GlycoEnzOnto: a GlycoEnzyme pathway and molecular function ontology

Abstract

Motivation: The 'glycoEnzymes' include a set of proteins having related enzymatic, metabolic, transport, structural and cofactor functions. Currently, there is no established ontology to describe glycoEnzyme properties and to relate them to glycan biosynthesis pathways.

Results: We present GlycoEnzOnto, an ontology describing 403 human glycoEnzymes curated along 139 glycosylation pathways, 134 molecular functions and 22 cellular compartments. The pathways described regulate nucleotide-sugar metabolism, glycosyl-substrate/donor transport, glycan biosynthesis and degradation. The role of each enzyme in the glycosylation initiation, elongation/branching and capping/termination phases is described. IUPAC linear strings present systematic human/machine-readable descriptions of individual reaction steps and enable automated knowledge-based curation of biochemical networks. All GlycoEnzOnto knowledge is integrated with the Gene Ontology biological processes. GlycoEnzOnto enables improved transcript overrepresentation analyses and glycosylation pathway identification compared to other available schema, e.g. KEGG and Reactome. Overall, GlycoEnzOnto represents a holistic glycoinformatics resource for systems-level analyses.

Availability and implementation: https://github.com/neel-lab/GlycoEnzOnto.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

FUT4 description in GlycoEnzOnto. Classes are depicted as ellipses and instances as diamonds. ‘Genzo’/yellow ellipses represent classes contributed by GlycoEnzOnto, ‘up’/light-blue represents the UniProt namespace, and ‘GO‘/green represents the GO namespace. Unless labeled, all edges connecting classes and instances have ‘rdfs: subClassOf’ relationship. Arrows depict the object properties linking individuals and classes

Fig. 2.

GlycoEnzOnto classification. A total of 403 glycoEnzymes were classified based on either molecular function (A), pathway (B) or compartment (C). The number of members in each (sub-)group is shown using black text next to the individual entries. Some members may appear in more than one subgroup

Fig. 3.

N-linked glycosylation pathway generation using GlycoEnzOnto reaction rules and constraints. (A) Depiction of five types of reactions described by GlycoEnzOnto (see Table 1 for more details). These are presented using specific glycoEnzyme examples: ST6Gal1, Neu4, DSE/DSEL, MPI and SLC35C1 (all part of GlycoEnzOnto). (B) Glycan structures shown in box were seeded into the network generation algorithm, along with enzymes B4GALT1, MGAT2, MGAT4, ST6GAL1 and ST3GAL1. Results from the first cycle of product inference is shown, with newly generated glycans outside of the boxed area. Note that, in this example, the (α2-3)sialytransferase ST3GAL1 does not process any of the input glycans as they do not contain the required reactive Type III lactosamine substrate. Thus, it is not part of the figure. Additional cycles may be performed to generate a larger network. All figures are presented using the Symbol Nomenclature For Glycans (SNFG) (Neelamegham et al., 2019)

Fig. 4.

Glycosylation pathway enrichment. Enrichment tests were performed to compare the transcriptome of normal breast tissue with Her2⁺ cancer tissue. Differential expression analysis was performed upon both considering the entire human transcriptome (26 686 genes) as the universe (A, B), and upon just considering the 403 glycoEnzymes (C, D). Upregulated and downregulated pathways in these panels are highlighted in red in panel A, and blue in panel C. Lighter shades indicate pathways that are altered, but not statistically significantly modified. Bar plots illustrate increased glycogenes that are part of the dolichol biosynthesis pathway in Her2⁺ breast tissue (B), and lacto-series initiation enzymes that are decreased during cancer (D)