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Brown algae are important players in the global carbon cycle by fixing carbon dioxide into 1 Gt of biomass annually, yet the fate of fucoidan—their major cell wall polysaccharide—remains poorly understood. Microbial degradation of fucoidans is slower than that of other polysaccharides, suggesting that fucoidans are more recalcitrant and may sequester carbon in the ocean. This may be due to the complex, branched and highly sulfated structure of fucoidans, which also varies among species of brown algae. Here, we show that ‘Lentimonas’ sp. CC4, belonging to the Verrucomicrobia, acquired a remarkably complex machinery for the degradation of six different fucoidans. The strain accumulated 284 putative fucoidanases, including glycoside hydrolases, sulfatases and carbohydrate esterases, which are primarily located on a 0.89-megabase pair plasmid. Proteomics reveals that these enzymes assemble into substrate-specific pathways requiring about 100 enzymes per fucoidan from different species of brown algae. These enzymes depolymerize fucoidan into fucose, which is metabolized in a proteome-costly bacterial microcompartment that spatially constrains the metabolism of the toxic intermediate lactaldehyde. Marine metagenomes and microbial genomes show that Verrucomicrobia including ‘Lentimonas’ are abundant and highly specialized degraders of fucoidans and other complex polysaccharides. Overall, the complexity of the pathways underscores why fucoidans are probably recalcitrant and more slowly degraded, since only highly specialized organisms can effectively degrade them in the ocean. ‘Lentimonas’—a marine microorganism from the Verrucomicrobia phylum—has >200 different glycoside hydrolase and sulfatase enzymes enabling digestion of the algal polysaccharide fucoidan, which was thought to be a recalcitrant source of carbon in our oceans.

Big data abound in microbiology, but the workflows designed to enable researchers to interpret data can constrain the biological questions that can be asked. Five years after anvi'o was first published, this community-led multi-omics platform is maturing into an open software ecosystem that reduces constraints in 'omics data analyses. Non

Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 40%-60% of the predicted genes are unknown. Despite previous attempts, systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we propose a conceptual framework and a computational workflow that bridge the known-unknown gap in genomes and metagenomes. We showcase our approach by exploring 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes. We quantify the extent of the unknown fraction, its diversity, and its relevance across multiple biomes. Furthermore, we provide a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria, being a significant resource to expand our understanding of their unusual biology. Finally, by identifying a target gene of unknown function for antibiotic resistance, we demonstrate how we can enable the generation of hypotheses that can be used to augment experimental data.