Molecular reconstruction of a genetic code alteration

The genetic code establishes the rules that govern gene translation into proteins. It was established more than 3.5 billion years ago and it is one of the most conserved features of life. Despite this, several alterations to the standard genetic code have been discovered in both prokaryotes and eukaryotes, namely in the fungal CTG clade where a unique seryl transfer RNA (tRNACAG Ser) decodes leucine CUG codons as serine. This tRNACAG Ser appeared 272±25 million years ago through insertion of an adenosine in the middle position of the anticodon of a tRNACGA Ser gene, which changed its anticodon from 5´-CGA-3´ to 5´-CAG-3´. This most dramatic genetic event restructured the proteome of the CTG clade species, but it is not yet clear how and why such deleterious genetic event was selected and became fixed in those fungal genomes. In this study we have attempted to shed new light on the evolution of this fungal genetic code alteration by reconstructing its evolutionary pathway in vivo in the yeast Saccharomyces cerevisiae. For this, we have expressed wild type and mutant versions of the C. albicans tRNACGA Ser gene into S. cerevisiae and evaluated the impact of the mutant tRNACGA Ser on fitness, tRNA stability, translation efficiency and aminoacylation kinetics. Our data demonstrate that these mutants are expressed and misincorporate Ser at CUGs, but their expression is repressed through an unknown molecular mechanism. We further demonstrate, using in vivo forced evolution methodologies, that the tRNACAG Ser can be easily inactivated through natural mutations that prevent its recognition by the seryl-tRNA synthetase. The overall data show that repression of expression of the mistranslating tRNACAG Ser played a critical role on the evolution of CUG reassignment from Leu to Ser. In order to better understand the evolution of natural genetic code alterations, we have also engineered partial reassignment of various codons in yeast. The data confirmed that genetic code ambiguity affects fitness, induces protein aggregation, interferes with the cell cycle and results in nuclear and morphologic alterations, genome instability and gene expression deregulation. Interestingly, it also generates phenotypic variability and phenotypes that confer growth advantages in certain environmental conditions. This study provides strong evidence for direct and critical roles of the environment on the evolution of genetic code alterations.

Development of high-resolution gamma cameras based on silicon photosensors

The development of a compact gamma camera with high spatial resolution is of great interest in Nuclear Medicine as a means to increase the sensitivity of scintigraphy exams and thus allow the early detection of small tumours. Following the introduction of the wavelength-shifting fibre (WSF) gamma camera by Soares et al. and evolution of photodiodes into highly sensitive silicon photomultipliers (SiPMs), this thesis explores the development of a WSF gamma camera using SiPMs to obtain the position information of scintillation events in a continuous CsI(Na) crystal. The design is highly flexible, allowing the coverage of different areas and the development of compact cameras, with very small dead areas at the edges. After initial studies which confirmed the feasibility of applying SiPMs, a prototype with 5 5 cm2 was assembled and tested at room temperature, in an active field-of-view of 10 10 mm2. Calibration and characterisation of intrinsic properties of this prototype were done using 57Co, while extrinsic measurements were performed using a high-resolution parallel-hole collimator and 99mTc. In addition, a small mouse injected with a radiopharmaceutical was imaged with the developed prototype. Results confirm the great potential of SiPMs when applied in a WSF gamma camera, achieving spatial resolution performance superior to the traditional Anger camera. Furthermore, performance can be improved by an optimisation of experimental conditions, in order to minimise and control the undesirable effects of thermal noise and non-uniformity of response of multiple SiPMs. The development and partial characterisation of a larger SiPM WSF gamma camera with 10 10 cm2 for clinical application are also presented.

Optimisation of viscoelastic treatments using genetic algorithms

Viscoelastic treatments are one of the most efficient treatments, as far as passive damping is concerned, particularly in the case of thin and light structures. In this type of treatment, part of the strain energy generated in the viscoelastic material is dissipated to the surroundings, in the form of heat. A layer of viscoelastic material is applied to a structure in an unconstrained or constrained configuration, the latter proving to be the most efficient arrangement. This is due to the fact that the relative movement of both the host and constraining layers cause the viscoelastic material to be subjected to a relatively high strain energy. There are studies, however, that claim that the partial application of the viscoelastic material is just as efficient, in terms of economic costs or any other form of treatment application costs. The application of patches of material in specific and selected areas of the structure, thus minimising the extension of damping material, results in an equally efficient treatment. Since the damping mechanism of a viscoelastic material is based on the dissipation of part of the strain energy, the efficiency of the partial treatment can be correlated to the modal strain energy of the structure. Even though the results obtained with this approach in various studies are considered very satisfactory, an optimisation procedure is deemed necessary. In order to obtain optimum solutions, however, time consuming numerical simulations are required. The optimisation process to use the minimum amount of viscoelastic material is based on an evolutionary geometry re-design and calculation of the modal damping, making this procedure computationally costly. To avert this disadvantage, this study uses adaptive layerwise finite elements and applies Genetic Algorithms in the optimisation process.