Browsing by Author "Weljie, Aalim"

Now showing 1 - 7 of 7
  • Thumbnail Image
    ItemOpen Access
    Chemical, Physiological and Metabolic Interactions between Pseudomonas, Metals and Environmental Nutrients
    (2017) Booth, Sean C.; Turner, Raymond J.; Weljie, Aalim; Gieg, Lisa; Turner, Raymond J.; Eltis, Lindsay; DeVinney, Rebekah
    Environmental pollution is one of the major problems facing humanity. Bacteria are capable of removing pollutants from the environment through their metabolic activities. This works for organic pollutants, but metals inhibit the degradation process. Pseudomonas pseudoalcaligenes KF707 is a bacterium that is resistant to metals and is able to degrade pollutants such as polychlorinated biphenyls. In this thesis I present how interactions between the bacterium, its environment and metals affect the bacterium’s physiology and metabolism of biphenyl. Chemical interactions with environmental components affect the toxicity of metals towards bacteria. By examining the tolerance of Pseudomonas species towards copper and aluminium in different media compositions I found that metal bioavailability and carbon source quality had a strong influence on the amount of metal they could withstand. Building on these data, I used metabolomics to understand how metals interfere with organic pollutant degradation. By quantifying the small molecules used and produced by the bacterial cell I was able to determine that metal toxicity is exacerbated by the oxidative stress of metabolizing an organic pollutant. P. pseudoalcaligenes KF707 can swim towards biphenyl but it was unknown how. By deleting genes that were expected to be involved in energy-taxis, a process that allows bacteria to swim to metabolizable carbon sources, I found that this was not how KF707 swims towards biphenyl. I did discover that some unexpected genes were involved in energy-taxis and also that the primary gene for this behavior, Aer, is actually a family of receptors with variable phylogenetic distribution in the genus Pseudomonas. These results provide new insight into the interactions between a bacterium and the nutrients and stressors in their environment.
  • Thumbnail Image
    ItemOpen Access
    Fatty acid synthesis in colorectal cancer: characterization of lipid metabolism in serum, tumour, and normal host tissues
    (2015-05-01) Mackay, Emily; Bathe, Oliver; Weljie, Aalim
    Reprogrammed energy metabolism is now listed as one of the central hallmarks of cancer cells. Aberrant fatty acid metabolism contributes to tumourigenesis through provision of substrates for membrane synthesis, signalling molecules, and synthesis of complex lipids. In this thesis, the role of fatty acid metabolism is explored in the context of colorectal cancer. Metabolomics techniques were employed to characterize fatty acid metabolites in serum, and lipogenic gene expression was quantified in tumour and normal tissues to investigate host response to cancer. Fatty acid metabolite abundance was increased in the serum of individuals with colorectal cancer, and a growth factor signalling axis and lipogenic transcription factor upstream of the endogenous fatty acid synthesis pathway were increased in colorectal liver metastases. It was concluded that liver metastases have an effect on growth factor production in the hepatic microenvironment, leading to increased signalling through a pathway that activates the lipogenic transcription factor that regulates fatty acid synthesis.
  • Thumbnail Image
    ItemOpen Access
    Metabolomics approach for characterizing fatty acid synthesis pathway – A target for brain tumor therapy
    (2013-10-02) Dang, Ngoc Ha; Weljie, Aalim; Habibi, Hamid
    The pharmacological inhibition of fatty acid synthesis using TOFA for acetyl-CoA carboxylase (ACC) and C75 for fatty acid synthase (FAS) was successfully exploited to target glioblastoma multiforme (GBM) metabolism. Despite the minimal impact on normal human astrocytes’ viability, C75 and TOFA had significant deleterious effects on GBM expressed p75NTR, the neurotrophin receptor that regulates GBM invasion, growth and apoptosis. In addition to identifying a “two-way” relationship with the fatty acid synthesis pathway, a key role of p75NTR in the upregulation of many important pathways in GBM, including pentose phosphate pathway, mitochondrial anaplerosis, and glutaminolysis opens the windows for the developments of more drugs targeting GBM’s invasion metabolically. C75 treatment increased the carbon flux from glycogenolysis to fatty acid synthesis. Unexpectedly, the connection between ACC inhibition by TOFA and the upregulation of glutaminolysis in GBM provide advantages for further investigation on drugs targeting both fatty acid synthesis and glutaminolysis.
  • Thumbnail Image
    ItemOpen Access
    Multivariate NMR analysis of human disease models
    (2013-02-01) Duggan, Gavin; Vogel, Hans; Weljie, Aalim
    In the late 1990s, the field of metabolic profiling evolved into metabolomics following the general move towards systems biology and other omics techniques. Using sensitive, analytical platforms such as NMR, metabolomics aims to gather an unbiased, broad perspective of the active biochemistry in biofluids. The result was an explosive growth in the data available to study short term physiological effects, followed perforce by the application of multivariate pattern-recognition techniques to aid in its interpretation. Given the sensitive and comprehensive nature of the technique, it quickly became apparent that any number of artifactual or spurious relationships appear in the results. To alleviate those concerns, a variety of improved experimental designs, analytical techniques, and validation paradigms can be applied. Starting with a basic experimental design, the aim of this work is to explore the ability of properly validated metabolomics to provide useful information about the metabolic shifts seen in established animal models of insulin resistance, a human disease with increasing medical significance. Different two-factor experimental designs are used to refine the results of this early study, validate the resulting hypothesis and reinforce its interpretation. Having seen significant differences in ostensibly identical batches of animals in the first three experiments, further analysis of the differences are performed. Techniques for comparing batch models, as a form of multivariate hypothesis validation, are evaluated and the ability of statistical techniques to predict or ameiliorate these “batch effects” is studied. Finally, a rat model of vitamin C deficiency, another condition with ongoing pathological implications in the third world, is studied using the same metabolomic techniques. The identified metabolic shifts are subjected to a complete pathway analysis, the context of which provides a potentially interesting insight into the regulation of an important human oxidative damage control mechanism.
  • Thumbnail Image
    ItemOpen Access
    Performance of variable selection methods using stability-based selection
    (2017-04-04) Lu, Danny; Weljie, Aalim; de Leon, Alexander R; McConnell, Yarrow; Bathe, Oliver F; Kopciuk, Karen
    Abstract Background Variable selection is frequently carried out during the analysis of many types of high-dimensional data, including those in metabolomics. This study compared the predictive performance of four variable selection methods using stability-based selection, a new secondary selection method that is implemented in the R package BioMark. Two of these methods were evaluated using the more well-known false discovery rate (FDR) as well. Results Simulation studies varied factors relevant to biological data studies, with results based on the median values of 200 partial area under the receiver operating characteristic curve. There was no single top performing method across all factor settings, but the student t test based on stability selection or with FDR adjustment and the variable importance in projection (VIP) scores from partial least squares regression models obtained using a stability-based approach tended to perform well in most settings. Similar results were found with a real spiked-in metabolomics dataset. Group sample size, group effect size, number of significant variables and correlation structure were the most important factors whereas the percentage of significant variables was the least important. Conclusions Researchers can improve prediction scores for their study data by choosing VIP scores based on stability variable selection over the other approaches when the number of variables is small to modest and by increasing the number of samples even moderately. When the number of variables is high and there is block correlation amongst the significant variables (i.e., true biomarkers), the FDR-adjusted student t test performed best. The R package BioMark is an easy-to-use open-source program for variable selection that had excellent performance characteristics for the purposes of this study.
  • Thumbnail Image
    ItemOpen Access
    Serum metabolomics: development and validation of a new diagnostic test for pancreatic cancer
    (2012) McConnell, Yarrow Jean; Bathe, Oliver F.; Weljie, Aalim
  • Thumbnail Image
    ItemOpen Access
    Steps involved in designing and creating the spiked-in data set
    (2016) Kopciuk, Karen; McConnell, Yarrow; Bathe, Oliver; Weljie, Aalim