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Journal of Clinical Microbiology, August 2005, p. 3971-3978, Vol. 43, No. 8
0095-1137/05/$08.00+0     doi:10.1128/JCM.43.8.3971-3978.2005
Copyright © 2005, American Society for Microbiology. All Rights Reserved.

Statistical Analyses of Complex Denaturing Gradient Gel Electrophoresis Profiles

Division of Microbial Diseases,¹ Biostatistics Unit, Eastman Dental Institute, UCL, 256 Gray's Inn Road, London WC1X 8LD, United Kingdom²

Received 1 October 2004/ Returned for modification 29 January 2005/ Accepted 16 May 2005

Studies using molecular techniques have demonstrated that a culture-based approach can severely underestimate the bacterial diversity in most environments. One of the molecular techniques that has been applied in microbial ecology is denaturing gradient gel electrophoresis (DGGE). The purpose of this study was to investigate differences in the microbiota of plaque, using a number of analysis techniques, from children without gingivitis (n = 30) and from those with gingivitis (n = 30). Extracted DNA from gingival margin plaque was subjected to PCR targeting the 16S rRNA gene using universal primers. DGGE profiles were analyzed in three ways. (i) Bacterial diversity was compared between cohorts by using the Shannon-Wiener index (also known as the Shannon-Weaver index). (ii) A hierarchical cluster analysis of the banding patterns was calculated and expressed as a dendrogram. (iii) Individual DGGE bands and their intensities for both cohorts were compared using a logistic regression analysis. The Shannon-Wiener indices demonstrated a greater bacterial diversity associated with no-gingivitis plaque (P = 0.009). Dendrograms demonstrated that seven clades associated with gingivitis and five clades associated with no gingivitis. The logistic regression demonstrated that one band was significantly associated with no gingivitis (P = 0.001), while two bands were significantly associated with gingivitis (P = 0.005 and P = 0.042). In conclusion, this study demonstrates that the development of gingivitis might be accompanied by a decrease in bacterial diversity. Furthermore, we have demonstrated that logistic regression is a good statistical method for analyzing and characterizing DGGE profiles.

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