TRANSCRIPTIONAL PROFILING OF FUR– AND IRON- REGULATED GENE EXPRESSION IN LISTERIA MONOCYTOGENES

Iron is required by almost all bacteria but concentrations of iron above the physiological levels are toxic. In bacteria, intracellular iron is regulated mostly by ferric uptake regulator, Fur or a similar functional protein. Iron limitation results in regulation of number of genes, including those involved in iron uptake process. A subset of the genes under the control of Fur is called Fur regulon. In this study we have identified the Fur- and iron- regulated genes in Listeria monocytogenes by DNA microarray analysis using fur mutant and its isogenic parent. In order to identify genes exclusively regulated in response to iron limitation, fur mutant and its isogenic parent grown in iron-deficient KRM media were compared. Similarly, to identify Fur regulated genes, fur mutant was compared with the parent under iron-deficient and excess-iron conditions. Listeria in low-iron resulted in up-regulation of 180 and down-regulation of 200 genes whereas in the fur mutant, 79 genes were up-regulated and 49 genes down-regulated. Our studies have identified at least 12 genes that are negatively controlled by Fur. In iron-deficient condition, these genes were up-regulated, while expression of fur was down-regulated. To further investigate the fur regulation, the fur promoter-lacZ transcriptional fusion strains were constructed in fur and perR mutant background. In the perR mutant, the regulation of fur, svpA, and feoB was inhibited in the iron-limited conditions. Our results indicate that fur is negatively regulated by Fur and PerR. Furthermore, these results demonstrate that regulation of some of the genes in iron-limited conditions require PerR. Experiments were aimed to determine significant transcriptional changes of (a) EGDe strain in the absence of iron (b) fur mutant compared to EGDe in the absence of iron (c) fur mutant compared to EGDe in the presence of iron and (d) fur mutant in the absence of iron. In each transcriptional profiling experiment, labeled cDNA prepared from total RNA was hybridized to L. monocytogenes array slides. The data from the hybridizations for each of the above experiments were normalized using MIDAS software using LOWESS algorithm to minimize biological and experimental variations. Significantly expressed or repressed genes were identified by estimating the false discovery rate (FDR) using significance analysis of microarrays (SAM) (Δ = 1.01; False discovery rate (FDR) at 5%; q < 0.05).