Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan


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.

This is a preview of subscription content

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: ‘Lentimonas’ sp. CC4 has a megaplasmid and distinct genetic loci for the degradation of sulfated polysaccharides.
Fig. 2: Degradation of complex fucoidans by specialized ‘Lentimonas’ spp.
Fig. 3: Differential proteomics reveals pathways for fucoidan degradation.
Fig. 4: Enzymatic specificity of degradation pathways for diverse fucoidans.
Fig. 5: High metabolic burden to express dedicated pathways for fucoidans including a fucose-specific BMC.
Fig. 6: Verrucomicrobia are abundant and specialized polysaccharide degraders.

Data availability

The data generated and analysed in the current study are publicly available. The genomic and transcriptomic data presented in this study were deposited under the accession code PRJEB34624 in the European Nucleotide Archive using the data brokerage service of the German Federation for Biological Data, in compliance with the minimal information about any (x) sequence (MIxS) standard77,78,79. Mass spectrometry data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD015328. The miTAGs from OSD and Tara Oceans were deposited at Strain ‘Lentimonas’ sp. CC4 was deposited at the Deutsche Sammlung von Mikroorganismen und Zellkulturen under accession DSM 110005. Reads of all genomes were deposited under accession SAMEA6101985 to SAMEA6101992 and assemblies are deposited under GCA_902726575, GCA_902726585, GCA_902726595, GCA_902726605, GCA_902728225, GCA_902728235, GCA_902728245 and GCA_902728255. The reads for ‘Lentimonas’ sp. CC4 and ‘Lentimonas’ sp. CC6 are deposited under the accessions SAMEA6101984 and SAMEA6101969, respectively.

Code availability

The workflow for analysing the metagenomic and miTAG data can be found at


  1. 1.

    Wang, M. et al. The great Atlantic Sargassum belt. Science 365, 83–87 (2019).

    CAS  PubMed  Google Scholar 

  2. 2.

    Field, C. B. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).

    CAS  PubMed  Google Scholar 

  3. 3.

    Krause-Jensen, D. & Duarte, C. M. Substantial role of macroalgae in marine carbon sequestration. Nat. Geosci. 9, 737–742 (2016).

    CAS  Google Scholar 

  4. 4.

    Deniaud-Bouët, E. et al. Chemical and enzymatic fractionation of cell walls from Fucales: insights into the structure of the extracellular matrix of brown algae. Ann. Bot. 114, 1203–1216 (2014).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Trevathan-Tackett, S. M. et al. Comparison of marine macrophytes for their contributions to blue carbon sequestration. Ecology 96, 3043–3057 (2015).

    PubMed  Google Scholar 

  6. 6.

    Deniaud-Bouët, E., Hardouin, K., Potin, P., Kloareg, B. & Hervé, C. A review about brown algal cell walls and fucose-containing sulfated polysaccharides: cell wall context, biomedical properties and key research challenges. Carbohydr. Polym. 175, 395–408 (2017).

    PubMed  Google Scholar 

  7. 7.

    Arnosti, C. Microbial extracellular enzymes and the marine carbon cycle. Ann. Rev. Mar. Sci. 3, 401–425 (2011).

    PubMed  Google Scholar 

  8. 8.

    Kopplin, G. et al. Structural characterization of fucoidan from Laminaria hyperborea: assessment of coagulation and inflammatory properties and their structure–function relationship. ACS Appl. Bio. Mater. 1, 1880–1892 (2018).

    CAS  Google Scholar 

  9. 9.

    Skriptsova, A. V., Shevchenko, N. M., Zvyagintseva, T. N. & Imbs, T. I. Monthly changes in the content and monosaccharide composition of fucoidan from Undaria pinnatifida (Laminariales, Phaeophyta). J. Appl. Phycol. 22, 79–86 (2010).

    CAS  Google Scholar 

  10. 10.

    Cong, Q. et al. Structural characterization and effect on anti-angiogenic activity of a fucoidan from Sargassum fusiforme. Carbohydr. Polym. 136, 899–907 (2016).

    CAS  PubMed  Google Scholar 

  11. 11.

    Chevolot, L., Mulloy, B., Ratiskol, J., Foucault, A. & Colliec-Jouault, S. A disaccharide repeat unit is the major structure in fucoidans from two species of brown algae. Carbohydr. Res. 330, 529–535 (2001).

    CAS  PubMed  Google Scholar 

  12. 12.

    Bilan, M. I. et al. Further studies on the composition and structure of a fucoidan preparation from the brown alga Saccharina latissima. Carbohydr. Res. 345, 2038–2047 (2010).

    CAS  PubMed  Google Scholar 

  13. 13.

    Van Vliet, D. M. et al. Anaerobic degradation of sulfated polysaccharides by two novel Kiritimatiellales strains isolated from black sea sediment. Front. Microbiol. 10, 253 (2019).

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Silchenko, A. et al. Hydrolysis of fucoidan by fucoidanase isolated from the marine bacterium, Formosa algae. Mar. Drugs 11, 2413–2430 (2013).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Barbeyron, T., L’Haridon, S., Michel, G. & Czjzek, M. Mariniflexile fucanivorans sp. nov., a marine member of the Flavobacteriaceae that degrades sulphated fucans from brown algae. Int. J. Syst. Evol. Microbiol. 58, 2107–2113 (2008).

    CAS  PubMed  Google Scholar 

  16. 16.

    Chen, F., Chang, Y., Dong, S. & Xue, C. Wenyingzhuangia fucanilytica sp. nov., a sulfated fucan utilizing bacterium isolated from shallow coastal seawater. Int. J. Syst. Evol. Microbiol. 66, 3270–3275 (2016).

    CAS  PubMed  Google Scholar 

  17. 17.

    Sakai, T., Ishizuka, K. & Kato, I. Isolation and characterization of a fucoidan-degrading marine bacterium. Mar. Biotechnol. 5, 409–416 (2003).

    CAS  PubMed  Google Scholar 

  18. 18.

    Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, 490–495 (2014).

    Google Scholar 

  19. 19.

    Hettle, A. G. et al. The molecular basis of polysaccharide sulfatase activity and a nomenclature for catalytic subsites in this class of enzyme. Structure 26, 747–758 (2018).

    CAS  PubMed  Google Scholar 

  20. 20.

    Barbeyron, T. et al. Matching the diversity of sulfated biomolecules: creation of a classification database for sulfatases reflecting their substrate specificity. PLoS ONE 11, e0164846 (2016).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Berteau, O., McCort, I., Goasdoué, N., Tissot, B. & Daniel, R. Characterization of a new α-l-fucosidase isolated from the marine mollusk Pecten maximus that catalyzes the hydrolysis of α-l-fucose from algal fucoidan (Ascophyllum nodosum). Glycobiology 12, 273–282 (2002).

    CAS  PubMed  Google Scholar 

  22. 22.

    Nagao, T. et al. Gene identification and characterization of fucoidan deacetylase for potential application to fucoidan degradation and diversification. J. Biosci. Bioeng. 124, 277–282 (2017).

    CAS  PubMed  Google Scholar 

  23. 23.

    Silchenko, A. S. et al. Fucoidan sulfatases from marine bacterium Wenyingzhuangia fucanilytica CZ1127T. Biomolecules 8, 98 (2018).

    PubMed Central  Google Scholar 

  24. 24.

    Vickers, C. et al. Endo-fucoidan hydrolases from glycoside hydrolase family 107 (GH107) display structural and mechanistic similarities to α-l-fucosidases from GH29. J. Biol. Chem. 293, 18296–18308 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Colin, S. et al. Cloning and biochemical characterization of the fucanase FcnA: definition of a novel glycoside hydrolase family specific for sulfated fucans. Glycobiology 16, 1021–1032 (2006).

    CAS  PubMed  Google Scholar 

  26. 26.

    Schultz-Johansen, M. et al. Discovery and screening of novel metagenome-derived GH107 enzymes targeting sulfated fucans from brown algae. FEBS J. 285, 4281–4295 (2018).

    CAS  PubMed  Google Scholar 

  27. 27.

    Silchenko, A. S. et al. Expression and biochemical characterization and substrate specificity of the fucoidanase from Formosa algae. Glycobiology 27, 254–263 (2017).

    CAS  PubMed  Google Scholar 

  28. 28.

    Ndeh, D. et al. Complex pectin metabolism by gut bacteria reveals novel catalytic functions. Nature 544, 65–70 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Reisky, L. et al. A marine bacterial enzymatic cascade degrades the algal polysaccharide ulvan. Nat. Chem. Biol. 15, 803–812 (2019).

    CAS  PubMed  Google Scholar 

  30. 30.

    Wegner, C.-E. et al. Expression of sulfatases in Rhodopirellula baltica and the diversity of sulfatases in the genus Rhodopirellula. Mar. Genom. 9, 51–61 (2013).

    Google Scholar 

  31. 31.

    Thrash, J. C., Cho, J. C., Vergin, K. L., Morris, R. M. & Giovannoni, S. J. Genome sequence of Lentisphaera araneosa HTCC2155T, the type species of the order Lentisphaerales in the phylum Lentisphaerae. J. Bacteriol. 192, 2938–2939 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Almagro Armenteros, J. J. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Martens, E. C., Chiang, H. C. & Gordon, J. I. Mucosal glycan foraging enhances fitness and transmission of a saccharolytic human gut bacterial symbiont. Cell Host Microbe 4, 447–457 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Ficko-Blean, E. et al. Carrageenan catabolism is encoded by a complex regulon in marine heterotrophic bacteria. Nat. Commun. 8, 1685 (2017).

    PubMed  PubMed Central  Google Scholar 

  35. 35.

    Nishino, T., Nishioka, C., Ura, H. & Nagumo, T. Isolation and partial characterization of a novel amino sugar-containing fucan sulfate from commercial Fucus vesiculosus fucoidan. Carbohydr. Res. 255, 213–224 (1994).

    CAS  PubMed  Google Scholar 

  36. 36.

    Bilan, M. I., Grachev, A. A., Shashkov, A. S., Nifantiev, N. E. & Usov, A. I. Structure of a fucoidan from the brown seaweed Fucus serratus L. Carbohydr. Res. 341, 238–245 (2006).

    CAS  PubMed  Google Scholar 

  37. 37.

    Kappelmann, L. et al. Polysaccharide utilization loci of North Sea Flavobacteriia as basis for using SusC/D-protein expression for predicting major phytoplankton glycans. ISME J. 13, 76–91 (2019).

    CAS  PubMed  Google Scholar 

  38. 38.

    Corzett, C. H. et al. Evolution of a vegetarian vibrio: metabolic specialization of Vibrio breoganii to macroalgal substrates. J. Bacteriol. 200, e00020-18 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Labourel, A. et al. The mechanism by which arabinoxylanases can recognise highly decorated xylans. J. Biol. Chem. 291, 22149–22159 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Hehemann, J.-H. et al. Biochemical and structural characterization of the complex agarolytic enzyme system from the marine bacterium Zobellia galactanivorans. J. Biol. Chem. 287, 30571–30584 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Katayama, T. et al. Molecular cloning and characterization of Bifidobacterium bifidum 1,2-α-l-fucosidase (AfcA), a novel inverting glycosidase (glycoside hydrolase family 95). J. Bacteriol. 186, 4885–4893 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Rogowski, A. et al. Glycan complexity dictates microbial resource allocation in the large intestine. Nat. Commun. 6, 7481 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Heinze, S. et al. Identification of endoxylanase XynE from Clostridium thermocellum as the first xylanase of glycoside hydrolase family GH141. Sci. Rep. 7, 11178 (2017).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Davies, G. J., Wilson, K. S. & Henrissat, B. Nomenclature for sugar-binding subsites in glycosyl hydrolases. Biochem. J. 321, 557–559 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Stam, M. R., Danchin, E. G. J., Rancurel, C., Coutinho, P. M. & Henrissat, B. Dividing the large glycoside hydrolase family 13 into subfamilies: towards improved functional annotations of α-amylase-related proteins. Protein Eng. Des. Sel. 19, 555–562 (2006).

    CAS  PubMed  Google Scholar 

  46. 46.

    Mewis, K., Lenfant, N., Lombard, V. & Henrissat, B. Dividing the large glycoside hydrolase family 43 into subfamilies: a motivation for detailed enzyme characterization. Appl. Environ. Microbiol. 82, 1686–1692 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Viborg, A. H. et al. A subfamily roadmap of the evolutionarily diverse glycoside hydrolase family 16 (GH16). J. Biol. Chem. 294, 15973–15986 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Hobbs, J. K., Pluvinage, B., Robb, M., Smith, S. P. & Boraston, A. B. Two complementary α-fucosidases from Streptococcus pneumoniae promote complete degradation of host-derived carbohydrate antigens. J. Biol. Chem. 294, 12670–12682 (2019).

    CAS  PubMed  Google Scholar 

  49. 49.

    Biely, P., Benen, J., Heinrichová, K., Kester, H. C. M. & Visser, J. Inversion of configuration during hydrolysis of α-1,4-galacturonidic linkage by three Aspergillus polygalacturonases. FEBS Lett. 382, 249–255 (1996).

    CAS  PubMed  Google Scholar 

  50. 50.

    Tenkanen, M. & Siika-aho, M. An α-glucuronidase of Schizophyllum commune acting on polymeric xylan. J. Biotechnol. 78, 149–161 (2000).

    CAS  PubMed  Google Scholar 

  51. 51.

    McClure, R. et al. Computational analysis of bacterial RNA-Seq data. Nucleic Acids Res. 41, e140 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52.

    Unfried, F. et al. Adaptive mechanisms that provide competitive advantages to marine bacteroidetes during microalgal blooms. ISME J. 12, 2894–2906 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Basan, M. et al. Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528, 99–104 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Shachrai, I., Zaslaver, A., Alon, U. & Dekel, E. Cost of unneeded proteins in E. coli is reduced after several generations in exponential growth. Mol. Cell 38, 758–767 (2010).

    CAS  PubMed  Google Scholar 

  55. 55.

    Axen, S. D., Erbilgin, O. & Kerfeld, C. A. A taxonomy of bacterial microcompartment loci constructed by a novel scoring method. PLoS Comput. Biol. 10, e1003898 (2014).

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    He, S. et al. Ecophysiology of freshwater Verrucomicrobia inferred from metagenome-assembled genomes. mSphere 2, e00277-17 (2017).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Erbilgin, O., McDonald, K. L. & Kerfeld, C. A. Characterization of a planctomycetal organelle: a novel bacterial microcompartment for the aerobic degradation of plant saccharides. Appl. Environ. Microbiol. 80, 2193–2205 (2014).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Petit, E. et al. Involvement of a bacterial microcompartment in the metabolism of fucose and rhamnose by Clostridium phytofermentans. PLoS ONE 8, e54337 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Baldomà, L. & Aguilar, J. Metabolism of l-fucose and l-rhamnose in Escherichia coli: aerobic–anaerobic regulation of l-lactaldehyde dissimilation. J. Bacteriol. 170, 416–421 (1988).

    PubMed  PubMed Central  Google Scholar 

  60. 60.

    Freitas, S. et al. Global distribution and diversity of marine verrucomicrobia. ISME J. 6, 1499–1505 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Needham, D. M. et al. Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling. ISME J. 12, 2417–2432 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Bachmann, J. et al. Environmental drivers of free-living vs. particle-attached bacterial community composition in the Mauritania upwelling system. Front. Microbiol. 9, 2836 (2018).

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 12613590 (2015).

    PubMed  Google Scholar 

  64. 64.

    Kopf, A. et al. The ocean sampling day consortium. Gigascience 4, 27 (2015).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Desai, M. S. et al. A dietary fiber-deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility. Cell 167, 1339–1353.e21 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Tegtmeier, D., Belitz, A., Radek, R., Heimerl, T. & Brune, A. Ereboglobus luteus gen. nov. sp. nov. from cockroach guts, and new insights into the oxygen relationship of the genera Opitutus and Didymococcus (Verrucomicrobia: Opitutaceae). Syst. Appl. Microbiol. 41, 101–112 (2018).

    PubMed  Google Scholar 

  67. 67.

    Mavromatis, K. et al. Complete genome sequence of Coraliomargarita akajimensis type strain (04OKA010-24). Stand. Genomic Sci. 2, 290–299 (2010).

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Kotak, M. et al. Complete genome sequence of the opitutaceae bacterium strain TAV5, a potential facultative methylotroph of the wood-feeding termite Reticulitermes flavipes. Genome Announc. 3, e00060–15 (2015).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Barbeyron, T. et al. Habitat and taxon as driving forces of carbohydrate catabolism in marine heterotrophic bacteria: example of the model algae-associated bacterium Zobellia galactanivorans DsijT. Environ. Microbiol. 18, 4610–4627 (2016).

    CAS  PubMed  Google Scholar 

  70. 70.

    Hehemann, J.-H. et al. Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes. Nat. Commun. 7, 12860 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Razeq, F. M. et al. A novel acetyl xylan esterase enabling complete deacetylation of substituted xylans. Biotechnol. Biofuels 11, 74 (2018).

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Zhou, J., Mopper, K., Passow, U. & Zhoul, J. The role of surface-active carbohydrates in the formation of transparent exopolymer of seawater particles by bubble adsorption. Limnology 43, 1860–1871 (2011).

    Google Scholar 

  73. 73.

    Engel, A., Thoms, S., Riebesell, U., Rochelle-Newall, E. & Zondervan, I. Polysaccharide aggregation as a potential sink of marine dissolved organic carbon. Nature 428, 929–932 (2004).

    CAS  PubMed  Google Scholar 

  74. 74.

    Koch, H. et al. Biphasic cellular adaptations and ecological implications of Alteromonas macleodii degrading a mixture of algal polysaccharides. ISME J. 13, 92–103 (2019).

    CAS  PubMed  Google Scholar 

  75. 75.

    Enke, T. N., Leventhal, G. E., Metzger, M., Saavedra, J. T. & Cordero, O. X. Microscale ecology regulates particulate organic matter turnover in model marine microbial communities. Nat. Commun. 9, 2743 (2018).

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Tibbles, B. J. & Rawlings, D. E. Characterization of nitrogen-fixing bacteria from a temperate saltmarsh lagoon, including isolates that produce ethane from acetylene. Microb. Ecol. 27, 65–80 (1994).

    CAS  PubMed  Google Scholar 

  77. 77.

    Diepenbroek, M. et al. in Informatik 2014 (eds Plödereder, E. et al.) 1711–1721 (Gesellschaft für Informatik, 2014).

  78. 78.

    Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol. 29, 415–420 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Harrison, P. W. et al. The European Nucleotide Archive in 2018. Nucleic Acids Res. 47, D84–D88 (2019).

    CAS  PubMed  Google Scholar 

  80. 80.

    Galperin, M. Y., Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res. 43, D261–D269 (2015).

    CAS  PubMed  Google Scholar 

  81. 81.

    El-Gebali, S. et al. The Pfam protein families database in 2019. Nucleic Acids Res. 47, D427–D432 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Overbeek, R. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res. 33, 5691–5702 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Darling, A. C. E. Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 14, 1394–1403 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Richter, M., Rosselló-Móra, R., Oliver Glöckner, F. & Peplies, J. JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison. Bioinformatics 32, 929–931 (2016).

    CAS  PubMed  Google Scholar 

  85. 85.

    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    CAS  PubMed  Google Scholar 

  86. 86.

    Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Zhang, H. et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 46, W95–W101 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Heinz, E. et al. The genome of the obligate intracellular parasite Trachipleistophora hominis: new insights into microsporidian genome dynamics and reductive evolution. PLoS Pathog. 8, e1002979 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Otto, A. et al. Systems-wide temporal proteomic profiling in glucose-starved Bacillus subtilis. Nat. Commun. 1, 137 (2010).

    PubMed  PubMed Central  Google Scholar 

  91. 91.

    Tyanova, S., Temu, T. & Cox, J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat. Protoc. 11, 2301–2319 (2016).

    CAS  PubMed  Google Scholar 

  92. 92.

    Shin, J. B. et al. Molecular architecture of the chick vestibular hair bundle. Nat. Neurosci. 16, 365–374 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Bo, T. H., Dysvik, B. & Jonassen, I. LSimpute: accurate estimation of missing values in microarray data with least squares methods. Nucleic Acids Res. 32, e34 (2004).

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Kammers, K., Cole, R. N., Tiengwe, C. & Ruczinski, I. Detecting significant changes in protein abundance. EuPA Open Proteom. 7, 11–19 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq-A python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  Google Scholar 

  97. 97.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).

    PubMed  PubMed Central  Google Scholar 

  98. 98.

    Ritchie, M. E. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  99. 99.

    Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. A. & Smith, F. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28, 350–356 (1956).

    CAS  Google Scholar 

  100. 100.

    Engel, A. & Händel, N. A novel protocol for determining the concentration and composition of sugars in particulate and in high molecular weight dissolved organic matter (HMW-DOM) in seawater. Mar. Chem. 127, 180–191 (2011).

    CAS  Google Scholar 

  101. 101.

    Sogin, E. M., Puskás, E., Dubilier, N. & Liebeke, M. Marine metabolomics: a method for nontargeted measurement of metabolites in seawater by gas chromatography–mass spectrometry. mSystems 4, e00638-19 (2019).

    PubMed  PubMed Central  Google Scholar 

  102. 102.

    Pruesse, E., Peplies, J. & Glöckner, F. O. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28, 1823–1829 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. 104.

    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).

    Google Scholar 

  105. 105.

    Steinegger, M., Mirdita, M. & Söding, J. Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold. Nat. Methods 16, 603–606 (2019).

    CAS  PubMed  Google Scholar 

  106. 106.

    Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).

    CAS  PubMed  Google Scholar 

  107. 107.

    Silberfeld, T. et al. A multi-locus time-calibrated phylogeny of the brown algae (Heterokonta, Ochrophyta, Phaeophyceae): investigating the evolutionary nature of the ‘brown algal crown radiation’. Mol. Phylogenet. Evol. 56, 659–674 (2010).

    CAS  PubMed  Google Scholar 

  108. 108.

    Nagaoka, M. et al. Structural study of fucoidan from Cladosiphon okamuranus TOKIDA. Glycoconj. J. 16, 19–26 (1999).

    CAS  PubMed  Google Scholar 

  109. 109.

    Hemmingson, J. A., Falshaw, R., Furneaux, R. H. & Thompson, K. Structure and antiviral activity of the galactofucan sulfates extracted from Undaria pinnatifida (Phaeophyta). J. Appl. Phycol. 18, 185–193 (2006).

    CAS  Google Scholar 

  110. 110.

    Nishino, T., Nagumo, T., Kiyohara, H. & Yamada, H. Structural characterization of a new anticoagulant fucan sulfate from the brown seaweed Ecklonia kurome. Carbohydr. Res. 211, 77–90 (1991).

    CAS  PubMed  Google Scholar 

Download references


We thank K. Caspersen for help with metabolite measurements and N. Dubilier for providing access to mass spectrometry resources. We thank T. Ferdelman and K. Imhoff for expertise in sulfate quantification, M.-K. Zühlke for submission of the proteome data to PRIDE, I. Kostadinov of German Federation for Biological Data for sequence data deposition and B. Hüttel from the Max Planck-Genome-Centre Cologne for assistance with genome sequencing and assembly. A.S. and M.S.S. are members of the International Max Planck Research School of Marine Microbiology (MarMic). J.H.H. acknowledges funding from the Max-Planck-Gesellschaft and from the Emmy Noether Program of the DFG (grant no. HE 7217/1-1). M.F.P. acknowledges support from the US Department of Energy (DE-SC0008743). We thank A. Bolte for the HPAEC-PAD measurements and acknowledge support from the HGF-POSY project. The work of S.M., T.S. and D.B. was financially supported by grants (nos. BE 3869/4-1 and SCHW 595/10-1) of the DFG in the framework of the research unit FOR 2406 ‘Proteogenomics of Marine Polysaccharide Utilization’ (POMPU). F.U. was supported by a scholarship from the Institute of Marine Biotechnology.

Author information




J.-H.H. and M.F.P. initiated the study and directed the project. C.H.C. and A.S. conducted the experiments. C.H.C. isolated the strains. F.U., S.M. and T.S. conducted the proteome analysis for which D.B. provided resources. M.S.S. and A.F.-G. analysed the OSD and Tara Oceans. M.L. conducted the gas chromatography–mass spectrometry measurements and provided metabolomics resources. A.S., J.-H.H. and M.F.P. prepared the manuscript and received input from all authors.

Corresponding author

Correspondence to Jan-Hendrik Hehemann.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Genome comparison of isolated ‘Lentimonas’ strains including the closest reference genome of Coraliomargarita akajimensis DSM 45221.

Contigs of Illumina sequenced genomes (LCC4 to LCC19) were aligned to the PacBio genome of LCC4. The heatmap shows their average nucleotide identity based on Blast (ANIb). The text inside each cell shows the ANIb and in brackets, the percentage of sequences that could be aligned between contigs.

Extended Data Fig. 2 Genome overview showing transcript levels and relative protein abundances of identified PULs in ‘Lentimonas’ sp. CC4.

The outermost ring shows the genes and the text label their annotation. The next five rings from outside to inside show the protein abundance as a heatmap of riBAQ z-scores during growth on galactose, ι-carrageenan, fucose, fucoidan from Cladosiphon okamuranus and Fucus vesiculosus. The three innermost rings show a heatmap of log2-transformed transcript levels (in TPM) during growth on mannose, ι-carrageenan and fucoidan from F. vesiculosus.

Extended Data Fig. 3 Depolymerization of fucoidans and ι-carrageenan by ‘Lentimonas’ sp. CC4.

a, Separation of charged polysaccharides via C-PAGE to monitor the change of molecular weight. Gel images show the first and last time point of ‘Lentimonas’ sp. CC4 during growth on fucoidans and ι-carrageenan. b, HPAEC-PAD chromatogram using the PA100 column to profile production of oligosaccharides during growth on different substrates. Cultures were grown in triplicates and representative chromatograms and gel images are shown. Chromatograms are color coded according to lag (blue), exponential (organge, green, red, purple) and stationary (brown) growth phase.

Extended Data Fig. 4 Comparison between RNAseq and proteome analysis.

a, Gene per gene comparison of transcripts per million (TPM) between ‘Lentimonas’ sp. CC4 and CC6 under the same growth condition. The graph shows a hexbin plot, in which adjacent datapoints are binned into hexagons and the number of data points per hexbin are represented by the colored gradient. In total, the transcription of 4005 genes across eight conditions (n = 32040) was compared. b, Gene per gene expression of CAZymes and sulfatases that are significantly upregulated comparing CC4 growing on fucoidan from F. vesiculosus versus ι-carrageenan in proteomics and transcriptomics. The data points are colored by a kernel density estimation using a normal distribution of the binary logarithm of the TPM and riBAQ values. In total, the expression of 128 genes was compared (n = 128).

Extended Data Fig. 5 Phylogenetic tree of GH29 enzymes from ‘Lentimonas’ sp. CC4.

Colored boxes along the tree branches comprise proteins over 50% identity and colored boxed around leaves denote their gene regulation according to their membership in a cluster of co-regulated enzymes (Fig. 3a). Node labels indicate the bootstrap support value and as outgroup, we used a GH107 enzyme which display structural similarities to GH29 enzymes24.

Extended Data Fig. 6 CAZyme and sulfatase content of operons of ‘Lentimonas’ sp. CC4.

The heatmap shows the copy number of each enzyme family per operon. Rows and columns are arranged according to hierarchical clustering using the ‘cityblock’ distance metric. Only operons with at least one differentially expressed enzyme are shown.

Extended Data Fig. 7 Quantification of cross-reactivity by batch enzyme extracts.

a, Release of reducing ends by batch enzyme extracts extracted from ‘Lentimonas’ sp. CC4 on different substrates. Rows represent substrates that were used to grow ‘Lentimonas’ sp. CC4 prior to enzyme extraction and columns represent substrates used for the enzyme reaction. Colored data points represent the reducing ends measured via the PAHBAH assay over time and the colored line denotes a fit of the data to a saturation curve. b, Left, the rate of released reducing ends after 30 minutes of the reaction in the linear part of the reaction. Right, the rate of released reducing ends normalized to the maximum rate per substrate. The height of the bar denotes the mean and the error bar the standard deviation from biologically independent experiments (n ≥ 3).

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1–7.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sichert, A., Corzett, C.H., Schechter, M.S. et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat Microbiol 5, 1026–1039 (2020).

Download citation

Further reading


Quick links