up of periodontitis.

			Action	DRS
	SWATH_library__FDR.xlsx	Xlsx
	S_H01.wiff	Wiff
	S_H01.wiff.scan	Wiff
	S_H02.wiff	Wiff
	S_H02.wiff.scan	Wiff

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Using SWATH-MS to Identify New Molecular Biomarkers in Saliva for Detecting Periodontitis and its Response to Treatment

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