Doctor of Philosophy

dissertationRapidly evolving technologies such as chip arrays and next-generation sequencing are uncovering human genetic variants at an unprecedented pace. Unfortunately, this ever growing collection of gene sequence variation has limited clinical utility without clear association to disease outcomes. As electronic medical records begin to incorporate genetic information, gene variant classification and accurate interpretation of gene test results plays a critical role in customizing patient therapy. To verify the functional impact of a given gene variant, laboratories rely on confirming evidence such as previous literature reports, patient history and disease segregation in a family. By definition variants of uncertain significance (VUS) lack this supporting evidence and in such cases, computational tools are often used to evaluate the predicted functional impact of a gene mutation. This study evaluates leveraging high quality genotype-phenotype disease variant data from 20 genes and 3986 variants, to develop gene-specific predictors utilizing a combination of changes in primary amino acid sequence, amino acid properties as descriptors of mutation severity and Naïve Bayes classification. A Primary Sequence Amino Acid Properties (PSAAP) prediction algorithm was then combined with well established predictors in a weighted Consensus sum in context of gene-specific reference intervals for known phenotypes. PSAAP and Consensus were also used to evaluate known variants of uncertain significance in the RET proto-oncogene as a model gene. The PSAAP algorithm was successfully extended to many genes and diseases. Gene-specific algorithms typically outperform generalized prediction tools. Characteristic mutation properties of a given gene and disease may be lost when diluted into genomewide data sets. A reliable computational phenotype classification framework with quantitative metrics and disease specific reference ranges allows objective evaluation of novel or uncertain gene variants and augments decision making when confirming clinical information is limited

Unfolding plant desiccation tolerance : evolution, structure, and function of LEA proteins

When plants colonized land they developed a wide range of adaptations to cope with life in a drier environment. One key adaptation was desiccation tolerance (DT) which is the ability to survive the removal of almost all cellular water without irreparable damage. DT is recurrent in orthodox seeds and in the vegetative body of species commonly known as ‘resurrection plants’. In this thesis a multilevel approach, combining genomics, transcriptomics, gene family evolution, protein structural and functional analysis, and seed physiology was employed in order to tackle curiosity-driven fundamental questions about the major mechanisms governing DT. Several mechanisms were found to be important for DT, including the coordinated activation of cell protection through Late Embryogenesis Abundant (LEA) proteins, which were shown to be common amongst resurrection plants and orthodox seeds. These findings aid to the comprehension of the complexity of DT in plants, and may provide transferrable knowledge to design more water-stress tolerant crops.</p

Analyses of All Possible Point Mutations within a Protein Reveals Relationships between Function and Experimental Fitness: A Dissertation

The primary amino acid sequence of a protein governs its specific cellular functions. Since the cracking of the genetic code in the late 1950’s, it has been possible to predict the amino acid sequence of a given protein from the DNA sequence of a gene. Nevertheless, the ability to predict a protein’s function from its primary sequence remains a great challenge in biology. In order to address this problem, we combined recent advances in next generation sequencing technologies with systematic mutagenesis strategies to assess the function of thousands of protein variants in a single experiment. Using this strategy, my dissertation describes the effects of most possible single point mutants in the multifunctional Ubiquitin protein in yeast. The effects of these mutants on the essential activation of ubiquitin by the ubiquitin activating protein (E1, Uba1p) as well as their effects on overall yeast growth were measured. Ubiquitin mutants defective for E1 activation were found to correlate with growth defects, although in a non-linear fashion. Further examination of select point mutants indicated that E1 activation deficiencies predict downstream defects in Ubiquitin function, resulting in the observed growth phenotypes. These results indicate that there may be selective pressure for the activity of the E1enzyme to selectively activate ubiquitin protein variants that do not result in functional downstream defects. Additionally, I will describe the use of similar techniques to discover drug resistant mutants of the oncogenic protein BRAFV600E in human melanoma cell lines as an example of the widespread applicability of our strategy for addressing the relationship between protein function and biological fitness

Characterisation of two desiccation-linked dehydrins from Xerophyta humilis

In response to abiotic stresses, organisms throughout the plant kingdom, as well as microorganisms and micro-animals such as nematodes or tardigrades, have been observed to express Late Embryogenesis Abundant (LEA) proteins as protective mechanisms. However, despite two decades of research, little is understood about their physiological functions and this has led to extensive nomenclature, with a large amount of redundancy. The primary reason for this lack of insight into LEA protein functions is their highly hydrophilic and intrinsically disordered nature. Intrinsically disordered proteins (IDPs) cannot be studied using conventional methods of structural analyses such as X-ray crystallography and, therefore, alternative techniques are required. A combination of transgenic and in vitro studies have also shown that LEA proteins are most likely to behave as molecular chaperones by binding water and ions, preventing macromolecular aggregation and protecting enzymatic activity during dehydration. This study characterized two dehydrins that were expressed during dehydration in the desiccation tolerant plant, Xerophyta humilis. From a transcriptome analyses on X. humilis, cDNA for the two dehydrins were obtained. These sequences were first analysed using various in silico tools in order to identify putative dehydrin-specific characteristics. Subsequently, these two dehydrins were cloned and expressed for production of recombinant dehydrin protein. These proteins were then analysed in terms of structural and functional characteristics. Structurally, through the use of circular dichroism in an in vitro system, both dehydrins demonstrated the shift towards being increasingly alpha-helical when placed in environments of decreasing water content. The role of these two dehydrins in stabilizing enzymes during dehydration was subsequently investigated using citrate synthase (CS) and lactate dehydrogenase (LDH). The preservation of enzyme activity was observed in both CS and LDH. This preservation of enzyme activity was further maintained by the presence of trehalose. Anti-aggregation roles were also investigated, however, neither dehydrin demonstrated significant ability to minimize the aggregation of LDH. This study hopes to establish a pipeline for characterizing LEA proteins using structural and functional assays in order to provide alternative means of LEA protein classification

Multiple Alignment of Structures using Center of Proteins

There is a buzz among structural biologists about conducting a major portion of their future work in silico, due to progressively refined computational tools and an amazing quantity of digitized biological data. This masters thesis focusses on the area of computational methods for aligning multiple protein structures. As the problem under consideration is known to be np–complete, several ways for coming up with good approximations have been suggested over the years. A new approach for achieving better, or at least as good results as before, is presented here. We discuss the proposed algorithm and its constituent methods. Finally, we report the widely used root mean square deviation (RMSD) as measures of structural similarity, and the execution time. Some chosen results, from our extensive experimentation, and their significance have been discussed. A web server has also been implemented for trying out a pairwise alignment algorithm. This is hosted on the university website and the link has been provided in the contribution

Identifying driver mutations in cancers

All cancers depend upon mutations in critical genes, which confer a selective advantage to the tumour cell. The key to understanding the contribution of a disease-associated mutation to the development and progression of cancer comes from an understanding of the consequences of that mutation on the function of the affected protein, and the impact on the pathways in which that protein is involved. Using data from over 30 different cancers from whole-exome sequencing cancer genomic projects, I analysed over one million somatic mutations. I identified mutational hotspots within domain families by mapping small mutations to equivalent positions in multiple sequence alignments of protein domains. I found that gain of function mutations from oncogenes and loss of function mutations from tumour suppressors are normally found in different domain families and when observed in the same domain families, hotspot mutations are located at different positions within the multiple sequence alignment of the domain. Next, I investigated the ability of seven prediction algorithms to discriminate between driver missense mutations in oncogenes and tumour suppressors. Using 19 features to describe these mutations, I then developed a random forest classifier, MOKCaRF, to distinguish between gain of function and loss of function missense mutations in cancer. MOKCaRF performs significantly better than existing algorithms. I then evaluated the ability of six existing prediction tools to distinguish between pathogenic and neutral mutations for both inframe insertion and inframe deletion mutations. I developed my own classifiers using 11 features that perform better than the current algorithms. Finally, using the algorithms that I developed, as well as changes in copy number and expression data for each gene, I analysed samples from 50 lung cancer patients to identify the actionable targets and potential new drug targets for each tumour

The global organization and topological properties of Drosophila melanogaster

The fundamental principles governing the natural phenomena of life is one of the critical issues receiving due importance in recent years. Most complex real-world systems are found to have a similar networking model that manages their behavioral pattern. Recent scientific discoveries have furnished evidence that most real world networks follow a scale-free architecture. A number of research efforts are in progress to facilitate the learning of valuable information by recognizing the underlying reality in the vast amount of genomic data that is becoming available. A key feature of scale-free architecture is the vitality of the highly connected nodes (hubs). This project focuses on the multi-cellular organism Drosophila melanogaster, an established model system for human biology. The major objective is to analyze the protein-protein interaction and the metabolic network of the organism to consider the architectural patterns and the consequence of removal of hubs on the topological parameters of the two interaction networks. Analysis shows that both interaction networks pursue a scale-free model establishing the fact that real networks from varied situations conform to the small world pattern. Similarly, the topology of the two networks suffers drastic variations on the removal of the hubs. It is found that the topological parameters of average path length and diameter show a two-fold and three-fold increase on the deletion of hubs for the protein-protein interaction and metabolic interaction network, respectively. The arbitrary exclusion of the nodes does not show any remarkable disparity in the topological parameters of the two networks. This aberrant behavior for the two cases underscores the significance of the most linked nodes to the natural topology of the networks