Databases and tools in glycobiology.

Glycans are crucial to the functioning of multicellular organisms. They may also play a role as mediators between host and parasite or symbiont. As many proteins (>50%) are posttranslationally modified by glycosylation, this mechanism is considered to be the most widespread posttranslational modification in eukaryotes. These surface modifications alter and regulate structure and biological activities/functions of proteins/biomolecules as they are largely involved in the recognition process of the appropriate structure in order to bind to the target cells. Consequently, the recognition of glycans on cellular surfaces plays a crucial role in the promotion or inhibition of various diseases and, therefore, glycosylation itself is considered to be a critical protein quality control attribute for commercial therapeutics, which is one of the fastest growing segments in the pharmaceutical industry. With the development of glycobiology as a separate discipline, a number of databases and tools became available in a similar way to other well-established "omics." Alleviating the recognized shortcomings of the available tools for data storage and retrieval is one of the highest priorities of the international glycoinformatics community. In the last decade, major efforts have been made, by leading scientific groups, towards the integration of a number of major databases and tools into a single portal, which would act as a centralized data repository for glycomics, equipped with a number of comprehensive analytical tools for data systematization, analysis, and comparison. This chapter provides an overview of the most important carbohydrate-related databases and glycoinformatic tools.

Erythropoietin produced in a human cell line (Dynepo) has significant differences in glycosylation compared with erythropoietins produced in CHO cell lines.

Recombinant human erythropoietin has been used to treat anemia associated with chronic renal disease. This paper provides a comprehensive comparative analysis of Dynepo and three other commercial erythropoiesis stimulating agents, Eprex, NeoRecormon and Aranesp. We found significant differences in the type, levels and amount of O-acetylation of sialic acids. Sialic acids and O-acetylation present provide protection from clearance from circulation. Aranesp had up to six O-acetyl groups attached to the sialic acids. Eprex and NeoRecormon had only minor amounts of O-acetylation while Dynepo had none. Dynepo had no Neu5Gc, which is potentially immunogenic for humans. Dynepo contained the least amount of disialylated and Aranesp the highest amount of tetrasialylated glycans. NeoRecormon and Eprex contained more trisialylated, but less tetrasialylated glycans than Dynepo and Aranesp. Dynepo had the highest amount of tetraantennary glycans and the lowest amounts of triantennary glycans with a ß1-6-GlcNAc linkage. All the samples contained poly-N-acetyl-lactosamine repeats with Dynepo having the least. The major N-acetyl-lactosamine extensions in Dynepo and Aranesp were on biantennary glycans, whereas in NeoRecomon and Eprex they were on triantennary glycans. The sLe(x) epitope was only detected in Dynepo.

GlycoExtractor: a web-based interface for high throughput processing of HPLC-glycan data.

Recently, an automated high-throughput HPLC platform has been developed that can be used to fully sequence and quantify low concentrations of N-linked sugars released from glycoproteins, supported by an experimental database (GlycoBase) and analytical tools (autoGU). However, commercial packages that support the operation of HPLC instruments and data storage lack platforms for the extraction of large volumes of data. The lack of resources and agreed formats in glycomics is now a major limiting factor that restricts the development of bioinformatic tools and automated workflows for high-throughput HPLC data analysis. GlycoExtractor is a web-based tool that interfaces with a commercial HPLC database/software solution to facilitate the extraction of large volumes of processed glycan profile data (peak number, peak areas, and glucose unit values). The tool allows the user to export a series of sample sets to a set of file formats (XML, JSON, and CSV) rather than a collection of disconnected files. This approach not only reduces the amount of manual refinement required to export data into a suitable format for data analysis but also opens the field to new approaches for high-throughput data interpretation and storage, including biomarker discovery and validation and monitoring of online bioprocessing conditions for next generation biotherapeutics.