Investigation of the role of protein kinase C (PKC) in cytostasis induced by tumour promoting phorbol esters and related compounds

Protein kinase C (PKC) is considered to be the major receptor for tumour promoting phorbol esters such as 12-0- tetradecanoylphorbol-13-acetate (TPA). These agents evoke a plethora of biological effects on cells in culture. The growth of A549 human lung carcinoma cells maintained in medium fortified with 10% foetal calf serum (FCS) is arrested for 6 days by TPA and other biologically active phorbol esters. In the work described in this thesis, the hypothesis was tested that modulation of PKC activity is closely related to events pivotal for cytostasis to occur. The effect of several phorbol esters, of newly synthesized analogues of diacylglycerols (DAG) and of bryostatins (bryos) on cell growth and ability to modulate activity of PKC has been investigated.Determination of the subcellular distribution of PKC following treatment of cells with TPA and partial enzyme purification by non-denaturing poly-acrylamide gel electrophoresis revealed translocation of enzyme activity from cytosoUc to paniculate fraction. Chronic exposure of cells to TPA resulted in a time and concentration dependent degradation of enzyme activity. Synthetic DAG and DAG analogues, unable to arrest the growth of cells at non-toxic concentrations, were neither able to affect subcellular PKC distribution nor compete effectively for phorbol ester binding sites at physiologically relevant concentrations. Bryos 1,2,4 and 5, natural products, possessing antineoplastic activity in mice, elicited transient arrest of A549 cell growth in vitro. They successfully competed for phorbol ester receptors in A549 cells with exquisite affinity and induced a shift in sub-cellular PKC distribution, though not to the same extent as PTA. Enzyme down-regulation resulted from prolonged exposure of cells to nanomolar concentrations of bryos. In vivo studies demonstrated that neither PDBu nor bryo 1 was able to inhibit A549 xenograft growth in athymic mice. The growth of A549 cell populations cultured under conditions of serum-deprivation was inhibited only transiently by biologically active phorbol esters. Fortification of serum-free medium with EGF or fetuin was able to partially restore sensitivity to maintained growth arrest by PTA. PKC translocation to the paniculate cellular fraction and subsequent enzyme down-regulation, induced by TPA, occurred in a manner similar to that observed in serum-supplemented cells. However, total PKC activity and cytosolic phorbol ester binding potential were greatly reduced in the serum-deprived cell population. Western blot analysis using monospecific monoclonal antibodies revealed the presence of PKC-a in both A549 cell populations, with significantly reduced protein levels in serum- deprived cells. PKC-/9 was not detected in either cell population.

Tear analysis and lens-tear interactions:Part I. Protein fingerprinting with microfluidic technology

The purpose of this work is to establish the application of a fully automated microfluidic chip based protein separation assay in tear analysis. It is rapid, requires small sample volumes and is vastly superior to, and more convenient than, comparable conventional gel electrophoresis assays. The protein sizing chip technology was applied to three specific fields of analysis. Firstly tear samples were collected regularly from subjects establishing the baseline effects of tear stimulation, tear state and patient health. Secondly tear samples were taken from lens wearing eyes and thirdly the use of microfluidic technology was assessed as a means to investigate a novel area of tear analysis, which we have termed the 'tear envelope'. Utilising the Agilent 2100 Bioanalyzer in combination with the Protein 200 Plus LabChip kit, these studies investigated tear proteins in the range of 14-200 kDa. Particular attention was paid to the relative concentrations of lysozyme, tear lipocalin, secretory IgA (sIgA), IgG and lactoferrin, together with the overall tear electropherogram 'fingerprint'. Furthermore, whilst lens-tear interaction studies are generally thought of as an investigation into the effects of tears components on the contact lens material, i.e. deposition studies, this report addresses the reverse phenomenon-the effect of the lens, and particularly the newly inserted lens, on the tear fluid composition and dynamics. The use of microfluidic technology provides a significant advance in tear studies and should prove invaluable in tear diagnostics and contact lens performance analysis.

Accurate estimation of isoelectric point of protein and peptide based on amino acid sequences

Motivation: In any macromolecular polyprotic system - for example protein, DNA or RNA - the isoelectric point - commonly referred to as the pI - can be defined as the point of singularity in a titration curve, corresponding to the solution pH value at which the net overall surface charge - and thus the electrophoretic mobility - of the ampholyte sums to zero. Different modern analytical biochemistry and proteomics methods depend on the isoelectric point as a principal feature for protein and peptide characterization. Protein separation by isoelectric point is a critical part of 2-D gel electrophoresis, a key precursor of proteomics, where discrete spots can be digested in-gel, and proteins subsequently identified by analytical mass spectrometry. Peptide fractionation according to their pI is also widely used in current proteomics sample preparation procedures previous to the LC-MS/MS analysis. Therefore accurate theoretical prediction of pI would expedite such analysis. While such pI calculation is widely used, it remains largely untested, motivating our efforts to benchmark pI prediction methods. Results: Using data from the database PIP-DB and one publically available dataset as our reference gold standard, we have undertaken the benchmarking of pI calculation methods. We find that methods vary in their accuracy and are highly sensitive to the choice of basis set. The machine-learning algorithms, especially the SVM-based algorithm, showed a superior performance when studying peptide mixtures. In general, learning-based pI prediction methods (such as Cofactor, SVM and Branca) require a large training dataset and their resulting performance will strongly depend of the quality of that data. In contrast with Iterative methods, machine-learning algorithms have the advantage of being able to add new features to improve the accuracy of prediction. Contact: yperez@ebi.ac.uk Availability and Implementation: The software and data are freely available at https://github.com/ypriverol/pIR. Supplementary information: Supplementary data are available at Bioinformatics online.