Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.
Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.
Cell Rep. 2023 Jul 25;42(7):112710. doi: 10.1016/j.celrep.2023.112710. Epub 2023 Jun 27.
Milk oligosaccharides (MOs) are among the most abundant constituents of breast milk and are essential for health and development. Biosynthesized from monosaccharides into complex sequences, MOs differ considerably between taxonomic groups. Even human MO biosynthesis is insufficiently understood, hampering evolutionary and functional analyses. Using a comprehensive resource of all published MOs from >100 mammals, we develop a pipeline for generating and analyzing MO biosynthetic networks. We then use evolutionary relationships and inferred intermediates of these networks to discover (1) systematic glycome biases, (2) biosynthetic restrictions, such as reaction path preference, and (3) conserved biosynthetic modules. This allows us to prune and pinpoint biosynthetic pathways despite missing information. Machine learning and network analysis cluster species by their milk glycome, identifying characteristic sequence relationships and evolutionary gains/losses of motifs, MOs, and biosynthetic modules. These resources and analyses will advance our understanding of glycan biosynthesis and the evolution of breast milk.
乳寡糖（MOs）是母乳中最丰富的成分之一，对健康和发育至关重要。MOs 由单糖生物合成形成复杂序列，在分类群之间差异很大。即使人类 MO 生物合成也未被充分了解，这阻碍了进化和功能分析。我们使用来自 >100 种哺乳动物的所有已发表 MO 的综合资源，开发了一种用于生成和分析 MO 生物合成网络的管道。然后，我们使用进化关系和这些网络的推断中间体来发现（1）系统糖组偏见，（2）生物合成限制，例如反应途径偏好，以及（3）保守的生物合成模块。这使我们能够在信息缺失的情况下修剪和精确定位生物合成途径。机器学习和网络分析根据乳聚糖将物种聚类，确定特征序列关系以及基序、MO 和生物合成模块的进化增益/损失。这些资源和分析将促进我们对聚糖生物合成和母乳进化的理解。
