Rapid tonicity induced re-localisation of endogenous aquaporin 4 in primary rat astrocytes - a therapeutic target for cytotoxic brain oedema?

It is estimated that 69-75 million people worldwide will suffer a traumatic brain injury (TBI) or stroke each year. Brain oedema caused by TBI or following a stroke, together with other disorders of the brain cost Europe €770 billion in 2014. Aquaporins (AQP) are transmembrane water channels involved in many physiologies and are responsible for the maintenance of water homeostasis. They react rapidly to changes in osmolarity by transporting water through their highly selective central pore to maintain tonicity and aid in cell volume regulation. We have previously shown that recombinant AQP1-GFP trafficking occurs in a proteinkinase C-microtubule dependant manner in HEK-293 cells in response to hypotonicity. This trafficking mechanism is also reliant on the presence of calcium and its messenger-binding protein calmodulin and results in increased cell surface expression of AQP1 in a time-scale of ~30 seconds. There is currently very little research into the trafficking mechanisms of endogenous AQPs in primary cells. AQP4 is the most abundantly expressed AQP within the brain, it is localised to the astrocytic end-feet, in contact with the blood vessels at the blood-brain-barrier. In situations where the exquisitely-tuned osmotic balance is disturbed, high water permeability can become detrimental. AQP4-mediated water influx causes rapid brain swelling, resulting in death or long term brain damage. Previous research has shown that AQP4 knock-out mice were protected from the formation of cytotoxic brain oedema in a stroke model, highlighting AQP4 as a key drug target for this pathology. As there are currently no treatments available to restrict the flow of water through AQP4 as all known inhibitors are either cytotoxic or non-specific, controlling the mechanisms involved in the regulation of AQP4 in the brain could provide a therapeutic solution to such diseases. Using cell surface biontinylation of endogenous AQP4 in primary rat astrocytes followed by neutraavidin based ELISA we have shown that AQP4 cell surface localisation increases by 2.7 fold after 5 minutes hypotonic treatment at around 85 mOsm/kg H2O. We have also shown that this rapid relocalisation of AQP4 is regulated by PKA, calmodulin, extra-cellular calcium and actin. In summary we have shown that rapid translocation of endogenous AQP4 occurs in primary rat astrocytes in response to hypotonic stimuli; this mechanism is PKA, calcium, actin and calmodulin dependant. AQP4 has the potential to provide a treatment for the development of brain oedema.

Toward prediction of class II mouse major histocompatibility complex peptide binding affinity:in silico bioinformatic evaluation using partial least squares, a robust multivariate statistical technique

The accurate identification of T-cell epitopes remains a principal goal of bioinformatics within immunology. As the immunogenicity of peptide epitopes is dependent on their binding to major histocompatibility complex (MHC) molecules, the prediction of binding affinity is a prerequisite to the reliable prediction of epitopes. The iterative self-consistent (ISC) partial-least-squares (PLS)-based additive method is a recently developed bioinformatic approach for predicting class II peptide−MHC binding affinity. The ISC−PLS method overcomes many of the conceptual difficulties inherent in the prediction of class II peptide−MHC affinity, such as the binding of a mixed population of peptide lengths due to the open-ended class II binding site. The method has applications in both the accurate prediction of class II epitopes and the manipulation of affinity for heteroclitic and competitor peptides. The method is applied here to six class II mouse alleles (I-Ab, I-Ad, I-Ak, I-As, I-Ed, and I-Ek) and included peptides up to 25 amino acids in length. A series of regression equations highlighting the quantitative contributions of individual amino acids at each peptide position was established. The initial model for each allele exhibited only moderate predictivity. Once the set of selected peptide subsequences had converged, the final models exhibited a satisfactory predictive power. Convergence was reached between the 4th and 17th iterations, and the leave-one-out cross-validation statistical terms - q2, SEP, and NC - ranged between 0.732 and 0.925, 0.418 and 0.816, and 1 and 6, respectively. The non-cross-validated statistical terms r2 and SEE ranged between 0.98 and 0.995 and 0.089 and 0.180, respectively. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made freely available online (http://www.jenner.ac.uk/MHCPred).

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.