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Clinical Evaluation of Pathognomonic Salivary Protease Fingerprinting for Oral Disease Diagnosis.

Garrit KollerEva SchürholzThomas ZiebartAndreas NeffRoland FrankenbergerJörg Walter Bartsch
Published in: Journal of personalized medicine (2021)
Dental decay (Caries) and periodontal disease are globally prevalent diseases with significant clinical need for improved diagnosis. As mediators of dental disease-specific extracellular matrix degradation, proteases are promising analytes. We hypothesized that dysregulation of active proteases can be functionally linked to oral disease status and may be used for diagnosis. To address this, we examined a total of 52 patients with varying oral disease states, including healthy controls. Whole mouth saliva samples and caries biopsies were collected and subjected to analysis. Overall proteolytic and substrate specific activities were assessed using five multiplexed, fluorogenic peptides. Peptide cleavage was further described by inhibitors targeting matrix metalloproteases (MMPs) and cysteine, serine, calpain proteases (CSC). Proteolytic fingerprints, supported by supervised machine-learning analysis, were delineated by total proteolytic activity (PepE) and substrate preference combined with inhibition profiles. Caries and peridontitis showed increased enzymatic activities of MMPs with common (PepA) and divergent substrate cleavage patterns (PepE), suggesting different MMP contribution in particular disease states. Overall, sensitivity and specificity values of 84.6% and 90.0%, respectively, were attained. Thus, a combined analysis of protease derived individual and arrayed substrate cleavage rates in conjunction with inhibitor profiles may represent a sensitive and specific tool for oral disease detection.
Keyphrases
  • machine learning
  • extracellular matrix
  • oral health
  • amino acid
  • clinical evaluation
  • hydrogen peroxide
  • nitric oxide
  • deep learning
  • ultrasound guided
  • structural basis
  • loop mediated isothermal amplification