Metagenomic discovery of biomass-degrading genes and genomes from cow rumen
Abstract
The paucity of enzymes that efficiently deconstruct plant polysaccharides represents a major bottleneck for industrial-scale conversion of cellulosic biomass into biofuels. Cow rumen microbes specialize in degradation of cellulosic plant material, but most members of this complex community resist cultivation. To characterize biomass-degrading genes and genomes, we sequenced and analyzed 268 gigabases of metagenomic DNA from microbes adherent to plant fiber incubated in cow rumen. From these data, we identified 27,755 putative carbohydrate-active genes and expressed 90 candidate proteins, of which 57% were enzymatically active against cellulosic substrates. We also assembled 15 uncultured microbial genomes, which were validated by complementary methods including single-cell genome sequencing. These data sets provide a substantially expanded catalog of genes and genomes participating in the deconstruction of cellulosic biomass.
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Shotgun metagenomics was first applied to the rumen to discover novel biomass degrading enzymes from switchgrass-associated microbes.
“Shotgun metagenomics was first applied to the rumen in order to discover novel biomass degrading enzymes from switchgrass-associated microbes (Hess et al., 2011).”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Hess et al., 2011
Hess et al. (2011) were the first to apply assembly binning to ruminants, assembling 15 draft microbial genomes from the switchgrass associated microbiome of cattle.
“Hess et al. (2011) were the first to apply this to ruminants, assembling 15 draft microbial genomes from the switchgrass associated microbiome of cattle.”
From Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future (2018) · cites this paper as Hess et al., 2011
Cites
The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics
Cantarel, B. L., Coutinho, P. M., Rancurel, C., Bernard, T., Lombard, V., & Henrissat, B. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn663 · Cited by 5,947 · see citing works
Accurate whole human genome sequencing using reversible terminator chemistry
Bentley, D. R., Balasubramanian, S., Swerdlow, H. P., Smith, G. P., Milton, J., Brown, C. G., Hall, K. P., Evers, D. J., Barnes, C. L., Bignell, H. R., et al. (2008) · Nature · doi:10.1038/nature07517 · Cited by 3,684 · see citing works
Self-citationMetagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite
Warnecke, F., Luginbühl, P., Ivanova, N., Ghassemian, M., Richardson, T. H., Stege, J. T., Cayouette, M., McHardy, A. C., Djordjevic, G., Aboushadi, N., et al. (2007) · Nature · doi:10.1038/nature06269 · Cited by 1,351 · see citing works
Self-citationMedini, D., Donati, C., Tettelin, H., Masignani, V., & Rappuoli, R. (2005) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2005.09.006 · Cited by 1,307 · see citing works
Genomics of cellulosic biofuels
Rubin, E. M. (2008) · Nature · doi:10.1038/nature07190 · Cited by 1,223 · see citing works
Self-citationBrulc, J. M., Antonopoulos, D. A., Miller, M. E. B., Wilson, M. K., Yannarell, A. C., Dinsdale, E. A., Edwards, R. E., Frank, E. D., Emerson, J., Wacklin, P., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0806191105 · Cited by 757 · see citing works
Metagenomic analysis of two enhanced biological phosphorus removal (EBPR) sludge communities
Martín, H. G., Ivanova, N., Kunin, V., Warnecke, F., Barry, K., McHardy, A. C., Yeates, C., He, S., Salamov, A. A., Szeto, E., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1247 · Cited by 724 · see citing works
Short pyrosequencing reads suffice for accurate microbial community analysis
Liu, Z., Lozupone, C., Hamady, M., Bushman, F. D., & Knight, R. D. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm541 · Cited by 692 · see citing works
Opportunities to improve fiber degradation in the rumen: microbiology, ecology, and genomics
Krause, D. O., Denman, S. E., Mackie, R. I., Morrison, M., Rae, A. L., Attwood, G. T., & McSweeney, C. S. (2003) · FEMS Microbiology Reviews · doi:10.1016/s0168-6445(03)00072-x · Cited by 515 · see citing works
Self-citationRegenerating cellulose from ionic liquids for an accelerated enzymatic hydrolysis
Zhao, H., Jones, C. L., Baker, G. A., Xia, S., Olubajo, O., & Person, V. N. (2009) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2008.08.009 · Cited by 492 · see citing works
Symbiosis insights through metagenomic analysis of a microbial consortium
Woyke, T., Teeling, H., Ivanova, N., Huntemann, M., Richter, M., Glöckner, F. O., Boffelli, D., Anderson, I., Barry, K., Shapiro, H., et al. (2006) · Nature · doi:10.1038/nature05192 · Cited by 450 · see citing works
Self-citationApplication of tetranucleotide frequencies for the assignment of genomic fragments
Teeling, H., Meyerdierks, A., Bauer, M., Amann, R., & Glöckner, F. O. (2004) · Environmental Microbiology · doi:10.1111/j.1462-2920.2004.00624.x · Cited by 386 · see citing works
Estimating the size of the bacterial pan-genome
Lapierre, P., & Gogarten, J. P. (2009) · Trends in Genetics · doi:10.1016/j.tig.2008.12.004 · Cited by 354 · see citing works
Functional metagenomics for enzyme discovery: challenges to efficient screening
Uchiyama, T., & Miyazaki, K. (2009) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2009.09.010 · Cited by 348 · see citing works
Assembling the Marine Metagenome, One Cell at a Time
Woyke, T., Xie, G., Copeland, A., González, J. M., Han, C., Kiss, H., Saw, J. H. W., Senin, P., Yang, C., Chatterji, S., et al. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005299 · Cited by 335 · see citing works
Self-citationNovel hydrolase diversity retrieved from a metagenome library of bovine rumen microflora
Ferrer, M., Golyshina, O. V., Chernikova, T. N., Khachane, A. N., Reyes‐Duarte, D., dos Santos, V. A. P. M., Strömpl, C., Elborough, K. M., Jarvis, G. N., Neef, A., et al. (2005) · Environmental Microbiology · doi:10.1111/j.1462-2920.2005.00920.x · Cited by 288 · see citing works
Matching phylogeny and metabolism in the uncultured marine bacteria, one cell at a time
Stepanauskas, R., & Sieracki, M. E. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700496104 · Cited by 285 · see citing works
Regulation of plant biomass production
Demura, T., & Ye, Z. (2010) · Current Opinion in Plant Biology · doi:10.1016/j.pbi.2010.03.002 · Cited by 242 · see citing works
Pope, P. B., Denman, S. E., Jones, M., Tringe, S. G., Barry, K., Malfatti, S., McHardy, A. C., Cheng, J., Hugenholtz, P., McSweeney, C. S., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1005297107 · Cited by 218 · see citing works
Self-citationWhy Don’t Ruminal Bacteria Digest Cellulose Faster?
Weimer, P. J. (1996) · Journal of Dairy Science · doi:10.3168/jds.s0022-0302(96)76509-8 · Cited by 206 · see citing works
Bioprospecting metagenomes: glycosyl hydrolases for converting biomass
Li, L., McCorkle, S., Monchy, S., Taghavi, S., & van der Lelie, D. (2009) · Biotechnology for Biofuels · doi:10.1186/1754-6834-2-10 · Cited by 171 · see citing works
Protein engineering in designing tailored enzymes and microorganisms for biofuels production
Wen, F., Nair, N. U., & Zhao, H. (2009) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2009.07.001 · Cited by 122 · see citing works
Fast enzymatic saccharification of switchgrass after pretreatment with ionic liquids
Zhao, H., Baker, G. A., & Cowins, J. V. (2009) · Biotechnology Progress · doi:10.1002/btpr.331 · Cited by 110 · see citing works
Sanderson, K. (2006) · Nature · doi:10.1038/444673a · Cited by 103 · see citing works
Kelly, W. J., Leahy, S. C., Altermann, E., Yeoman, C. J., Dunne, J. C., Kong, Z., Pacheco, D. M., Li, D., Noel, S. J., Moon, C. D., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0011942 · Cited by 99 · see citing works
Microbiological evaluation of the intraruminal in sacculus digestion technique
Meyer, J. H., & Mackie, R. I. (1986) · Applied and Environmental Microbiology · doi:10.1128/aem.51.3.622-629.1986 · Cited by 94 · see citing works
Self-citationWang, F., Li, F., Chen, G., et al. (2009) · Microbiological Research · doi:10.1016/j.micres.2008.12.002 · Cited by 89 · see citing works
Rincón, M. T., Dassa, B., Flint, H. J., Travis, A. J., Jindou, S., Borovok, I., Lamed, R., Bayer, E. A., Henrissat, B., Coutinho, P. M., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012476 · Cited by 78 · see citing works
Addressing the Need for Alternative Transportation Fuels: The Joint BioEnergy Institute
Blanch, H. W., Adams, P. D., Andrews-Cramer, K. M., et al. (2008) · ACS Chemical Biology · doi:10.1021/cb700267s · Cited by 45 · see citing works
Selecting, Establishing, and Managing Switchgrass (Panicum virgatum) for Biofuels
Parrish, D. J., & Fike, J. H. (2009) · Methods in molecular biology · doi:10.1007/978-1-60761-214-8_2 · Cited by 38 · see citing works
Flagged first, then by citations
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metaSPAdes: a new versatile metagenomic assembler
Nurk, S., Meleshko, D., Korobeynikov, A., & Pevzner, P. A. (2017) · Genome Research · doi:10.1101/gr.213959.116 · Cited by 4,496 · see citing works
Gut metagenome in European women with normal, impaired and diabetic glucose control
Karlsson, F. H., Tremaroli, V., Nookaew, I., Bergström, G., Behre, C. J., Fagerberg, B., Nielsen, J., & Bäckhed, F. (2013) · Nature · doi:10.1038/nature12198 · Cited by 3,115 · see citing works
Microbial degradation of complex carbohydrates in the gut
Flint, H. J., Scott, K. P., Duncan, S. H., Louis, P., & Forano, E. (2012) · Gut Microbes · doi:10.4161/gmic.19897 · Cited by 2,331 · see citing works
Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants.
Shabat, S. K. B., Sasson, G., Doron-Faigenboim, A., Durman, T., Yaacoby, S., Berg Miller, M. E., White, B. A., Shterzer, N., & Mizrahi, I. (2016) · The ISME Journal · doi:10.1038/ismej.2016.62 · Cited by 823 · see citing works
Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection
Hungate1000 project collaborators, Seshadri, R., Leahy, S. C., Attwood, G. T., Teh, K. H., Lambie, S. C., Cookson, A. L., Eloe-Fadrosh, E. A., Pavlopoulos, G. A., Hadjithomas, M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4110 · Cited by 624 · see citing works
Self-citationAssembly of 913 microbial genomes from metagenomic sequencing of the cow rumen
Stewart, R. D., Auffret, M. D., Warr, A., Wiser, A. H., Press, M. O., Langford, K. W., Liachko, I., Snelling, T. J., Dewhurst, R. J., Walker, A. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03317-6 · Cited by 559 · see citing works
The Human Microbiome Project: A Community Resource for the Healthy Human Microbiome
Gevers, D., Knight, R., Petrosino, J. F., Huang, K., McGuire, A. L., Birren, B. W., Nelson, K. E., White, O., Methé, B. A., & Huttenhower, C. (2012) · PLoS Biology · doi:10.1371/journal.pbio.1001377 · Cited by 493 · see citing works
Huws, S. A., Creevey, C. J., Oyama, L. B., Mizrahi, I., Denman, S. E., Popova, M., Muñoz-Tamayo, R., Forano, E., Waters, S. M., Hess, M., et al. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.02161 · Cited by 487 · see citing works
Self-citationThe rumen microbial metagenome associated with high methane production in cattle
Wallace, R. J., Rooke, J. A., McKain, N., Duthie, C.-A., Hyslop, J. J., Ross, D. W., Waterhouse, A., Watson, M., & Roehe, R. (2015) · BMC Genomics · doi:10.1186/s12864-015-2032-0 · Cited by 416 · see citing works
Methane yield phenotypes linked to differential gene expression in the sheep rumen microbiome
Shi, W., Moon, C. D., Leahy, S. C., Kang, D., Froula, J., Kittelmann, S., Fan, C., Deutsch, S., Gagic, D., Seedorf, H., et al. (2014) · Genome Research · doi:10.1101/gr.168245.113 · Cited by 414 · see citing works
Self-citationComplex Carbohydrate Utilization by the Healthy Human Microbiome
Cantarel, B. L., Lombard, V., & Henrissat, B. (2012) · PLoS ONE · doi:10.1371/journal.pone.0028742 · Cited by 378 · see citing works
Early-branching gut fungi possess a large, comprehensive array of biomass-degrading enzymes
Solomon, K. V., Haitjema, C. H., Henske, J. K., Gilmore, S. P., Borges-Rivera, D., Lipzen, A., Brewer, H. M., Purvine, S. O., Wright, A. T., Theodorou, M. K., et al. (2016) · Science · doi:10.1126/science.aad1431 · Cited by 302 · see citing works
Salehi Jouzani, G., & Taherzadeh, M. J. (2015) · Biofuel Research Journal · doi:10.18331/brj2015.2.1.4 · Cited by 224 · see citing works
A Review of bioinformatics tools for bio-prospecting from metagenomic sequence data
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McAllister, T. A., Dunière, L., Drouin, P., Xu, S., Wang, Y., Munns, K., & Zaheer, R. (2018) · Journal of Dairy Science · doi:10.3168/jds.2017-13704 · Cited by 162 · see citing works
Svartström, O., Alneberg, J., Terrapon, N., Lombard, V., de Bruijn, I., Malmsten, J., Dalin, A.-M., EL Muller, E., Shah, P., Wilmes, P., et al. (2017) · The ISME Journal · doi:10.1038/ismej.2017.108 · Cited by 161 · see citing works
CowPI: a rumen microbiome focussed version of the PICRUSt functional inference software
Wilkinson, T. J., Huws, S. A., Edwards, J. E., Kingston-Smith, A. H., Siu-Ting, K., Hughes, M., Rubino, F., Friedersdorff, M., & Creevey, C. J. (2018) · Frontiers in Microbiology · doi:10.3389/fmicb.2018.01095 · Cited by 160 · see citing works
New aspects and strategies for methane mitigation from ruminants
Kumar, S., Choudhury, P. K., Carro, M. D., Griffith, G. W., Dagar, S. S., Puniya, M., Calabro, S., Ravella, S. R., Dhewa, T., Upadhyay, R. C., et al. (2014) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-013-5365-0 · Cited by 152 · see citing works
The bovine ruminal fluid metabolome
Saleem, F., Bouatra, S., Guo, A. C., Psychogios, N., Mandal, R., Dunn, S. M., Ametaj, B. N., & Wishart, D. S. (2013) · Metabolomics · doi:10.1007/s11306-012-0458-9 · Cited by 149 · see citing works
Edwards, J. E., Forster, R. J., Callaghan, T. M., Dollhofer, V., Dagar, S. S., Cheng, Y., Chang, J., Kittelmann, S., Fliegerova, K., Puniya, A. K., et al. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01657 · Cited by 130 · see citing works
Oral Samples as non-invasive proxies for assessing the composition of the rumen microbial community
Tapio, I., Shingfield, K. J., McKain, N., Bonin, A., Fischer, D., Bayat, A. R., Vilkki, J., Taberlet, P., Snelling, T. J., & Wallace, R. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0151220 · Cited by 125 · see citing works
Rubino, F., Carberry, C., Waters, S. M., Kenny, D., McCabe, M. S., & Creevey, C. J. (2017) · The ISME Journal · doi:10.1038/ismej.2016.172 · Cited by 101 · see citing works
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Liu, G., Qin, Y., Li, Z., & Qu, Y. (2013) · Biotechnology Advances · doi:10.1016/j.biotechadv.2013.03.001 · Cited by 95 · see citing works
Xie, S., Syrenne, R., Sun, S., & Yuan, J. S. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.02.007 · Cited by 62 · see citing works
Elekwachi, C. O., Wang, Z., Wu, X., Rabee, A., & Forster, R. J. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01814 · Cited by 48 · see citing works
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Belanche, A., Newbold, C. J., Lin, W., Rees Stevens, P., & Kingston-Smith, A. H. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.01456 · Cited by 42 · see citing works
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Kingston-Smith, A. H., Davies, T. E., Rees Stevens, P., & Mur, L. A. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0082801 · Cited by 17 · see citing works
- Authors
- Hess, M., Sczyrba, A., Egan, R., Kim, T.-W., Chokhawala, H., Schroth, G., Luo, S., Clark, D. S., Chen, F., Zhang, T., Mackie, R. I., Pennacchio, L. A., Tringe, S. G., Visel, A., Woyke, T., Wang, Z., & Rubin, E. M.
- Affiliations
- Lawrence Berkeley National Laboratory (US)
- Energy Biosciences Institute (US)
- Illumina (United States) (US)
- University of Illinois Urbana-Champaign (US)
- ORCID
- Venue
- Science
- Published
- 2011-01-28
- Volume
- 331
- Issue
- 6016
- Pages
- 463-467
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W1969346416
- Cited by
- 1,319
- References
- 30
- Citations by year
- 2026: 39 · 2025: 53 · 2024: 53 · 2023: 51 · 2022: 81 · 2021: 78 · 2020: 80 · 2019: 90 · 2018: 79 · 2017: 115 · 2016: 91 · 2015: 107 · 2014: 112 · 2013: 127 · 2012: 110
- FWCI
- 75.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PUB – Publications at Bielefeld University (Bielefeld University) (submittedVersion)
- Funders
- Office of Science (DE-AC02-05CH112)
- U.S. Department of Energy (DE-AC02-05CH11231)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Biofuel production and bioconversion
- Physical Sciences › Engineering › Building and Construction › Anaerobic Digestion and Biogas Production
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- rumen microbiota, carbohydrate-active enzymes, plant polysaccharide degradation, metagenomics, single-cell DNA sequencing
- MeSH terms
- Amino Acid Sequence, Animals, Bacteria, Bacterial Proteins, Cattle, Cellulase, Cellulose, Genes, Bacterial, Poaceae, Molecular Sequence Data, Rumen, Genome, Bacterial, Sequence Analysis, DNA, Biomass, Cellulose 1,4-beta-Cellobiosidase, Cellulases, Carbohydrate Metabolism, Metagenome, Metagenomics, Molecular Sequence Annotation
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