Search

Myzus persicae is one of the most successful insects on the planet. It is the world’s most pesticide-resistant insect, feeds on hundreds of plant species and acts as a vector for over 100 viruses. Upon perception of M. persicae feeding, plants activate pattern-triggered immunity (PTI), a pivotal part of which is believed to be calcium signalling. The aim of this thesis is to uncover the role that calcium signalling might be playing in the interaction between M. persicae and one of its hosts: the model plant Arabidopsis. Using a fluorescent calcium sensor (GCAMP3), in vivo imaging of calcium dynamics was performed on leaves infested with M. persicae. There is a rapid and highly localised calcium burst around the feeding site in the epidermal and mesophyll cells, making it as one of the first plant responses to aphid attack. This calcium burst is triggered after perception of the aphid by the defence co-receptor BRASSINOSTEROID INSENSITIVE-ASSOCIATED KINASE 1 (BAK1), establishing it as part of PTI. Calcium is released from the extracellular space into the cell by GLUTAMATE-LIKE RECEPTORS 3.3 and 3.6 (GLR3.3 and GLR3.6), in combination with the release of intracellular calcium from the vacuole by TWO-PORE CHANNEL 1 (TPC1). Loss of BAK1, GLR3.3/GLR3.6 or TPC1 significantly attenuates the aphid-induced calcium burst and has an effect on the induction of anti-aphid defence responses. Downstream of the burst, CBL-INTERACTING PROTEIN KINASES 3, 9, 23 and 26 are activated by calcium and together mediate plant resistance to aphid attack. Furthermore, the M. persicae effector Mp10 partially suppresses the feeding site calcium burst, suggesting that the aphid is manipulating this pathway as part of its successful colonisation of the plant. Together, the data presented in this thesis provide evidence for the significant involvement of calcium signalling in the plant response to aphid attack.

Complexity science studies the dynamic interactions between components of a system. The sensitivity of these interactions to environmental conditions means that a single complex system expresses varying emergent behaviour; making its understanding difficult, particularly when it comes to predicting its behaviour. One such "complex" problem is protein folding, which addresses the question of how a linear sequence of amino acids carries information for a three-dimensional protein structure. Deciphering of a code, or even a subset of rules, that could accu- rately help predict the 3-dimensional arrangement of amino acids within a protein would be of tremendous benefit to the basic and applied chemical, biological and biomedical sciences. One approach to solving the protein folding problem is to learn to predict smaller structures and gradually increase the size of our targets. This Thesis follows a similar mindset and focusses on the coiled coil, a ubiquitous IX-helical protein- folding motif that is involved in many biological processes. Coiled-coil prediction can be separated into two distinct problems; firstly, the identification of coiled- coil regions within protein sequences; and, secondly, predicting the architecture and topology that a coiled-coil sequence is likely to adopt; i.e., its oligomer state and the orientation of its helices. The work presented in this Thesis is grounded on the application of statistical modelling to investigate both aspects of the coiled coil-prediction problem. First, a comprehensive assessment of the existing coiled-coil domain prediction algorithms is carried out and their limitations discussed. This forms the basis for the development of a coiled-coil domain meta-predictor, which shows improve- ments over the existing individual algorithms. The contributions of the work pre- sented in this Thesis regarding the prediction of coiled-coil topology are two-fold: firstly, SCORER 2.0, an algorithm that improves our ability to discriminate be- tween coiled-coil topologies, is introduced. Despite state-of-the-art performance, SCORER 2.0 and other existing predictors are limited to two-state predictions and cover only rv 33% of the known coiled-coil population. This triggered the devel- opment of LOGIC OIL, which is the major contribution of this Thesis. LOGICOIL is a Bayesian-based methodology that increases our ab initio prediction coverage of the known coiled-coil population from rv 33% to rv 93%, but also extends our ability to differentiate between multiple coiled-coil oligomeric states from amino- acid sequence alone. Finally, the usefulness of the prediction tools developed in this work is illustrated on problems of biological interest, more specifically on the prediction of coiled coils in the tenascin, thrombospondin and matrilin protein assemblies; i.e., proteins of the extracellular matrix. Here, the coiled-coil domain meta-predictor, and LOGICOIL are used to analyze the presence and structure of coiled-coils domains, as well as their contribution to the overall architecture of the protein complexes.

Herein, density functional theory (DFT) was used for optimizing the geometry of proposed intermediates, built using Spartan Student, in the synthesis of the indium infinite-chain secondary building unit. Dimeric indium species with similar backbones were proposed for each system as a representative of the indium infinite-chain secondary building unit. By calculating the enthalpies of formation for intermediates created from InCl3(H2O) and In(NO3)3(H2O), similar and contrasting enthalpic trends between systems and individually proposed mechanisms were found and compared. All systems consist of motifs 1, 2, 3a, and 3b, which attempt to describe similar mechanistic routes from starting material to proposed dimer. The deprotonation of water before and after becoming a µ2 bridge with respect to two indium atoms was compared among systems, and the most enthalpically favorable bridge to form first (hydroxyl or carboxylate) in the synthesis of the indium infinite-chain secondary building unit was examined. After analyzing the results, they support the possibility that there may be multiple enthalpically reasonable methods of self-assembly and repair.

Means of achieving rapid, efficient separations of analytes are explored in detail, with particular emphasis on the use of chromatographic and electrophoretic theory as an aid in system design and optimization. The benefits of miniaturization of chromatographic systems are assessed. First, the utility of semi-micro Ion Chromatography is explored by the manufacture of 2mm ID analytical and suppressor columns and a micro-conductivity cell. The quality of the columns and detector cell are evaluated by the separation of a test mixture and the calculation of peak variance contribution of the detector cell. The use of readily available analytical scale instrumentation for semi-micro IC is demonstrated. Next, a further downsizing of the IC system is described, in which 530μm ID fused silica tubing is utilized for column manufacture. In this case, a suppressor column is not used and UV detection is employed in the analysis of nucleoside monophosphates. Again, column performance characteristics are measured and noted. Application of this system to the separation of a hydrolysed nucleic acid sample demonstrates the feasibility of the technique to the analysis of volume-limited samples in low concentration with notable sensitivity. The benefits of a miniaturized liquid chromatographic system under pressure-driven flow is studied and the improved permeability of micropacked capillary columns is exploited in the manufacture of several 25 to 30cm columns which achieve high efficiencies with relatively low pressure drops. Van Deemter plots illustrate the performance characteristics of the columns. Finally, electroosmotic flow is studied as the motive force for liquid chromatographic separations. This combination of two techniques, packed capillary liquid chromatography and capillary electrophoresis, results in a system which achieves good resolving power through maximization of selectivity and efficiency.