Glycoinformatics-assisted Diabetes Glycomics Analysis Service
Unlocking Diabetes Insights with Glycoinformatics Precision
CD BioGlyco provides an advanced
Glycoinformatics-assisted Platform for Glycomics Analysis of various diseases, including
Malaria,
Cancer,
Neuronal Diseases, and diabetes. Diabetes, a chronic metabolic disorder, is marked by persistent hyperglycemia and poses significant risks for long-term complications affecting the nervous system, kidneys, eyes, and cardiovascular system. N-glycosylation, a widespread co-, and post-translational modification, enhances protein structure and function. Alterations in N-glycosylation have been documented across various diseases, including type 1 diabetes, type 2 diabetes, and gestational diabetes, and are increasingly being explored as biomarkers for ongoing pathological states. Elevated levels of specific glycans bound to serum proteins in diabetes patients, which cannot be fully accounted for by increased glycoprotein levels, suggest a direct role of these glycans in diabetes pathophysiology. Therefore, we use an integrated glycoinformatics-assisted glycomics approach to explore and confirm the changes in glycopatterns of diabetes. By leveraging this analysis service, we aim to uncover glycan alterations in diabetes to help clients research novel diagnoses and find new targets.
By using our sophisticated technological platform, which incorporates software for the comprehensive analysis of glycan released from glycoproteins at concentrations as low as low femtomoles.
Sample preparation
We begin by derivatizing the glycans to aid in their detection, followed by desalting and cleaning up the derivated samples. Then release the N- and O-glycans from glycoproteins by using glycosidases. Finally, we conduct purification steps of released N-/O-linked glycans.
Characterization of glycans
Our analytical approaches are versatile and flexible, customized to match the distinctive properties of each sample and the varied needs of our clients. We utilize a diverse array of techniques to meet these needs, ensuring comprehensive and efficient glycomics analysis.
- Chromatography: We employ various chromatography methods, including gas chromatography (GC) and high-performance liquid chromatography (HPLC), each optimized for different sample types and analytes.
- Mass spectrometry (MS): MS is a key technology, that provides unparalleled molecular identification and quantification capabilities. We utilize various MS methods, including GC-MS, LC-MS, and matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) MS, among others.
- Electrophoresis: We offer a range of electrophoresis methods, including gel electrophoresis, capillary electrophoresis, and isoelectric focusing, to cater to the specific needs of our clients.
- Multi-technique integration: We regularly combine various methods into thorough workflows, because no one approach can solve all analytical problems. Combining different technologies enhances both separation efficiency and detection sensitivity, making it the go-to choice for complex glycomics analysis services.
Through these strategies, we analyze a broad variety of samples with unmatched precision and accuracy.
Data analysis
We also leverage advanced software tools to streamline data acquisition, processing, and interpretation, ensuring that our clients receive actionable insights from their samples.
- For HPLC data, we rely on GlycoExtractor and autoGU to interpret glycan traits derived from chromatographic separation. The utilization of these tools helps in extracting valuable data from intricate HPLC profiles, allowing us to precisely identify and describe individual glycans.
- For MS data, several available tools such as Glyco-Peakfinder, GlycoSpectrumScan, GlycoPep Grader, SysBioWare, GlycoMiner, and GlypID are utilized to interpret MS data from scratch. These tools enable us to decipher novel glycan and glycoprotein structures by analyzing MS spectra without relying on pre-existing databases. For rapid and reliable annotation, we also employ tools like GlycoPeptide Search, GlycoSearchMS, and GlycoPep DB, which match recorded MS data against well-annotated databases. The vast knowledge stored in current glycomics and glycoproteomics repositories is utilized to speed up the identification process with this approach.
Publication Data
Technology: Capillary electrophoresis, GlycoStore
Journal: Molecules
Published: 2021
IF: 4.2
Results: In this study, authors introduced a straightforward capillary electrophoresis-based method for analyzing N-glycosylation in type 2 diabetes patients using whole blood samples. They first examined how blood collection, storage, and handling conditions affect the N-glycan profile, including multiple thawing and refreezing cycles and three different types of collection tubes. Their results suggest that using native-type collection tubes is optimal for preserving sialylated structures during analysis. Subsequently, they compared the N-glycan profiles of serum and whole blood samples between healthy individuals and type 2 diabetes patients. Asparagine-linked carbohydrates were enzymatically released, fluorophore-labeled, and analyzed using capillary electrophoresis with high-sensitivity laser-induced fluorescence detection, revealing fifteen distinct N-glycan structures based on glucose unit (GU) values. The structure identification used direct mining from GU database entries via the GUcal application connected to GlycoStore data collection. In the serum analysis, they found minimal differences between the groups, with no significant variations between healthy serum and T2D serum or between healthy serum and blood samples. In contrast, the whole blood profile showed significant changes, with notable increases in the concentrations of FA2G1S1 and A2BG2S1 glycans (6.4× and 8.2×, respectively) in T2D cases. These two potential biomarkers indicate the need for further research with a larger patient cohort for statistical validation. Furthermore, examining specific IgG N-glycosylation profiles in T2D may provide insights into underlying inflammatory processes and identify new drug targets.
Advantages
- Our comprehensive suite of advanced technologies, coupled with a deep understanding of various methodologies, enables us to design customized analytical schemes that guarantee precision, accuracy, and efficiency in every project.
- We use tools that do not rely on pre-existing databases for discovering novel glycan and glycoprotein structures.
- We can conduct high-throughput analysis to efficiently process large volumes of data.
Applications
- Through our glycoinformatics-assisted diabetes glycomics analysis service, we help identify specific glycan biomarkers associated with diabetes. These biomarkers can be used to research diagnostic methods at an earlier stage.
- Our glycoinformatics-assisted diabetes glycomics analysis service can be used to research the role of glycans in diabetes pathology by analyzing how glycan structures influence insulin signaling pathways and glucose metabolism.
- Our glycoinformatics-assisted diabetes glycomics analysis service can be applied to studying how alterations in glycosylation affect cellular interactions and contribute to diabetes-related complications.
Frequently Asked Questions
- What complications can diabetes cause?
- Cardiovascular disease
- Neuropathy
- Retinopathy
- Nephropathy
- Hearing impairment
- Cognitive decline
- Foot problems
- What are the biomarkers of diabetes?
- Hemoglobin A1c (HbA1c): The formation of HbA1c occurs when glucose binds to the amino-terminal group of the β subunit of hemoglobin. Chronic glycemia is reflected by HbA1c.
- Fructosamine (FA): FA is a ketoamine formed by the glycosylation of total serum proteins, primarily albumin, and its levels increase with high glucose concentrations.
- Fetuin-A (FetA): FetA, a hepatic secretory glycoprotein, is thought to induce lipid-related insulin resistance by activating the TLR4-inflammatory signaling pathway, which leads to the production of inflammatory cytokines.
- Glycated albumin (GA): Glycosylation of albumin, measured by the ratio of GA to total albumin, is associated with prediabetes and diabetes.
- 1,5 Anhydroglucito (1,5 AG): Plasma concentrations of the dietary monosaccharide 1,5 AG are inversely correlated with plasma glucose levels, and are lower in individuals with prediabetes and diabetes compared to those with normoglycemia.
CD BioGlyco uses advanced
Glycoinformatics Tools to provide a comprehensive analysis of glycomic profiles in diabetes. By integrating cutting-edge technologies and sophisticated data analytics, we offer detailed insights into glycosylation patterns and their impact on diabetes progression and management. If you would like to identify biomarkers and understand disease mechanisms, please contact us.
Reference
- Torok, R.; et al. N-Glycosylation profiling of human blood in type 2 diabetes by capillary electrophoresis: a preliminary study. Molecules. 2021, 26(21): 6399.
For research use only. Not intended for any diagnostic use.
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