Markov Chain modeling of pyelonephritis-associated pili expression in uropathogenic Escherichia coli

Monitoring F1651 P-like fimbria expression at the single-cell level reveals a highly heterogeneous phenotype

F1651 and the pyelonephritis-associated pili (Pap) are two members of the type P family of adhesive factors. They play a key role in establishing disease caused by extraintestinal pathogenic Escherichia coli (ExPEC) strains in animals and humans. Both F1651 and Pap are under the control of an epigenetic and reversible switch that defines the number of fimbriated (ON) and afimbriated (OFF) cells within a clonal population. Using the Gfp reporter system, we monitored in vitro the level of fluorescence intensity corresponding to the F1651 and Pap fimbrial synthesis. Monitoring individual Escherichia coli cells by flow cytometry and by real-time fluorescence microscopy, we identified cells associated with a low or high level of fluorescence intensity and a large amount of cells with partial levels of fluorescence, mostly present in the F1651 system. This mixed population identified through fluorescence intensity could be attributed to the high switching rate previously observed in F1651-positive bacteria. The fimbrial heterogeneous phenotype for these ExPEC could represent increased fitness in unpredictable environments. Our study illustrates that within the large repertoire of fimbrial variants such as the well-characterized Pap, F1651 is an exquisite example of regulatory expression that arms the bacterium with strategies for surviving in more than one particular environment.

Mathematical modeling: bridging the gap between concept and realization in synthetic biology

Mathematical modeling plays an important and often indispensable role in synthetic biology because it serves as a crucial link between the concept and realization of a biological circuit. We review mathematical modeling concepts and methodologies as relevant to synthetic biology, including assumptions that underlie a model, types of modeling frameworks (deterministic and stochastic), and the importance of parameter estimation and optimization in modeling. Additionally we expound mathematical techniques used to analyze a model such as sensitivity analysis and bifurcation analysis, which enable the identification of the conditions that cause a synthetic circuit to behave in a desired manner. We also discuss the role of modeling in phenotype analysis such as metabolic and transcription network analysis and point out some available modeling standards and software. Following this, we present three case studies—a metabolic oscillator, a synthetic counter, and a bottom-up gene regulatory network—which have incorporated mathematical modeling as a central component of synthetic circuit design.

Establishing and maintaining sequestration of Dam target sites for phase variation of agn43 in Escherichia coli

Phase variation of the outer membrane protein Ag43 encoded by agn43 in Escherichia coli is controlled by an epigenetic mechanism. Sequestration of the regulatory region from Dam-dependent methylation has to be established and maintained throughout a generation to obtain and maintain the OFF phase. This work shows that hemimethylated DNA, which is formed by the passage of the DNA replication fork in an ON-phase cell, can be sequestered from methylation by OxyR binding, which is thus a key event for the switch from ON to OFF. No evidence was found that the protein SeqA, which also binds to the region, is involved in sequestration. To facilitate the dissection of this process further, a novel approach was introduced that does not alter the sequence of the regulatory region or the cellular concentration of Dam or OxyR, which consists of inserting auxiliary OxyR binding sites upstream of the regulatory region. Using this strategy, it was shown that the ON-to-OFF switch frequency can be modulated without changing the OFF-to-ON frequency. The data support a model in which in an ON-phase cell, the subcellular OxyR availability at the replication fork as it passes through the agn43 regulatory region is key for initiating an ON-to-OFF switch. In contrast, this availability is not a determining factor for the switch from OFF to ON. This finding shows that different variables affect these two stochastic events. This provides new insight into the events determining the stochastic nature of epigenetic phase variation.

Stochastic modeling of gene positive autoregulation networks involving signal molecules

Positive autoregulation in gene regulation networks has been shown in the past to exhibit stochastic behavior, including stochastic bistability, in which an initially uniform cell population develops into two distinct subpopulations. However, positive autoregulation is often mediated by signal molecules, which have not been considered in prior stochastic analysis of these networks. Here we propose both a full model of such a network that includes a signal molecule, and a simplified model in which the signal molecules have been eliminated through the use of two simplifications. The simplified model is amenable to direct mathematical analysis that shows that stochastic bistability is possible. We use stochastic Petri networks for simulating both types of models. The simulation results show that 1), the stochastic behavior of the two models is similar; and 2), that the analytical steady-state distribution of the simplified model matches well the transient results at times equal to that of a cell generation. A discussion of the simplifications we used in the context of the results indicates the importance of the signal molecule number as a factor determining the presence of bistability. This is further supported from a deterministic steady-state analysis of the full model that is shown to be a useful indicator of potential stochastic bistability. We use the regulation of SdiA in Escherichia coli as an example, due to the importance of this protein and of the signal molecule, a bacterial autoinducer, that is involved. However, the use of kinetic parameter values representing typical cellular activities make the conclusions applicable to other signal-mediated positive autoregulation networks as well.

Epigenetic gene regulation in the bacterial world

Like many eukaryotes, bacteria make widespread use of postreplicative DNA methylation for the epigenetic control of DNA-protein interactions. Unlike eukaryotes, however, bacteria use DNA adenine methylation (rather than DNA cytosine methylation) as an epigenetic signal. DNA adenine methylation plays roles in the virulence of diverse pathogens of humans and livestock animals, including pathogenic Escherichia coli, Salmonella, Vibrio, Yersinia, Haemophilus, and Brucella. In Alphaproteobacteria, methylation of adenine at GANTC sites by the CcrM methylase regulates the cell cycle and couples gene transcription to DNA replication. In Gammaproteobacteria, adenine methylation at GATC sites by the Dam methylase provides signals for DNA replication, chromosome segregation, mismatch repair, packaging of bacteriophage genomes, transposase activity, and regulation of gene expression. Transcriptional repression by Dam methylation appears to be more common than transcriptional activation. Certain promoters are active only during the hemimethylation interval that follows DNA replication; repression is restored when the newly synthesized DNA strand is methylated. In the E. coli genome, however, methylation of specific GATC sites can be blocked by cognate DNA binding proteins. Blockage of GATC methylation beyond cell division permits transmission of DNA methylation patterns to daughter cells and can give rise to distinct epigenetic states, each propagated by a positive feedback loop. Switching between alternative DNA methylation patterns can split clonal bacterial populations into epigenetic lineages in a manner reminiscent of eukaryotic cell differentiation. Inheritance of self-propagating DNA methylation patterns governs phase variation in the E. coli pap operon, the agn43 gene, and other loci encoding virulence-related cell surface functions.