Influence of the Alternative Sigma Factor RpoN on Global Gene Expression and Carbon Catabolism in Enterococcus faecalis V583

The alternative sigma factor σ54 has been shown to regulate the expression of a wide array of virulence-associated genes, as well as central metabolism, in bacterial pathogens. In Gram-positive organisms, the σ54 is commonly associated with carbon metabolism. In this study, we show that the Enterococcus faecalis alternative sigma factor σ54 (RpoN) and its cognate enhancer binding protein MptR are essential for mannose utilization and are primary contributors to glucose uptake through the Mpt phosphotransferase system. To gain further insight into how RpoN contributes to global transcriptional changes, we performed microarray transcriptional analysis of strain V583 and an isogenic rpoN mutant grown in a chemically defined medium with glucose as the sole carbon source. Transcripts of 340 genes were differentially affected in the rpoN mutant; the predicted functions of these genes mainly related to nutrient acquisition. These differentially expressed genes included those with predicted catabolite-responsive element (cre) sites, consistent with loss of repression by the major carbon catabolite repressor CcpA. To determine if the inability to efficiently metabolize glucose/mannose affected infection outcome, we utilized two distinct infection models. We found that the rpoN mutant is significantly attenuated in both rabbit endocarditis and murine catheter-associated urinary tract infection (CAUTI). Here, we examined a ccpA mutant in the CAUTI model and showed that the absence of carbon catabolite control also significantly attenuates bacterial tissue burden in this model. Our data highlight the contribution of central carbon metabolism to growth of E. faecalis at various sites of infection

Results of Programmed Evolution.

<p>(A) The starting population with equal amounts of all 24 strains was spread on LB agar plates with the indicated antibiotic and a disk treated as indicated. (B) Top row: spots of cells on LB agar with ampicillin for all 24 starting strains (left) and examples of clones after Programmed Evolution (right). Middle row: Agarose gels with PCR products to determine PCN for all 24 strains (left) and examples after Programmed Evolution (right). The 750 bp band for the low copy origin and the 500 bp band for the high copy origin are indicated by arrows. Bottom row: Agarose gels with PCR products to chaperone genotype for all 24 strains (left) and examples after Programmed Evolution (right). (C) The graph shows relative frequency of each of the genotype before (top) and after (bottom) Programmed Evolution. The order of chaperone plasmids along the left to right horizontal axis is pG-Tf2, pTf16, pG-KJE8, pGro7, pKJE7, and no chaperone. The order of genotype combinations along the other horizontal axis from back to front is high strength promoter/RBS + high copy origin; high strength promoter/RBS + low copy origin; low strength promoter/RBS + high copy origin; and low strength promoter/RBS + low copy origin.</p

Starting Population for Programmed Evolution.

<p>(A) An ampicillin resistance plasmid carries variation in the strength of promoters and RBS elements as well as the low and high copy number origins of replication. (B) A chloramphenicol resistance plasmid carries chaperones DNA KJE, Trigger Factor, and Gro ESL chaperones individually and in two combinations (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0118322#sec004" target="_blank">Methods</a> for details).</p

Relative Fitness of Genotypes as a Function of Theophylline Production.

<p>Theophylline production as measured by LC-MS analysis is listed for the three genotypes with the highest fitness and two genotypes with very low fitness.</p

Results of Programmed Evolution.

<p>The number and genotype of colonies analyzed after Programmed Evolution from three replicate plate experiments.</p

Biosensor and Fitness Modules.

<p>(A) The Biosensor Module contains a promoter, a riboswitch that binds to theophylline, and a GFP gene. (B) Cells with the indicated genotypes were incubated with caffeine or theophylline. Fluorescence of cells grown in theophylline or caffeine was divided by absorbance at 590 nm (relative fluorescence) to correct for variation in cell density. (C) Relative fluorescence as a function of time in cells with and without the biosensor grown in 2.5 mM theophylline. (D) The Fitness Module contains a promoter, a riboswitch that binds theophylline, and the tetracycline resistance gene (<i>tetA</i>). (E) Cell growth in media containing tetracycline and either theophylline or caffeine as indicated.</p

Origins of Replication Determine Plasmid Copy Number.

<p>The origins of replication used in the study are listed with their descriptions and part numbers in the Registry of Standard Biological Parts. The means and standard deviations of PCN values were determined by qPCR and yields of minipreps.</p

Programmed Evolution.

<p>The Combinatorics Module facilitates variation of elements controlling orthogonal metabolism. Genetic variation is illustrated by different colors of bacteria. The Fitness Module defines fitness as orthogonal metabolic output and cell growth, and imposes negative selection on bacteria with low metabolic output, shown by elimination of some of the colored bacteria. A Biosensor Module is used to measure the metabolic output of the population or individual cells. Programmed Evolution can be repeated for successive cycles.</p

Optimization of Metabolic Pathways.

<p>(A) Orthogonal metabolic output in a bacterial cell is depicted as a function (<i>f</i>) of the genetic circuit controlling metabolism and additional variables. (B) Two gene expression cassettes are drawn that encode enzymes controlling a metabolic pathway. Promoters, ribosome binding sites, and alleles for the two cassettes are chosen from a library of elements.</p