Mechanochemical analysis of DNA gyrase using rotor bead tracking

DNA gyrase is a molecular machine that uses the energy of ATP hydrolysis to introduce essential negative supercoils into DNA. The directionality of supercoiling is ensured by chiral wrapping of the DNA around a specialized domain of the enzyme before strand passage. Here we observe the activity of gyrase in real time by tracking the rotation of a submicrometre bead attached to the side of a stretched DNA molecule. In the presence of gyrase and ATP, we observe bursts of rotation corresponding to the processive, stepwise introduction of negative supercoils in strict multiples of two. Changes in DNA tension have no detectable effect on supercoiling velocity, but the enzyme becomes markedly less processive as tension is increased over a range of only a few tenths of piconewtons. This behaviour is quantitatively explained by a simple mechanochemical model in which processivity depends on a kinetic competition between dissociation and rapid, tension-sensitive DNA wrapping. In a high-resolution variant of our assay, we directly detect rotational pauses corresponding to two kinetic substeps: an ATP-independent step at the end of the reaction cycle, and an ATP-binding step in the middle of the cycle, subsequent to DNA wrapping.     

A bottom-up approach to gene regulation

The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models. If simple, well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components, we may begin to build an understanding of cellular regulatory processes from the 'bottom up'. Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli. We studied its behaviour in synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and show that the model can be extended and used to accurately predict the in vivo behaviour of the network when it is expanded to include positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division, which we confirm experimentally. This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger, more complex regulatory networks, and that this bottom-up approach can provide insights into gene regulation.