Glycoproteomics is the systems-wide study of glycoproteins: proteins with complex sugar molecules, or glycans, attached to them. Glycoproteins play critical roles in biological processes, including cell signaling, immune response, and the development of diseases.
This field involves the integration of advanced techniques in mass spectrometry, biochemistry, and artificial intelligence with bioinformatics tools to analyze the complex structures and functions of glycoproteins in biological systems.
Learn the basics of glycobiology and why we study glycoproteins in this engaging, illustrated video.
Glycosylation is a common post-translational modification process through which glycans are covalently attached to proteins.
Different types of glycosylation exist, depending on where the glycan is attached to the protein.
By exploring how proteins are glycosylated and how these modifications affect protein function, we can uncover invaluable insights into the mechanisms of health and disease. We can discover new biomarkers for disease diagnosis, understand disease progression, and develop targeted therapies, making glycoproteomics a cornerstone of modern biomedical research and personalized medicine.
Glycosylation controls the activation state of immune cells. Altered glycosylation patterns on cancer cells can promote tumor growth, metastasis, and evasion of the immune system. Researchers, including our founder Dr. Carolyn Bertozzi, have shown that Siglec receptors in immune cells act as breaks for cell activation and prevent unwanted inflammation. However, cancer cells can hijack this mechanism and avoid immune responses by increasing the production of sialic acid, the ligand of Siglecs. This knowledge has led to the development of therapeutics that block this negative regulation by removing sialic acid from cancer cells and restoring the balance of the immune system. See paper.
Glycosylation of blood proteins distinguish specific physiological states. The protein glycosylation patterns change when we develop a disease and can be used to understand a person’s physiological state. Researchers, including our founder Dr. Carlito Lebrilla, have used glycoproteomics to define alterations in glycosylation in blood and and other fluids that are associated with disease. See paper.
Watch InterVenn Co-Founder and Nobel Prize laureate, Carolyn Bertozzi, present her TEDx talk about glycans and their implications on holistic disease understanding.
Mass spectrometry is a pivotal analytical technique in glycoproteomics, offering unparalleled specificity and sensitivity in identifying and quantifying glycoproteins along with their complex glycan structures.
How mass spectrometers work
The process starts with the enzymatic or chemical digestion of glycoproteins to generate glycopeptides, which are then ionized before being analyzed in the mass spectrometer. Mass spectrometry can distinguish glycopeptides based on their mass-to-charge (m/z) ratios and on the precise way they break apart (fragmentation), facilitating the understanding of glycan composition, structure, and attachment sites.
Complexity of data output
Glycoproteomics analysis presents significant challenges, including the diversity of glycan structures (glycoforms), the low abundance of specific glycoforms, and the need for high-resolution instruments to identify complex mixtures of glycopeptides and glycans.
The integration of bioinformatics and AI has revolutionized the field of glycoproteomics by addressing the significant challenges associated with data analysis and interpretation. Given the complexity and diversity of glycoproteins and their glycan structures, the vast amount of data generated by mass spectrometry analyses requires sophisticated computational tools for efficient and accurate processing. Bioinformatics offers the computational framework necessary for managing, analyzing, and interpreting these large datasets, facilitating the identification and quantification of glycoproteins and their glycan moieties.
AI and machine learning (ML)
Artificial intelligence (AI) and machine learning (ML) models, in particular, play a critical role in deciphering the intricate patterns and relationships within glycoproteomics data. These models can be trained on vast datasets to learn the structural features of glycans, predict glycosylation sites on proteins, and even identify novel glycan structures that have not been previously characterized.
Clinical evidence based on glycoproteomics has been accumulating, highlighting the significant role of glycoproteins and their glycan modifications in various diseases. Glycoproteomics, by detailing the structure and function of glycoproteins, offers insights into disease mechanisms, potential biomarkers for diagnosis and prognosis, and targets for therapeutic intervention. Here are some areas where glycoproteomics has provided clinical evidence:
Glycoproteomics has identified alterations in glycosylation patterns that are characteristic of tumor cells. For instance, analysis of glycoproteins like MUC1 (mucin 1), CA19-9 and PSA (prostate-specific antigen) have been linked to cancer progression and metastasis. The differential glycosylation patterns observed in cancer cells compared to normal cells provide potential biomarkers for early cancer detection. In addition, glycoproteomics has led to the identification of biomarkers associated with response to treatment.
Multiple reaction monitoring for the quantitation of serum protein glycosylation profiles: Application to ovarian cancer Novel plasma glycoprotein biomarkers predict progression-free survival in surgically resected clear cell renal cell carcinoma Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy
The role of glycoproteins in viral infections - including the entry of viruses into host cells - has been investigated using glycoproteomics. For example, the glycosylation of viral envelope proteins affects the infectivity and immune evasion strategies of viruses like HIV and influenza. Understanding these glycan-mediated interactions has implications for vaccine development and antiviral therapies.
Differential peripheral blood glycoprotein profiles In symptomatic and asymptomatic COVID-19
The study of glycosylation patterns in neurodegenerative diseases like Alzheimer’s disease (AD) and Parkinson’s disease (PD) has revealed potential biomarkers and therapeutic targets. Glycoproteomics analyses of cerebrospinal fluid and brain tissues have identified specific glycosylation changes in proteins related to these conditions, offering insights into their pathogenesis and potential avenues for intervention.
Glycosylation alterations in serum of Alzheimer’s disease patients show widespread changes in N-glycosylation of proteins related to immune function, inflammation, and lipoprotein metabolism
Glycosylation changes in immune system proteins have been associated with autoimmune and inflammatory diseases. Glycoproteomics studies have identified specific glycan structures on immunoglobulins, such as altered galactosylation and sialylation, that correlate with the activity of diseases like rheumatoid arthritis and systemic lupus erythematosus. These glycan modifications can serve as markers for disease severity and therapeutic response.
Glycans in the immune system and The Altered Glycan Theory of Autoimmunity: A critical review Serum glycoprotein markers for predicting stages of fibrosis in non-alcoholic steatohepatitis (NASH)
Glycoproteomics provides a unique perspective into human health by defining the roles of glycosylation in regulating health and disease.
It complements other -omics fields by contributing detailed information about protein modifications that critically influence their protein function and stability, offering insights into disease mechanisms, biomarker discovery, and potential therapeutic targets that are not apparent through other -omics analyses alone. Integrating data from all -omics fields is a powerful approach for understanding biological systems.
While genomics gives you the blueprint of your potential health outcomes, the pathology of disease is very complex. Protein glycosylation is closer to the inherent drivers of disease, and provides a window into the dynamic nature of human health.
While proteomics provides a broad view of the proteins present and their potential functions, glycoproteomics gives a detailed view of the glycosylation of proteins, a critical post-translational modification that affects protein function, localization, stability, and interactions.
Transcriptomics shows which genes are being actively transcribed, but not all RNAs will be translated into proteins. Glycoproteomics provides a closer view of the actual functional molecules (proteins) and their post-translational modifications in the cell, which are critical for understanding the functional state of a cell or tissue.
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