Structure of the N-terminal oligomerization domain of DnaD reveals a unique tetramerization motif and provides insights into scaffold formation

DnaD is a primosomal protein that remodels supercoiled plasmids. It binds to supercoiled forms and converts them to open forms without nicking. During this remodeling process, all the writhe is converted to twist and the plasmids are held around the periphery of large scaffolds made up of DnaD molecules. This DNA-remodeling function is the sum of a scaffold-forming activity on the N-terminal domain and a DNA-dependent oligomerization activity on the C-terminal domain. We have determined the crystal structure of the scaffold-forming N-terminal domain, which reveals a winged-helix architecture, with additional structural elements extending from both N- and C-termini. Four monomers form dimers that join into a tetramer. The N-terminal extension mediates dimerization and tetramerization, with extensive interactions and distinct interfaces. The wings and helices of the winged-helix domains remain exposed on the surface of the tetramer. Structure-guided mutagenesis and atomic force microscopy imaging indicate that these elements, together with the C-terminal extension, are involved in scaffold formation. Based upon our data, we propose a model for the DnaD-mediated scaffold formation.

Large-scale neural dynamics: simple and complex

We review the use of neural field models for modelling the brain at the large scales necessary for interpreting EEG, fMRI, MEG and optical imaging data. Albeit a framework that is limited to coarse-grained or mean-field activity, neural field models provide a framework for unifying data from different imaging modalities. Starting with a description of neural mass models we build to spatially extended cortical models of layered two-dimensional sheets with long range axonal connections mediating synaptic interactions. Reformulations of the fundamental non-local mathematical model in terms of more familiar local differential (brain wave) equations are described. Techniques for the analysis of such models, including how to determine the onset of spatio-temporal pattern forming instabilities, are reviewed. Extensions of the basic formalism to treat refractoriness, adaptive feedback and inhomogeneous connectivity are described along with open challenges for the development of multi-scale models that can integrate macroscopic models at large spatial scales with models at the microscopic scale.