Mammalian Milk Glycomes: Connecting the Dots between Evolutionary Conservation and Biosynthetic Pathways

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 nonparametric pipeline for generating and analyzing MO biosynthetic networks, which readily generalizes to other glycan classes. We then use evolutionary relationships and inferred intermediates of these networks to discover (i) distributional glycome biases, (ii) biosynthetic restrictions, such as reaction path dependence, and (iii) 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.