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Robust Ligature-Induced Model of Murine Periodontitis for the Evaluation of Oral Neutrophils
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Molecular differences between chronic and aggressive periodontitis.

M Kebschull1, P Guarnieri, R T Demmer

  • 1Division of Periodontics, Section of Oral and Diagnostic Sciences, Columbia University College of Dental Medicine, New York, NY, USA.

Journal of Dental Research
|October 15, 2013
PubMed
Summary
This summary is machine-generated.

Molecular profiling revealed minimal gene expression differences between chronic (CP) and aggressive periodontitis (AgP). These findings suggest limited biological distinctions between established CP and AgP lesions, paving the way for new classification methods.

Keywords:
classificationgene expressionmachine learningmicroarray analysispathogenesistranscriptome

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Area of Science:

  • Periodontology
  • Genomics
  • Immunology

Background:

  • Chronic periodontitis (CP) and aggressive periodontitis (AgP) are major forms of periodontitis.
  • Histopathological, microbiological, and immunological features do not sufficiently distinguish between CP and AgP.
  • Molecular profiling offers a potential avenue to explore biological differences.

Purpose of the Study:

  • To investigate biological differences between CP and AgP using molecular profiling.
  • To apply machine-learning algorithms for supervised classification of CP and AgP.
  • To assess the potential for a novel, intrinsic classification of periodontitis.

Main Methods:

  • Whole-genome gene expression profiling of gingival tissue biopsies (n=310) from healthy individuals, CP patients, and AgP patients.
  • Analysis of transcriptional profiles to identify differentially expressed genes.
  • Supervised classification using machine-learning algorithms with internal validation.

Main Results:

  • Limited differences observed in gingival tissue transcriptional profiles between AgP and CP.
  • Genes related to immune responses, apoptosis, and signal transduction were overexpressed in AgP.
  • Genes related to epithelial integrity and metabolism were overexpressed in CP.
  • Machine-learning classifiers showed variable performance in discriminating between CP and AgP (AUC 0.63–0.99).

Conclusions:

  • Established AgP and CP lesions exhibit limited gene expression dissimilarities.
  • The variability in classifier performance underscores the subtle biological differences.
  • Future research may lead to an intrinsic classification of periodontitis based on molecular signatures.