Experimental 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.
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.
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 controlling the switch from latency to lytic infection, the immediate early (IE) genes lie at the core of herpesvirus pathogenesis. To image the 72kDa human cytomegalovirus (HCMV) major IE protein (IE1-72K), a recombinant virus encoding IE1 fused with EGFP was constructed. Using this construct, the IE1-EGFP fusion was detected at ND10 (PML-bodies) within 2h post infection (p.i.) and the complete disruption of ND10 imaged through to 6h p.i. HCMV genomes and IE2-86K protein could be detected adjacent to the slowly degrading IE1-72K/ND10 foci. IE1-72K associates with metaphase chromatin, recruiting both PML and STAT2. hDaxx, STAT1 and IE2-86K did not re-locate to metaphase chromatin; the fate of hDaxx is particularly important as this protein contributes to an intrinsic barrier to HCMV infection. While IE1-72K participates in a complex with chromatin, PML, STAT2 and Sp100, IE1-72K releases hDaxx from ND10 yet does not appear to remain associated with it.
The rate of protein structures being deposited in the Protein Data Bank surpasses the capacity to experimentally characterise them and therefore computational methods to analyse these structures have become increasingly important. Identifying the region of the protein most likely to be involved in function is useful in order to gain information about its potential role. There are many available approaches to predict functional site, but many are not made available via a publicly-accessible application.
Charges are important for hyperthermophile protein structure and function. However, the number of charges and their predicted contributions to folded state stability are not correlated, implying that more charge does not imply greater stability. The charge properties that distinguish hyperthermophile proteins also differentiate psychrophile proteins from mesophile proteins, but in the opposite direction and to a smaller extent. We conclude that charge number relates to solubility, whereas protein stability is determined by charge location. Most other structural properties are poorly separated over the ambient temperature range, apart from the burial of certain amino acids. Of particular interest are large non-polar sidechains that tend to increased exposure in proteins evolved to function at higher temperatures. Looking at tryptophan in more detail, this increase is often located close to the termini of secondary structure elements, and is discussed in terms of a novel potential role in protein thermostabilisation.
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