abstract: Glycans are monosaccharide-based heteropolymers that are found covalently attached to many different proteins and lipids and are ubiquitously displayed on the exterior surfaces of cells. Serum glycan composition and structure are well known to be altered in many different types of cancer. In fact, glycans represent a promising but only marginally accessed source of cancer markers. The approach used in this dissertation, which is referred to as “glycan node analysis”, is a molecularly bottom-up approach to plasma/serum (P/S) glycomics based on glycan linkage analysis that captures features such as α2-6 sialylation, β1-6 branching, and core fucosylation as single analytical signals. The diagnostic utility of this approach as applied to lung cancer patients across all stages as well as prostate, serous ovarian, and pancreatic cancer patients compared to certifiably healthy individuals, nominally healthy individuals and/or risk-matched controls is reported. Markers for terminal fucosylation, α2-6 sialylation, β1-4 branching, β1-6 branching and outer-arm fucosylation were most able to differentiate cases from controls. These markers behaved in a stage-dependent manner in lung cancer as well as other types of cancer. Using a Cox proportional hazards regression model, the ability of these markers to predict progression and survival in lung cancer patients was assessed. In addition, the potential mechanistic role of aberrant P/S glycans in cancer progression is discussed. Plasma samples from former bladder cancer patients with currently no evidence of disease (NED), non-muscle invasive bladder cancer (NMIBC), and muscle invasive bladder cancer (MIBC) along with certifiably healthy controls were analyzed. Markers for α2-6 sialylation, β1-4 branching, β1-6 branching, and outer-arm fucosylation were able to separate current and former (NED) cases from controls; but NED, NMIBC, and MIBC were not distinguished from one another. Markers for α2-6 sialylation and β1-6 branching were able to predict recurrence from the NED state using a Cox proportional hazards regression model adjusted for age, gender, and time from cancer. These two glycan features were found to be correlated to the concentration of C-reactive protein, a known prognostic marker for bladder cancer, further strengthening the link between inflammation and abnormal plasma protein glycosylation. / Dissertation/Thesis / Doctoral Dissertation Chemistry 2018

Exposomics applies metabolomics methods and technologies to the comprehensive analysis of all low molecular weight molecules (< 1.5 kDa) in complex biological samples to characterize the interaction between cellular metabolism and exogenous lifestyle exposures that determine health and quality of life. To fully access the diverse classes of biological molecules related to an individual’s metabolic profile, metabolomics frequently requires the use of complementary analytical platforms, and employs targeted and untargeted molecular profiling strategies to identify biomarkers that are clinically relevant to an individual’s health status. Chapter 2 describes a quinoline-based boronic acid biosensor for N-acetylneuraminic acid that undergoes a striking binding enhancement under strongly acidic conditions. For the first time, this work allows for direct analysis of acidic sugars with high selectivity when using UV absorbance or fluorescence detection based on formation of a highly stable boronate ester complex with metabolites containing an α-hydroxycarboxylate moiety. Chapter 3 describes a targeted analysis of 24 different organic contaminants using GC-MS that can serve as biomarkers of recent smoke exposure following search-and-rescue training exercises by firefighters located at three different sites across the province of Ontario. Importantly, skin and possible respiratory uptake of various polycyclic aromatic hydrocarbons, methoxyphenols, and resin acids was confirmed by peak excretion of several wood smoke biomarkers in urine within 6 h following acute exposure. Chapter 4 applied a cross-platform metabolomics strategy based on CE-MS and GC-MS in order to identify and validate dietary biomarkers in matching plasma and urine samples collected from healthy participants in the pilot Diet and Gene Interaction Study (DIGEST). For the first time, we demonstrate that a panel of metabolites can serve as reliable biomarkers following contrasting Prudent and Western diets over 2 weeks of food provisions, which correlated well with self-reported diet records. This work paves the way for the development of objective biomarkers for accurate assessment of wood smoke exposures, as well as complex dietary patterns as required for new advances in occupational health and nutritional epidemiology. / Dissertation / Doctor of Philosophy (PhD) / Exposomics is an emerging multidisciplinary science aimed at deciphering the complex interactions that impact human health and gene expression, such as lifestyle choices (i.e., habitual diet) and lifelong environmental exposures. There is growing interest in identifying biomarkers that can be readily measured for chronic disease prevention given an alarming global prevalence of obesity and cardiometabolic disorders, including heart disease, type 2 diabetes and cancer. The research in this thesis focuses on developing new analytical methods for identifying and quantifying metabolites that may allow for better assessments of human health, and has contributed to the development of novel biosensors for the targeted analysis of N-acetylneuraminic (sialic) acid and related acidic sugars, as well as high resolution methods for broad spectrum analysis of biotransformed organic contaminants from smoke exposure by GC-MS, and plasma and urinary metabolites that differentiate contrasting Prudent and Western diets and correlate well with self-reported diet records.