Since the dawn of the genomics era, the genetics of numerous human disorders has been understood which has led to improvements in targeted therapeutics. However, the focus of most research has been primarily on protein coding genes, which account for only 2% of the entire genome, leaving much of the remaining genome relatively unstudied. In particular, repetitive sequences, called microsatellites (MST), which are tandem repeats of 1 to 6 bases, are known to be mutational hotspots and have been linked to diseases, such as Huntington disease and Fragile X syndrome. This work represents a significant effort towards closing this knowledge gap. Specifically, we developed a next generation sequencing based enrichment method along with the supporting computational pipeline for detecting novel MST sequences in the human genome. Using this global MST enrichment protocol, we have identified 790 novel sequences. Analysis of these novel sequences has identified previously unknown functional elements, demonstrating its potential for aiding in the completion of the euchromatic DNA. We also developed a disease risk diagnostic using a novel target specific enrichment method that produces high resolution MST sequencing data that has the potential to validate, for the first time, the link between MST genotype variation and cancer. Combined with publicly available exome datasets of non-small cell lung cancer and 1000 genomes project, the target specific MST enrichment method uncovered a signature set of 21 MST loci that can differentiate between lung cancer and non-cancer control samples with a sensitivity ratio of 0.93. Finally, to understand the molecular causes of MST instability, we analyzed genomic variants and gene expression data for an autosomal recessive disorder, Fanconi anemia (FA). This first of its kind study quantified the heterogeneity of FA cells and demonstrated the possibility of utilizing the DNA crosslink repair dysfunctional FA cells as a suitable system to further study the causes of MST instability. / Ph. D.

No / Aim: A diagnostic test that could detect Treponema pallidum antigens in urine would facilitate the prompt diagnosis of syphilis. Materials & methods: Urine from 54 individuals with various clinical stages of syphilis and 6 controls were pooled according to disease stage and interrogated with complementary mass spectrometry techniques to uncover potential syphilis biomarkers. Results & conclusion: In total, 26 unique peptides were uncovered corresponding to four unique T. pallidum proteins that have low genetic sequence similarity to other prokaryotes and human proteins. This is the first account of direct T. pallidum protein detection in human clinical samples using mass spectrometry. The implications of these findings for future diagnostic test development is discussed. Data are available via ProteomeXchange with identifier PXD009707.

<p>Obesity is a globally increasing disease particularly in developing countries and among children. It is mainly caused by intake of diets high in fat and the lack of physical activity. Obesity is a risk factor for diseases such as type II diabetes, high blood pressure, high cholesterol and certain cancers. Prediabetes is a condition where blood glucose levels are above normal but have not&nbsp / reached those of diabetes. It is difficult to diagnose, as there are no signs or symptoms. Some type II diabetes patients bear no symptoms at all and the disease is discovered late. Proteomics is a field that can provide opportunities for early diagnosis of diseases through biomarker discovery. The early diagnosis of diabetes can assist in the prevention and treatment of diabetes. Therefore there is a need for the early diagnosis of diabetes. Twenty Wistar rats were used. The rats were initially fed a CHOW diet, which is the standard balanced diet for rats, for 4 weeks. The rats were then divided into 2 groups of 10 where 1 group was fed CHOW and another was fed a high fat (HF) diet in order to induce obesity. The two groups were fed their respective diets for 18 weeks. Rats were weighed. Rats were placed in metabolic chambers and 24 hour urine samples were collected. Ketone levels were measured by Ketostix. Urine proteins were precipitated by acetone, quantified and separated on both the 1D SDS-PAGE and the 2D SDS-PAGE. Protein expression changes between CHOW and HF fed rats were determined and identified using MALDI-TOF mass spectrometry. Protein spots intensities increased and decreased between the CHOW and HF fed rats. Transducin (beta)-like 3 was identified as the only differentially expressed protein, which might serve as a potential biomarker for prediabetes.</p>

GWAS mainly aim to identify variations associated with certain phenotypes or diseases. Recently the combined p-value approach is described as the next step after GWAS to map the significant SNPs to genes and pathways to evaluate SNP-gene-disease associations. Major bottleneck of standard GWAS approaches is the prioritization of statistically significant results. The connection between statistical analysis and biological relevance should be established to understand the underlying molecular mechanisms of diseases. There are few tools offered for SNP prioritization but these are mainly based on user-defined subjective parameters, which are hard to standardize. Our group has recently developed a novel AHP based SNP prioritization algorithm. Beside statistical association AHP based SNP prioritization algorithm scores SNPs according to their biological relevance in terms of genomic location, functional consequence, evolutionary conservation, and gene-disease association. This allows researchers to evaluate the significantly associated SNPs quickly and objectively. Here, we have investigated the performance of the AHP based prioritization as the next step in the utilization of the algorithm in comparison to the other available tools for SNP prioritization. The user-defined parameters for AHP based prioritization have been investigated and our suggestion on how to use these parameters are presented. Additionally, the GWAS results from the analysis of two different sets of Alzheimer Disease Genotyping data with the newly proposed AHP based prioritization and the integrated software, METU-SNP, it was implemented, is reported and our new findings on the association of SNPs and genes with AD based on this analysis is discussed.

<p>Obesity is a globally increasing disease particularly in developing countries and among children. It is mainly caused by intake of diets high in fat and the lack of physical activity. Obesity is a risk factor for diseases such as type II diabetes, high blood pressure, high cholesterol and certain cancers. Prediabetes is a condition where blood glucose levels are above normal but have not&nbsp / reached those of diabetes. It is difficult to diagnose, as there are no signs or symptoms. Some type II diabetes patients bear no symptoms at all and the disease is discovered late. Proteomics is a field that can provide opportunities for early diagnosis of diseases through biomarker discovery. The early diagnosis of diabetes can assist in the prevention and treatment of diabetes. Therefore there is a need for the early diagnosis of diabetes. Twenty Wistar rats were used. The rats were initially fed a CHOW diet, which is the standard balanced diet for rats, for 4 weeks. The rats were then divided into 2 groups of 10 where 1 group was fed CHOW and another was fed a high fat (HF) diet in order to induce obesity. The two groups were fed their respective diets for 18 weeks. Rats were weighed. Rats were placed in metabolic chambers and 24 hour urine samples were collected. Ketone levels were measured by Ketostix. Urine proteins were precipitated by acetone, quantified and separated on both the 1D SDS-PAGE and the 2D SDS-PAGE. Protein expression changes between CHOW and HF fed rats were determined and identified using MALDI-TOF mass spectrometry. Protein spots intensities increased and decreased between the CHOW and HF fed rats. Transducin (beta)-like 3 was identified as the only differentially expressed protein, which might serve as a potential biomarker for prediabetes.</p>

Magister Scientiae - MSc / Obesity is a globally increasing disease particularly in developing countries and among children. It is mainly caused by intake of diets high in fat and the lack of physical activity. Obesity is a risk factor for diseases such as type II diabetes, high blood pressure, high cholesterol and certain cancers. Prediabetes is a condition where blood glucose levels are above normal but have not reached those of diabetes. It is difficult to diagnose, as there are no signs or symptoms. Some type II diabetes patients bear no symptoms at all and the disease is discovered late. Proteomics is a field that can provide opportunities for early diagnosis of diseases through biomarker discovery. The early diagnosis of diabetes can assist in the prevention and treatment of diabetes. Therefore there is a need for the early diagnosis of diabetes. Twenty Wistar rats were used. The rats were initially fed a CHOW diet, which is the standard balanced diet for rats, for 4 weeks. The rats were then divided into 2 groups of 10 where 1 group was fed CHOW and another was fed a high fat (HF) diet in order to induce obesity. The two groups were fed their respective diets for 18 weeks. Rats were weighed. Rats were placed in metabolic chambers and 24 hour urine samples were collected. Ketone levels were measured by Ketostix. Urine proteins were precipitated by acetone, quantified and separated on both the 1D SDS-PAGE and the 2D SDS-PAGE. Protein expression changes between CHOW and HF fed rats were determined and identified using MALDI-TOF mass spectrometry. Protein spots intensities increased and decreased between the CHOW and HF fed rats. Transducin (beta)-like 3 was identified as the only differentially expressed protein, which might serve as a potential biomarker for prediabetes. / South Africa

Affinity-based proteomics offers opportunities for the discovery and validation of disease-associated proteins in human body fluids. This thesis describes the use of antibody-based immunoassays for multiplexed analysis of proteins in human plasma, serum and cerebrospinal fluid (CSF). This high-throughput method was applied with the objective to identify proteins associated to clinical variables. The main work in this thesis was conducted within the diseases of multiple sclerosis and malignant melanoma, as well as mammographic density, a risk factor for breast cancer. The suspension bead array (SBA) technology has been the main method for the work presented in this thesis (Paper I-IV). SBA assays and other affinity proteomic technologies were introduced for protein profiling of sample material obtained from clinical collaborators and biobanks. Perspectives on the validation of antibody selectivity by means of e.g. immuno-capture mass spectrometry are also provided. Paper I describes the development and application of a protocol for multiplexed pro- tein profiling of CSF. The analysis of 340 CSF samples from patients with multiple sclerosis and other neurological disease revealed proteins with potential association to disease progression (GAP43) and inflammation (SERPINA3). Paper II continued on this work with an extended investigation of more than 1,000 clinical samples and included both plasma and CSF collected from the same patients. Comparison of disease subtypes and controls revealed five plasma proteins of potential diagnostic relevance, such as IRF8 and GAP43. The previously reported associations for GAP43 and SERPINA3 in CSF was confirmed. Subsequent immunohistochemical analysis of post-mortem brain tissue revealed differential protein expression in disease affected areas. In Paper III, 150 serum samples from patients with cutaneous malignant melanoma were analyzed. Protein profiles from antibody bead arrays suggested three proteins (RGN, MTHFD1L, STX7) of differential abundance between patients with no disease recurrence and low tumor thickness (T-stage 1 and 2) compared to patients with high tumor thickness (T-stage 3 and 4) and disease recurrence. We observed MTHFD1L expression in tissue of a majority of patients, while expression of STX7 in melanoma tissue had been reported previously. Paper IV describes the analysis of protein in plasma in relation to mammographic breast density (MD), one of the strongest risk factors for the development of breast cancers. More than 1,300 women without prior history of breast cancer were screened. Linear associations to MD in two independent sample sets were found for 11 proteins, which are expressed in the breast and involved in tissue homeostasis, DNA repair, cancer development and/or progression in MD. In conclusion, this thesis describes the use of multiplexed antibody bead arrays for protein profiling of serum, plasma and CSF, and it shortlists disease associated proteins for further validation studies. / <p>QC 20170302</p>

Le cancer du sein est le deuxième cancer le plus répandu dans le monde et la principale cause de décès due à un cancer chez les femmes. L'amélioration du pronostic du cancer a été l'une des principales préoccupations afin de permettre une meilleure gestion et un meilleur traitement clinique des patients. Avec l'avancement rapide des technologies de profilage génomique durant ces dernières décennies, la disponibilité aisée d'une grande quantité de données génomiques pour la recherche médicale a motivé la tendance actuelle qui consiste à utiliser des outils informatiques tels que l'apprentissage statistique dans le domaine de la science des données afin de découvrir les biomarqueurs moléculaires en lien avec l'amélioration du pronostic. Cette thèse est conçue suivant deux directions d'approches destinées à répondre à deux défis majeurs dans l'analyse de données génomiques pour le pronostic du cancer du sein d'un point de vue méthodologique de l'apprentissage statistique : les approches basées sur le classement pour améliorer le pronostic moléculaire et les approches guidées par un réseau donné pour améliorer la découverte de biomarqueurs. D'autre part, les méthodologies développées et étudiées dans cette thèse, qui concernent respectivement l'apprentissage à partir de données de classements et l'apprentissage sur un graphe, apportent une contribution significative à plusieurs branches de l'apprentissage statistique, concernant au moins les applications à la biologie du cancer et la théorie du choix social. / Breast cancer is the second most common cancer worldwide and the leading cause of women's death from cancer. Improving cancer prognosis has been one of the problems of primary interest towards better clinical management and treatment decision making for cancer patients. With the rapid advancement of genomic profiling technologies in the past decades, easy availability of a substantial amount of genomic data for medical research has been motivating the currently popular trend of using computational tools, especially machine learning in the era of data science, to discover molecular biomarkers regarding prognosis improvement. This thesis is conceived following two lines of approaches intended to address two major challenges arising in genomic data analysis for breast cancer prognosis from a methodological standpoint of machine learning: rank-based approaches for improved molecular prognosis and network-guided approaches for enhanced biomarker discovery. Furthermore, the methodologies developed and investigated in this thesis, pertaining respectively to learning with rank data and learning on graphs, have a significant contribution to several branches of machine learning, concerning applications across but not limited to cancer biology and social choice theory.

Biological markers (i.e., biomarkers) are essential in clinical and epidemiological studies as they may provide mechanistic insights into the developmental origins of disease, as well as improve diagnostic testing and risk assessment for disease prevention. However, major challenges remain due to the lack of rapid yet selective analytical methods for high throughput screening that are also amenable to volume-restricted specimens. This thesis includes two major research themes that take advantage of capillary electrophoresis (CE) separations, including (1) the targeted analysis of urinary iodide and thiocyanate for assessment of nutritional adequacy and tobacco smoke exposures in the population, and (2) the discovery of new biomarkers in sweat specimens that may improve universal newborn screening programs for cystic fibrosis (CF) infants beyond impaired chloride transport. Chapter II examines the prevalence and risk factors associated with iodine deficiency in 24 h urine samples collected from 800 participants across four clinical sites in Canada as part of the Prospective Urban and Rural Epidemiological (PURE) study when using CE with UV detection in conjunction with sample self-stacking. Importantly, regional variations in iodine status were revealed with participants from Quebec City and Vancouver at greater risk for iodine deficiency than Hamilton and Ottawa. Overall, iodine supplement use, thyroxine prescription, urinary sodium excretion, and self-reported dairy intake were found to be protective factors against iodine deficiency. Chapter III applied a validated CE assay to measure urinary thiocyanate as a biomarker of tobacco smoke and dietary exposures in an international cohort of 1000 participants from the PURE study spanning 14 countries with varied income status, smoking habits, and diet quality. Current smokers residing in high-income countries had the highest extent of cyanide exposure indicative of greater harms from tobacco smoke compared to middle- and low-income countries after adjusting for smoking intensity and other covariates. Chapter IV introduces a rapid CE method with indirect UV detection to simultaneously measure sweat chloride and bicarbonate from presumptive CF infants’ residual sweat samples. Although bicarbonate did not provide clinical value in neonatal CF diagnosis, sweat chloride testing by CE may reduce test failure rates due to insufficient volumes from infants in a clinical setting. Lastly, Chapter V applied an untargeted strategy to characterize the sweat metabolome from presumptive CF infants when using multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS). A panel of sweat metabolites were found to discriminate CF from non-CF (i.e., unaffected carriers) infants, including aspartic acid, glutamine, oxoproline, and pilocarpic acid, which also correlated with sweat chloride. The clinical utility of these sweat metabolites to prognosticate late-onset CF infants from indeterminate sweat chloride test results was also explored. In summary, this thesis contributes innovative separation methods for biomarker screening and discovery in clinical and epidemiological studies for the prevention and early treatment of human diseases that benefit from optimal nutrition. / Dissertation / Doctor of Philosophy (PhD)

The Support Vector Machine (SVM) classifier seeks to find the separating hyperplane wx=r that maximizes the margin distance 1/||w||2^2. It can be formalized as an optimization problem that minimizes the hinge loss Ʃ[subscript i](1-y[subscript i] f(x[subscript i]))₊ plus the L₂-norm of the weight vector. SVM is now a mainstay method of machine learning. The goal of this dissertation work is to solve different biomedical data analysis problems efficiently using extensions of SVM, in which we augment the standard SVM formulation based on the application requirements. The biomedical applications we explore in this thesis include: cancer diagnosis, biomarker discovery, and energy function learning for protein structure prediction. Ovarian cancer diagnosis is problematic because the disease is typically asymptomatic especially at early stages of progression and/or recurrence. We investigate a sample set consisting of 44 women diagnosed with serous papillary ovarian cancer and 50 healthy women or women with benign conditions. We profile the relative metabolite levels in the patient sera using a high throughput ambient ionization mass spectrometry technique, Direct Analysis in Real Time (DART). We then reduce the diagnostic classification on these metabolic profiles into a functional classification problem and solve it with functional Support Vector Machine (fSVM) method. The assay distinguished between the cancer and control groups with an unprecedented 99\% accuracy (100\% sensitivity, 98\% specificity) under leave-one-out-cross-validation. This approach has significant clinical potential as a cancer diagnostic tool. High throughput technologies provide simultaneous evaluation of thousands of potential biomarkers to distinguish different patient groups. In order to assist biomarker discovery from these low sample size high dimensional cancer data, we first explore a convex relaxation of the L₀-SVM problem and solve it using mixed-integer programming techniques. We further propose a more efficient L₀-SVM approximation, fractional norm SVM, by replacing the L₂-penalty with L[subscript q]-penalty (q in (0,1)) in the optimization formulation. We solve it through Difference of Convex functions (DC) programming technique. Empirical studies on the synthetic data sets as well as the real-world biomedical data sets support the effectiveness of our proposed L₀-SVM approximation methods over other commonly-used sparse SVM methods such as the L₁-SVM method. A critical open problem in emph{ab initio} protein folding is protein energy function design. We reduce the problem of learning energy function for extit{ab initio} folding to a standard machine learning problem, learning-to-rank. Based on the application requirements, we constrain the reduced ranking problem with non-negative weights and develop two efficient algorithms for non-negativity constrained SVM optimization. We conduct the empirical study on an energy data set for random conformations of 171 proteins that falls into the {it ab initio} folding class. We compare our approach with the optimization approach used in protein structure prediction tool, TASSER. Numerical results indicate that our approach was able to learn energy functions with improved rank statistics (evaluated by pairwise agreement) as well as improved correlation between the total energy and structural dissimilarity.