Literature DB >> 30199235

Efficient Synthesis of Glycosaminoglycan Analogs.

Chengzhe Gao, Kevin J Edgar.   

Abstract

Glycosaminoglycans (GAGs) are among the most complex, biologically active polysaccharides in nature. The complexity of GAGs greatly impedes their synthesis, thus complicating the structure-property studies that are so necessary for us to understand the roles of GAGs in natural processes, in pathogen invasion, and to understand how to develop effective interventions, for example, to prevent undesired GAG hijacking by pathogens. Total synthesis of GAG oligomers from monosaccharide building blocks is useful, but incredibly labor-intensive, expensive, and inefficient. In this study, we report a regiospecific synthetic route to two types of designed GAG analogs by chemical modification of commercially available, inexpensive cellulose acetate. Cellulose acetate was first brominated, followed by azide displacement to introduce azides as the GAG amine precursors. The resulting 6-N3 cellulose acetate was then saponified to liberate 6-OH groups. Subsequent oxidation of the liberated primary hydroxyl groups to carboxyl groups was smoothly effected by a TEMPO-catalyzed process. Finally, the azides were reduced to amines using an aqueous process, new to polysaccharide chemistry, employing reduction by dithiothreitol (DTT). Alternatively, another process new to polysaccharide chemistry could be employed to convert most of the azides to acetamido groups (mimicking those present, for example, in native hyaluronic acid) by reduction with thioacetic acid. All the intermediates and products were characterized by 1H NMR, 13C NMR, and FT-IR spectroscopy. This synthetic route provides access to GAG analogs that will be of great interest for exploring structure-property relationships in various biomedical applications.

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Year:  2018        PMID: 30199235     DOI: 10.1021/acs.biomac.8b01150

Source DB:  PubMed          Journal:  Biomacromolecules        ISSN: 1525-7797            Impact factor:   6.988


  1 in total

1.  A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data.

Authors:  Norah Alghamdi; Wennan Chang; Pengtao Dang; Xiaoyu Lu; Changlin Wan; Silpa Gampala; Zhi Huang; Jiashi Wang; Qin Ma; Yong Zang; Melissa Fishel; Sha Cao; Chi Zhang
Journal:  Genome Res       Date:  2021-07-22       Impact factor: 9.043

  1 in total

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