BioProject

There is increasing evidence to support a role for sigma factor 54 (RpoN) in the regulation of stress resistance factors and protein secretion systems important to bacterial transmission and pathogenesis. More...

There is increasing evidence to support a role for sigma factor 54 (RpoN) in the regulation of stress resistance factors and protein secretion systems important to bacterial transmission and pathogenesis. In enterohemorrhagic E. coli O157:H7, acid resistance and type III secretion are essential determinants of gastric passage and colonization. This study thus described the transcriptome of an rpoN null strain of E. coli O157:H7 (EcJR-8) to determine the influence of RpoN on virulence and stress resistance gene regulation, and further explored its contribution to glutamate-dependent acid resistance (GDAR). Inactivation of rpoN resulted in the growth phase-dependent, differential expression of 104 genes. This included type III secretion structural and regulatory genes encoded on the locus of enterocyte effacement (LEE), as well as GDAR genes gadA, gadBC and gadE. Upregulation of gad transcript levels in EcJR-8 during logarithmic growth correlated with increased GDAR and survival in a model stomach. Acid susceptibility was reconstituted in EcJR-8 complemented in trans with wild-type rpoN. Acid resistance in EcJR-8 was dependent on exogenous glutamate, gadE and rpoS, but was independent of hns. Results also suggest that GDAR may be controlled by RpoN at multiple regulatory levels. This study supports the hypothesis that RpoN is an important regulator of virulence and stress resistance factors in E. coli O157:H7, and is the first to examine the mechanism by which it represses GDAR. Overall design: Hybridizations measured transcriptional differences between an rpoN null and wild-type (WT) strain of E. coli O157:H7 Sakai at logarithmic and transition phase. Image files (TIFF) of hybridized microarray slides were generated using an Axon 4000B scanner (Molecular Devices), and analyzed using GenePix Pro software (Molecular Devices, ver. 6.0). The resulting microarray intensity data was log2-transformed, and normalized using the LOWESS algorithm in MAANOVA ver. 0.98-8 (R ver. 2.2.1). Less...