Richard B. Greaves

Structure of the trp RNA-binding attenuation protein, TRAP, bound to RNA

The trp RNA-binding attenuation protein (TRAP) regulates expression of the tryptophan biosynthetic genes of several bacilli by binding single-stranded RNA. The binding sequence is composed of eleven triplet repeats, predominantly GAG, separated by two or three non-conserved nucleotides. Here we present the crystal structure of a complex of TRAP and a 53-base single-stranded RNA containing eleven GAG triplets, revealing that each triplet is accommodated in a binding pocket formed by β-strands. In the complex, the RNA has an extended structure without any base-pairing and binds to the protein mostly by specific protein–base interactions. Eleven binding pockets on the circular TRAP 11-mer form a belt with a diameter of about 80 Å. This simple but elegant mechanism of arresting the RNA segment by encircling it around a protein disk is applicable to both transcription, when TRAP binds the nascent RNA, and to translation, when TRAP binds the same sequence within a non-coding leader region of the messenger RNA.

Determining Disease Intervention Strategies Using Spatially Resolved Simulations

Predicting efficacy and optimal drug delivery strategies for small molecule and biological therapeutics is challenging due to the complex interactions between diverse cell types in different tissues that determine disease outcome. Here we present a new methodology to simulate inflammatory disease manifestation and test potential intervention strategies in silico using agent-based computational models. Simulations created using this methodology have explicit spatial and temporal representations, and capture the heterogeneous and stochastic cellular behaviours that lead to emergence of pathology or disease resolution. To demonstrate this methodology we have simulated the prototypic murine T cell-mediated autoimmune disease experimental autoimmune encephalomyelitis, a mouse model of multiple sclerosis. In the simulation immune cell dynamics, neuronal damage and tissue specific pathology emerge, closely resembling behaviour found in the murine model. Using the calibrated simulation we have analysed how changes in the timing and efficacy of T cell receptor signalling inhibition leads to either disease exacerbation or resolution. The technology described is a powerful new method to understand cellular behaviours in complex inflammatory disease, permits rational design of drug interventional strategies and has provided new insights into the role of TCR signalling in autoimmune disease progression.

In silico investigation into dendritic cell regulation of CD8Treg mediated killing of Th1 cells in murine experimental autoimmune encephalomyelitis

BackgroundExperimental autoimmune encephalomyelitis has been used extensively as an animal model of T cell mediated autoimmunity. A down-regulatory pathway through which encephalitogenic CD4Th1 cells are killed by CD8 regulatory T cells (Treg) has recently been proposed. With the CD8Treg cells being primed by dendritic cells, regulation of recovery may be occuring around these antigen presenting cells. CD4Treg cells provide critical help within this process, by licensing dendritic cells to prime CD8Treg cells, however the spatial and temporal aspects of this help in the CTL response is currently unclear.ResultsWe have previously developed a simulator of experimental autoimmune encephalomyelitis (ARTIMMUS). We use ARTIMMUS to perform novel in silico experimentation regarding the priming of CD8Treg cells by dendritic cells, and the resulting CD8Treg mediated killing of encephalitogenic CD4Th1 cells. Simulations using dendritic cells that present antigenic peptides in a mutually exclusive manner (either MBP or TCR-derived, but not both) suggest that there is no significant reliance on dendritic cells that can prime both encephalitogenic CD4Th1 and Treg cells. Further, in silico experimentation suggests that dynamics of CD8Treg priming are significantly influenced through their spatial competition with CD4Treg cells and through the timing of Qa-1 expression by dendritic cells.ConclusionThere is no requirement for the encephalitogenic CD4Th1 cells and cytotoxic CD8Treg cells to be primed by the same dendritic cells. We conjecture that no significant portion of CD4Th1 regulation by Qa-1 restricted CD8Treg cells occurs around individual dendritic cells, and as such, that CD8Treg mediated killing of CD4Th1 cells occurring around dendritic cells is not critical for recovery from the murine autoimmune disease. Furthermore, the timing of the CD4Treg licensing of dendritic cells and the spatial competition between CD4Treg and CD8Treg cells around the dendritic cell is critical for the size of the cytotoxic T lymphocyte response, because dendritic cells have a limited lifespan. If treatments can be found to either speed up the licensing process, or increase the spatial competitiveness of CD8Treg cells, the magnitude of the cytotoxic T lymphocyte response can be increased.

In silico investigation of novel biological pathways: The role of CD200 in regulation of T cell priming in experimental autoimmune encephalomyelitis

The use of simulation to investigate biological domains will inevitably lead to the need to extend existing simulations as new areas of these domains become more fully understood. Such simulation extensions can entail the incorporation of additional cell types, molecules or molecular pathways, all of which can exert a profound influence on the simulation behaviour. Where the biological domain is not well characterised, a structured development methodology must be employed to ensure that the extended simulation is well aligned with its predecessor. We develop and discuss such a methodology, relying on iterative simulation development and sensitivity analysis. The utility of this methodology is demonstrated using a case study simulation of experimental autoimmune encephalomyelitis (EAE), a murine T cell-mediated autoimmune disease model of multiple sclerosis, where it is used to investigate the activity of an additional regulatory pathway. We discuss how application of this methodology guards against creating inappropriate simulation representations of the biology when investigating poorly characterised biological mechanisms.

Extending an established simulation: exploration of the possible effects using a case study in experimental autoimmune encephalomyelitis

Investigation of a biological domain through simulation can naturally lead to the desire to extend the simulation as new areas of the domain are explored. Such extension may entail the incorporation of additional cell types, molecules or entire molecular pathways. The addition of these extensions can have a profound influence on simulation behaviour, and where the biological domain is not well characterised, a structured development methodology must be employed to ensure that the extended simulation is well aligned with its predecessor. The paper presents such a methodology, relying on iterated development and sensitivity analysis, by extending an existing simulation of Experimental Autoimmune Encephalomyelitis (EAE), a disease model for Multiple Sclerosis, via inclusion of an additional regulatory pathway. We reflect on the implications of extensions which alter simulation behaviour on pre-extension results.

Automated production of small-molecule ­dictionaries for use in crystallographic refinements

Many macromolecules are now being studied crystallographically in complexes with a range of ligands and other associated molecules. It is necessary to have templates describing the expected geometry of such molecules before refinement and model building can be carried out. This paper describes a method for generating templates beginning from the SMILES description of the molecule, the final format of the molecular template being based on the mmCIF definitions for chemical composition. Additionally, the program SMILE2DICT, which converts the SMILES string to a more extended format, is described. The description details the input required, the output produced and how the program relates to attempts to automate the procedure of model building for crystallographic refinement. Examples of input to and output from the program are given.

A conceptual and computational framework for modelling and understanding the non-equilibrium gene regulatory networks of mouse embryonic stem cells

The capacity of pluripotent embryonic stem cells to differentiate into any cell type in the body makes them invaluable in the field of regenerative medicine. However, because of the complexity of both the core pluripotency network and the process of cell fate computation it is not yet possible to control the fate of stem cells. We present a theoretical model of stem cell fate computation that is based on Halley and Winkler’s Branching Process Theory (BPT) and on Greaves et al.’s agent-based computer simulation derived from that theoretical model. BPT abstracts the complex production and action of a Transcription Factor (TF) into a single critical branching process that may dissipate, maintain, or become supercritical. Here we take the single TF model and extend it to multiple interacting TFs, and build an agent-based simulation of multiple TFs to investigate the dynamics of such coupled systems. We have developed the simulation and the theoretical model together, in an iterative manner, with the aim of obtaining a deeper understanding of stem cell fate computation, in order to influence experimental efforts, which may in turn influence the outcome of cellular differentiation. The model used is an example of self-organization and could be more widely applicable to the modelling of other complex systems. The simulation based on this model, though currently limited in scope in terms of the biology it represents, supports the utility of the Halley and Winkler branching process model in describing the behaviour of stem cell gene regulatory networks. Our simulation demonstrates three key features: (i) the existence of a critical value of the branching process parameter, dependent on the details of the cistrome in question; (ii) the ability of an active cistrome to “ignite” an otherwise fully dissipated cistrome, and drive it to criticality; (iii) how coupling cistromes together can reduce their critical branching parameter values needed to drive them to criticality.

Correction: Determining disease intervention strategies using spatially resolved simulations.

The image for Fig 4C is incorrect. Please see the complete, corrected Fig 4 here. Fig 4 Effector T cell and clinical disease dynamics given anti-CD3 intervention at day 4.