Understanding how genetic variation propagate to differences in phenotypes in individuals is an ongoing challenge in genetics. Genome-wide association studies have allowed for the identification of many trait-associated genomic loci. However, they are limited in their inability to explain the altered cellular mechanism. Genetic variation can drive disease by altering a range of mechanisms, including signalling networks, TF binding, and protein folding. Understanding the impact of variants on such processes has key implications in therapeutics, drug development, and more. This thesis aims to utilise computational predictors to shed light on how cellular mechanisms are altered in the context of genetic variation and better understand how they drive both molecular and organism-level phenotypes. Many binding events in the cell are mediated by short stretches of sequence motifs. The ability to discover these underlying rules of binding could greatly aid our understanding of variant impact. Kinase–substrate phosphorylation is one of the most prominent post-translational modifications (PTMs) which is mediated by such motifs. We first describe a computational method which utilises interaction and phosphorylation data to predict sequence preferences of kinases. Our method was applied to 57% of human kinases capturing known well-characterised and novel kinase specificities. We experimentally validate four understudied kinases to show that predicted models closely resemble true specificities. We further demonstrate that this method can be applied to different organisms and can be used for other phospho-recognition domains. The described approach allows for an extended repertoire of sequence specificities to be generated, particularly in organisms for which little data is available. TF-DNA binding is another mechanism driven by sequence motifs, which is key for the tight regulation of gene expression and can be greatly altered by genetic variation. We have comprehensively benchmarked current methods used to predict non-coding variant effects on TF-DNA binding by employing over 20,000 compiled allele-specific ChIP-seq variants across 94 TFs. We show that machine learning-based approaches significantly outperform more rudimentary methods such as the position weight matrix. We further note that models for many TFs with distinct binding specificities were unable to accurately assess the impact of variants. For these TFs, we explore alternative mechanisms underlying TF-binding, such as methylation, co-operative binding, and DNA shape that drive poor performance. Our results demonstrate the complexity of predicting non-coding variant effects and the importance of incorporating alternative mechanisms into models. Finally, we describe a comprehensive effort to compile and benchmark state-of-the-art sequence and structure-based predictors of mutational consequences and predict the effect of coding and non-coding variants in the reference genomes of human, yeast, and E. coli. Predicted mechanisms include the impact on protein stability, interaction interfaces, and PTMs. These variant effects are provided through mutfunc, a fast and intuitive web tool by which users can interactively explore pre-computed mechanistic variant impact predictions. We validate computed predictions by analysing known pathogenic disease variants and provide mechanistic hypotheses for causal variants of unknown function. We further use our predictions to devise gene-level functionality scores in human and yeast individuals, which we then used to perform gene-phenotype associations and uncover novel gene-phenotype associations.

Antibacterial agents specific for the 50S ribosomal subunit not only inhibit translation but also prevent assembly of that subunit. I examined the 30S ribosomal subunit in growing Escherichia coli cells to see if antibiotics specific for that subunit also had a second inhibitory effect. I used the aminoglycoside antibiotics paromomycin and neomycin, which bind specifically to the 30S ribosomal subunit. Both antibiotics inhibited the growth rate, viable cell number, and protein synthesis. I used a 3H-uridine pulse and chase assay to examine the kinetics of ribosome subunit assembly in the presence and absence of each antibiotic. Analysis revealed a concentration dependent inhibition of 30S subunit formation in the presence of each antibiotic. Sucrose gradient profiles of cell lysates showed the accumulation of an intermediate 21S translational particle. Taken together this data gives the first demonstration that 30S ribosomal subunit inhibitors can also prevent assembly of the small subunit.

Reusing stormwater is a sustainable approach that a lot of cities around the world, including cities in Canada, are developing to improve local and regional water resources. For this purpose, water is typically withdrawn from stormwater ponds (large urban infrastructure that retain stormwater) and used for applications that require less than pristine water quality. However, the large size of these ponds along with the heterogeneity in water quality internally, make the withdrawal location from these ponds for reusable stormwater critically important. Also due to the large sizes of these ponds, collecting data throughout the pond to determine the optimal location for withdrawal is not practical. Modelling however, can provide a more practical means of studying contaminant distribution within the pond over time in order to identify the withdrawal location, among other valuable information. In this dissertation, a modelling approach was developed that simulates fate and transport of bacteria in stormwater ponds after rainstorm events. The model was run to simulate bacteria in the Inverness stormwater pond, which is a large T-shaped pond located in southeast of the City of Calgary, Alberta, Canada. The model has two components: a hydrological component and a Computational Fluid Dynamics (CFD) component. The hydrological component calculates the stormwater runoff of the subbasins of the catchment draining into the pond. The results were compared with collected data and good agreement was observed. Then, the results were fed to the CFD component as input in order to simulate the distribution of contamination brought in by the local hydrology. The CFD component simulates the hydrodynamics of the pond 3-dimensionally. The model was run based on collected data from the pond and multiple versions of the model were developed with regard to free-surface and particulate-attached bacteria transport. In order to address a common issue with hydro-environmental models – being difficult to validate - the model was validated in two ways. First, an instrument was designed and built to measure fluid flow velocity magnitude and direction in the pond. Once calibrated, it was deployed to the pond and the flow field was measured at multiple locations for validation purposes. Second, a non-dimensional number was introduced allowing a comparison between the bacteria concentration data from collected data and that of modelling result in multiple locations of the pond. In both of the validations, good agreement with collected data was observed. A volume of Fluid model and sediment transport model were integrated into the model, which allowed consideration of free-surface effects and for modeling wider range of bacteria, respectively. The model was used to identify the optimal location for water withdrawal for reuse. The middle of the pond, where the three wings join and near the surface, was located as the optimal location due to the lowest bacteria concentration. In an attempt to improve the water quality in the optimal location, strategic tree planting on the north bank of the West wing was studied. It was shown that the trees can reduce the transport of bacteria from the most contaminated location to the withdrawal location. The model was also used to study the impact of some of the important assumptions and environmental factors, such as rain and wind, on bacteria distribution. Wind was found to play a crucial role in the bacteria distribution in the pond. / Graduate

MSc (Microbiology) / Department of Microbiology / See the attached abstract below