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Updated: Aug 18, 2026

In Situ Detection of Bacteria within Paraffin-embedded Tissues Using a Digoxin-labeled DNA Probe Targeting 16S rRNA
Published on: May 21, 2015
Concurrence between the gene expression pattern of Actinobacillus actinomycetemcomitans in localized aggressive
Joseph Richardson1, Justin Corey Craighead1, Sam Linsen Cao1
1Center for Molecular Microbiology and Department of Oral Biology, 1600 SW Archer Road, University of Florida, Gainesville, FL 32610-0424, USA.
Abstract:
Actinobacillus actinomycetemcomitans is a facultatively intracellular pathogen and the aetiological agent of localized aggressive periodontitis. Screening of the genome of A. actinomycetemcomitans for in vivo-induced antigen determinants previously demonstrated that the proteome of this organism differs in laboratory culture compared with conditions found during active infection. The aim of the present study was to determine whether the bacterial gene expression pattern inferred with in vivo-induced antigen technology (IVIAT) in human infections was consistent with the gene expression pattern occurring upon epithelial cell association. To this end, a real-time PCR method was developed and used to quantify absolute and relative bacterial gene expression of A. actinomycetemcomitans grown extra- and intracellularly in two human epithelial cell lines (HeLa and IHGK). The amount of template used in the assay was normalized using the total count of viable bacteria (c.f.u.) as a reference point and performed in duplicate in at least two independent experiments. Controls for this experiment included 16S rRNA and gapdh. Transcription of all eight ORFs tested increased significantly (P < 0.05) in HeLa and IHGK cells compared with bacteria grown extracellularly. The concurrence of gene expression patterns found in the two models suggests that these epithelial cells are valid in vitro models of infection for the genes tested. IVIAT is an experimental platform that can be used as a validation tool to assess the reliability of animal and other models of infection and is applicable to most pathogens.
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