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Biology Articles » Biochemistry » Carbohydrate Biochemistry » Large-scale approaches for glycobiology » Unraveling the biosynthetic glycosylation machinery

Unraveling the biosynthetic glycosylation machinery
- Large-scale approaches for glycobiology

Although many recent developments in 'glycomics' focus on structural and functional analysis of surface-displayed sugars, the biosynthetic machinery that builds these complex molecules also greatly interests the glycobiologist. We briefly discuss carbohydrate biosynthesis here, both to acknowledge the heroic researchers who laid an impressive foundation without benefit of large-scale technologies and to illustrate the need for high-throughput strategies to accelerate progress. We use the term glycosylation machinery to describe biochemical pathways that convert monosaccharides (for example, dietary glucosamine) into nine different high-energy sugar-nucleotide building blocks (for example, UDP-N-acetylglucosamine (UDP-GlcNAc)) and assemble them into the complex oligosaccharides found on proteins and lipids (Figure 1). Basic components of this metabolic factory were discovered in a painstakingly slow, one-at-a-time process over many decades (for a detailed perspective, see the fascinating historical overview by Saul Roseman [3]). Traditional biochemical studies from the 1950s to the 1970s identified many small-molecule metabolites and characterized the enzymatic activities that link them into metabolic pathways. Once metabolites were arranged into putative pathways, the next requirement was to match genes with enzymatic activities; this formidable task was tackled, primarily one gene at a time, by elegant but time-consuming methods such as the forward genetic screens developed in the 1970s, and by the DNA cloning and recombinant gene expression strategies that became routine in the 1980s [4]. More recently, RNA-inhibition techniques have begun to yield insights into glycosylation by downregulating individual genes [5].

Around 2% of human genes are involved in glycosylation, as judged from the most recent developments in large-scale biology, primarily the sequencing of the human genome coupled with predictive algorithms for gene function. This information, along with 'metabolomic' methods for large-scale characterization of small-molecule metabolites [6], has sped up the placement of the finishing touches on the framework of the glycosylation machinery. Almost all its metabolic components are known and have been assembled into well defined pathways, as can be seen by following the links for 'Carbohydrate metabolism' and 'Glycan biosynthesis and metabolism' in the Kyoto Encyclopedia of Genes and Genomes, KEGG [7]. A static picture of glycosylation does not, however, reflect dynamic moment-by-moment, developmental, and disease-related metabolic fluctuations, nor does it provide much insight into subcellular organization and organelle topography, which are critical factors in shaping final oligosaccharide structures [8]. In the future, computational 'systems biology' promises to bring the glycosylation machinery to life [9] and thereby offers insights into repairing glycosylation abnormalities associated with widespread diseases, including diabetes [5] and cancer [10].

 


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