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Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan

  • 1.

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

  • 2.

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

  • 3.

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

  • 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).

  • 5.

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

  • 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).

  • 7.

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

  • 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).

  • 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).

  • 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).

  • 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).

  • 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).

  • 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).

  • 14.

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

  • 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).

  • 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).

  • 17.

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

  • 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).

  • 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).

  • 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).

  • 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).

  • 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).

  • 23.

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

  • 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).

  • 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).

  • 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).

  • 27.

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

  • 28.

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

  • 29.

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

  • 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).

    • Article
    • Google Scholar
  • 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).

  • 32.

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

  • 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).

  • 34.

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

  • 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).

  • 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).

  • 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).

  • 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).

  • 39.

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

  • 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).

  • 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).

  • 42.

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

  • 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).

  • 44.

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

  • 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).

  • 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).

  • 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).

  • 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).

  • 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).

  • 50.

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

  • 51.

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

  • 52.

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

  • 53.

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

  • 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).

  • 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).

  • 56.

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

  • 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).

  • 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).

  • 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).

  • 60.

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

  • 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).

  • 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).

  • 63.

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

  • 64.

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

  • 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).

  • 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).

  • 67.

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

  • 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).

  • 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).

  • 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).

  • 71.

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

  • 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.

    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).

  • 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).

  • 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).

  • 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).

  • 77.

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

  • 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).

  • 79.

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

  • 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).

  • 81.

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

  • 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).

  • 83.

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

  • 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).

  • 85.

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

  • 86.

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

  • 87.

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

  • 88.

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

  • 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).

  • 90.

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

  • 91.

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

  • 92.

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

  • 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).

  • 94.

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

  • 95.

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

  • 96.

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

  • 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).

  • 98.

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

  • 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).

  • 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).

  • 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).

  • 102.

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

  • 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).

  • 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).

  • 105.

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

  • 106.

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

  • 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).

  • 108.

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

  • 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).

  • 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).


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