Developing an Integrative Glycobiology Workflow for the Identification of Disease Markers for Pancreatic Cancer

A deeper understanding of dysregulated glycosylation in pancreatic cancer can provide insights into disease mechanisms and the identification of novel disease markers. Recent improvements in mass spectrometry techniques have been instrumental in profiling biologically relevant tissue sections in order to identify disease marker candidates, but have either not yet been adopted for studying glycosylation or applied directly to pancreatic cancer. In the dissertation herein, new methods have been developed and adapted to the study of aberrant glycosylation in pancreatic cancer, with the ultimate goal of identifying novel disease marker candidates. For the first time, we describe a mass spectrometry imaging approach to study the localization of N-glycans. This technique demonstrated a histology-derived localization of N-glycans across tissue sections, with identifications displaying remarkable consistency with documented studies. Furthermore, the technique provides superior structural information compared to preexisting methodologies. In the analysis of diseased specimen, changes in glycosylation can be linked to aberrations in glycosyltransferase expression. When applied to pancreatic cancer in a high-throughput and high-dimensional analysis, panels of glycans displayed an improved ability to differentiate tumor from non-tumor tissues compared to current disease markers. Furthermore, the data suggest that glycosylation can identify premalignant lesions, as well as differentiate between malignant and benign conditions. These observations overcome significant limitations that hinder the efficacy of current disease markers. In an effort to link aberrant glycosylation to the modified protein, a subset of glycosylated proteins were enriched and analyzed by mass spectrometry to identify proteins that are integral to disease progression and can be probed for the early detection of pancreatic cancer. Known disease markers were among the glycoproteins identified, validating the utility of the enrichment and detection strategy outlined. This approach also differentiated the role of N- and O-glycosylation in antigen expression. Finally, we outline an integrated workflow that takes advantage of the unique capabilities of high resolution mass spectrometers. This workflow can capitalize on prior glycomic and proteomic experiments to provide a comprehensive analysis of dysregulated protein glycosylation in pancreatic cancer

IMass time: The future, in future!

Joseph John Thomson discovered and proved the existence of electrons through a series of experiments. His work earned him a Nobel Prize in 1906 and initiated the era of mass spectrometry (MS). In the intervening time, other researchers have also been awarded the Nobel Prize for significant advances in MS technology. The development of soft ionization techniques was central to the application of MS to large biological molecules and led to an unprecedented interest in the study of biomolecules such as proteins (proteomics), metabolites (metabolomics), carbohydrates (glycomics), and lipids (lipidomics), allowing a better understanding of the molecular underpinnings of health and disease. The interest in large molecules drove improvements in MS resolution and now the challenge is in data deconvolution, intelligent exploitation of heterogeneous data, and interpretation, all of which can be ameliorated with a proposed IMass technology. We define IMass as a combination of MS and artificial intelligence, with each performing a specific role. IMass will offer advantages such as improving speed, sensitivity, and analyses of large data that are presently not possible with MS alone. In this study, we present an overview of the MS considering historical perspectives and applications, challenges, as well as insightful highlights of IMass

O-glycomic and proteomic signatures of spontaneous and butyrate-stimulated colorectal cancer cell line differentiation

Gut microbiota of the gastrointestinal tract provide health benefits to the human host via bacterial metabolites. Bacterial butyrate has beneficial effects on intestinal homeostasis and is the preferred energy source of intestinal epithelial cells, capable of inducing differentiation. It was previously observed that changes in the expression of specific proteins as well as protein glycosylation occur with differentiation. In this study, specific mucin O-glycans were identified that mark butyrate-induced epithelial differentiation of the intestinal cell line CaCo-2 (Cancer Coli-2), by applying porous graphitized carbon nano-liquid chromatography with electrospray ionization tandem mass spectrometry. Moreover, a quantitative proteomic approach was used to decipher changes in the cell proteome. It was found that the fully differentiated butyrate-stimulated cells are characterized by a higher expression of sialylated O-glycan structures, whereas fucosylation is downregulated with differentiation. By performing an integrative approach, we generated hypotheses about the origin of the observed O-glycome changes. These insights pave the way for future endeavors to study the dynamic O-glycosylation patterns in the gut, either produced via cellular biosynthesis or through the action of bacterial glycosidases as well as the functional role of these patterns in homeostasis and dysbiosis at the gut-microbiota interface

Systems Glycobiology: Past, Present, and Future

Glycobiology is a glycan-based field of study that focuses on the structure, function, and biology of carbohydrates, and glycomics is a sub-study of the field of glycobiology that aims to define structure/function of glycans in living organisms. With the popularity of the glycobiology and glycomics, application of computational modeling expanded in the scientific area of glycobiology over the last decades. The recent availability of progressive Wet-Lab methods in the field of glycobiology and glycomics is promising for the impact of systems biology on the research area of the glycome, an emerging field that is termed “systems glycobiology.” This chapter will summarize the up-to-date leading edge in the use of bioinformatics tools in the field of glycobiology. The chapter provides basic knowledge both for glycobiologists interested in the application of bioinformatics tools and scientists of computational biology interested in studying the glycome

Determination of N-Linked Glycosylation Changes in Hepatocellular Carcinoma and the Associated Glycoproteins for Enhanced Biomarker Discovery and Therapeutic Targets

With hepatocellular carcinoma (HCC) remaining as the fifth most common cancer in the world, causing more than 700,000 deaths annually, the need for reliable, early stage diagnoses and preventive treatments is crucial. While serum glycoproteins are hepatic in origin, making them excellent targets for HCC biomarkers, they can originate from both cancerous and non-cancerous regions and direct analysis of cancerous tissue itself is lacking. To counteract this, I hypothesized that direct tissue analysis combined with proteomic analysis could be utilized to identify more potential targets specific to HCC for early detection. This was done with a primary focus on glycosylation—as most clinically approved biomarkers are glycoproteins—and examined direct tissue glycomics in conjunction with glycoproteomic techniques through two specific aims: 1) Determining patterns of N-linked glycan changes in HCC tissue using MALDI imaging mass spectrometry to compare to previously published serum changes and 2) identifying glycopeptides containing changes in observed patterns of N- linked glycans in HCC samples using a targeted glycoproteomic approach. In Aim 1, HCC tissue was examined using MALDI imaging mass spectrometry to v verify changes in glycosylation via direct tissue analysis. Here, it was found that increased branching and fucosylation were directly associated with the cancerous tissue when compared to normal or cirrhotic. To further identify changes in glycosylation, two methods (one novel and one adapted for imaging) were implemented on tissue to further classify N-linked glycan isoforms through linkage analysis, specifically for sialic acids and core fucose. Again, it was shown that core fucose is most directly related to HCC tissue, thus confirming serum findings in the literature. For Aim 2, the novel method of determining core fucosylation was used in conjunction with glycoproteomic techniques to further elucidate the core fucosylated glycoproteins of interest. With the tag left behind following the enzymatic cleavage, targeted glycoproteomics was used to determine glycoproteins of interest while eliminating some biases inherent in the method, such as low ionization efficiencies for more complex N-glycans. This work outlines the first in-depth analysis of HCC tissue specifically regarding N- glycan changes, a novel application to determine N-glycan isoforms, and the application of these methods for glycoproteomic enhancement. With these findings, new trends in glycosylation related to the disease state could be further uncovered, as well as provide new biomarker candidates or therapeutic targets for future studies

Strategies for Proteome-Wide Quantification of Glycosylation Macro- and Micro-Heterogeneity

Protein glycosylation governs key physiological and pathological processes in human cells. Aberrant glycosylation is thus closely associated with disease progression. Mass spectrometry (MS)-based glycoproteomics has emerged as an indispensable tool for investigating glycosylation changes in biological samples with high sensitivity. Following rapid improvements in methodologies for reliable intact glycopeptide identification, site-specific quantification of glycopeptide macro- and micro-heterogeneity at the proteome scale has become an urgent need for exploring glycosylation regulations. Here, we summarize recent advances in N- and O-linked glycoproteomic quantification strategies and discuss their limitations. We further describe a strategy to propagate MS data for multilayered glycopeptide quantification, enabling a more comprehensive examination of global and site-specific glycosylation changes. Altogether, we show how quantitative glycoproteomics methods explore glycosylation regulation in human diseases and promote the discovery of biomarkers and therapeutic targets

Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods

Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe–host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women’s health