Open AccessEvaluation of housekeeping genes in Listeria monocytogenes as potential internal control references for normalizing mRNA expression levels in stress adaptation models using real‐time PCR
Author(s) -
Tasara Taurai,
Stephan Roger
Publication year - 2007
Publication title -
fems microbiology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.899
H-Index - 151
eISSN - 1574-6968
pISSN - 0378-1097
DOI - 10.1111/j.1574-6968.2007.00633.x
Subject(s) - listeria monocytogenes , housekeeping gene , biology , reference genes , gene expression , gene , real time polymerase chain reaction , rpob , 16s ribosomal rna , ribosomal rna , genetics , microbiology and biotechnology , bacteria
Abstract Listeria monocytogenes is an important food‐borne pathogen that can tolerate a wide range of stress conditions. However, its stress adaptation processes are still poorly understood. Real‐time‐based quantitative RT‐PCR (qRT‐PCR) provides a tool to probe gene expression changes underlying stress adaptation. But, a limitation to study mRNA levels by real‐time qRT‐PCR is that validated reference genes are required for normalization. Such genes are currently lacking for experimental models that may be applied to evaluate stress‐related gene expression changes in L. monocytogenes . Therefore, five housekeeping genes (HKG) were studied as potential reference genes. Their expression stability was evaluated across 16 L. monocytogenes strains. Three experimental models designed to assess gene expression changes induced by cold, acid and high NaCl concentration stress adaptation were applied. The 16S rRNA gene was consistently the most stably expressed HKG across the different L. monocytogenes strains under all the experimental conditions. While the expressions of β‐glucosidase ( bglA ), Glyceraldehyde‐3P‐dehydrogenase ( gap ), RNA polymerase beta subunit ( rpoB ) and Ribosomal protein L4 ( rplD ) was stable amongst the different L. monocytogenes strains, they were prone to significant variations under the different stress adaptation models.