Unveiling CAZyme modularity patterns: Comparative genomics links domain architecture to arthropod diets and life...

Carbohydrate-active enzymes (CAZymes) are proteins that assemble, remodel, and depolymerize complex carbohydrates. They are ubiquitous across the tree of life, underpinning energy capture, cell-wall architecture, microbiome interactions, and host-pathogen dynamics. CAZymes are modular in structure and function, being tightly related to the overall enzyme architecture. Domain architecture constrains folding, substrate range, and integration into metabolic pathways. Arthropods, the most species-rich animal phylum, offer a powerful system to study enzymatic modularity because their enzymatic machineries must function in extremely diverse ranges of diet, niche, and abiotic conditions. Here we built a genome-scale framework to characterize CAZyme modular organization across Arthropoda and to test how domain architecture is influenced by diets, life histories and clades while revealing emergent functional relationships. We find that unimodular architectures dominate CAZyme repertoires, while multimodular and class-exclusive arrangements remain rare but highly variable. We identify herbivory as responsible for the discrimination on three CAZyme structural strategies (saprophagous, leaf-stem-root, and pollen-nectar feeders) differing in class composition, modular complexity, and catalytic emphasis. Correlation and co-occurrence analyses at genomic and protein levels show that expansions of glycosyltransferases, glycoside hydrolases, carbohydrate-binding modules, and auxiliary activities are tightly coupled, especially in myriapods and crustaceans, and that intra-protein networks of CAZyme families are denser in hexapods, holometabolans, and herbivores. Within these networks, GT2 and hydrolytic domains occupy central positions in co-occurrence clusters. Together, our findings indicate that arthropod CAZymes evolve as integrated macromolecular systems whose modularity and class balance track ecological demands, providing a scalable framework for engineering CAZyme networks for biotechnological and pest-management applications.