Biomass Conversion pp 141-151

Part of the Methods in Molecular Biology book series (MIMB, volume 908)

Bioprospecting Metagenomics for New Glycoside Hydrolases

  • Jack Gilbert
  • Luen-Luen Li
  • Safiyh Taghavi
  • Sean M. McCorkle
  • Susannah Tringe
  • Daniel van der Lelie
Protocol

Abstract

To efficiently deconstruct recalcitrant plant biomass to fermentable sugars in industrial processes, biocatalysts of higher performance and lower cost are required. The genetic diversity found in the metagenomes of natural microbial biomass decay communities may harbor such enzymes. The aim of this chapter is to describe strategies, based on metagenomic approaches, for the discovery of glycoside hydrolases (GHases) from microbial biomass decay communities, especially those from unknown or never-been-cultivated microorganisms.

Key words

Metagenomics Glycoside hydrolase Cellulase Biomass decay community 16S rRNA 

References

  1. 1.
    Himmel ME (ed) (2008) Biomass recalcitrance - deconstructing the plant cell wall for bioenergy. Blackwell, LondonGoogle Scholar
  2. 2.
    Himmel ME (ed) et al (1997) Fuels and chemicals from biomass. American Chemical Society Symposium Series, American Chemical Society, Washington, DCGoogle Scholar
  3. 3.
    Bayer EA, Shimon LJ et al (1998) Cellulosomes-structure and ultrastructure. J Struct Biol 124:221–234CrossRefGoogle Scholar
  4. 4.
    Bayer EA, Belaich J-P et al (2004) The cellulosomes: multienzyme machines for degradation of plant cell wall polysaccharides. Annu Rev Microbiol 58:521–554CrossRefGoogle Scholar
  5. 5.
    Ferrer M, Golyshina OV et al (2005) Novel hydrolase diversity retrieved from a metagenome library of bovine rumen microflora. Environ Microbiol 7:1966–2010CrossRefGoogle Scholar
  6. 6.
    Feng Y, Duan C et al (2007) Cloning and identification of novel cellulase genes from uncultured microorganisms in rabbit cecum and characterization of the expressed cellulases. Appl Microbiol Biotechnol 75:319–328CrossRefGoogle Scholar
  7. 7.
    Flint HJ, Bayer EA et al (2008) Polysaccharide utilization by gut bacteria: potential for new insights from genomic analysis. Nat Rev Microbiol 6:121–131CrossRefGoogle Scholar
  8. 8.
    Singh B, Gautam SK et al (2008) Metagenomics in animal gastrointestinal ecosystem: potential biotechnological prospects. Anaerobe 14:138–144CrossRefGoogle Scholar
  9. 9.
    Warnecke F, Luginbuhl P et al (2007) Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature 450:560–565CrossRefGoogle Scholar
  10. 10.
    Ohkuma M (2008) Symbioses of flagellates and prokaryotes in the gut of lower termites. Trends Microbiol 16:345–352CrossRefGoogle Scholar
  11. 11.
    Scharf ME, Tartar A (2008) Termite digestomes as sources for novel lignocellulases. Biofuels, Bioprod Bioref 2:540–552CrossRefGoogle Scholar
  12. 12.
    Zachary A, Colwell RR (1979) Gut-associated microflora of Limnoria tripunctata in marine creosote-treated wood pilings. Nature 282:716–717CrossRefGoogle Scholar
  13. 13.
    Yu H, Zeng G et al (2007) Microbial community succession and lignocellulose degradation during agricultural waste composting. Biodegradation 18:793–802CrossRefGoogle Scholar
  14. 14.
    Schluter A, Bekel T et al (2008) The metagenome of a biogas-producing microbial community of a production-scale biogas plant fermenter analysed by the 454-pyrosequencing technology. J Biotechnol 136:77–90CrossRefGoogle Scholar
  15. 15.
    Allgaier M, Reddy A et al (2010) Targeted discovery of glycoside hydrolases from a switchgrass-adapted compost community. PLoS ONE 5:e8812CrossRefGoogle Scholar
  16. 16.
    Whitman WB, Coleman DC et al (1998) Prokaryotes: the unseen majority. Proc Natl Acad Sci U S A 95:6578–6583CrossRefGoogle Scholar
  17. 17.
    Amann RJ, Binder BL et al (1990) Combination of 16S rRNA targeted oligonucleotide probes with flow-cemetry for analysing mixed microbial populations. Appl Environ Microbiol 56:1910–1925Google Scholar
  18. 18.
    Tyson GW, Chapman J et al (2004) Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428:37–43CrossRefGoogle Scholar
  19. 19.
    Daniel R (2005) The metagenomics of soil. Nat Rev Microbiol 3:470–478CrossRefGoogle Scholar
  20. 20.
    Lorenz P, Eck J (2005) Metagenomics and industrial applications. Nature 3:510–516Google Scholar
  21. 21.
    Li LL, McCorkle S et al (2009) Bioprospecting metagenomes: glycosyl hydrolases for converting biomass. Biotechnol Biofuels 2:10CrossRefGoogle Scholar
  22. 22.
    Lesaulnier C, Papamichail D et al (2008) Elevated atmospheric CO2 affects soil microbial diversity associated with trembling aspen. Environ Microbiol 10:926–941CrossRefGoogle Scholar
  23. 23.
    Costello EK, Lauber CL et al (2009) Bacterial community variation in human body habitats across space and time. Science 326:1694–1697CrossRefGoogle Scholar
  24. 24.
    Foster JS, Green SJ et al (2009) Molecular and morphological characterization of cyanobacterial diversity in the stromatolites of Highborne Cay, Bahamas. ISME J 3:573–587CrossRefGoogle Scholar
  25. 25.
    Caporaso JG, Field D et al. Everything is everywhere: a 19th century hypothesis confirmed with 21st century science. ISME J (Submitted)Google Scholar
  26. 26.
    Gilbert JA, Field D et al (2009) The seasonal structure of microbial communities in the Western English Channel. Environ Microbiol 11:3132–3139CrossRefGoogle Scholar
  27. 27.
    Suen G, Scott JJ et al (2010) An insect herbivore microbiome with high plant biomass-degrading capacity. PLoS Genet 6(9)Google Scholar
  28. 28.
    Gianoulis TA, Raes J et al (2009) Quantifying environmental adaptation of metabolic pathways in metagenomics. Proc Natl Acad Sci U S A 106:1374–1379CrossRefGoogle Scholar
  29. 29.
    Gilbert JA, Field D et al (2010) The taxonomic and functional diversity of microbes at a temperate coastal site: a ‘multi-omic’ study of seasonal and diel temporal variation. PLoS One 5:e15545CrossRefGoogle Scholar
  30. 30.
    Noguchi H, Park J et al (2006) MetaGene: prokaryotic gene finding from environmental genome shotgun sequences. Nucleic Acids Res 34:5623–5630CrossRefGoogle Scholar
  31. 31.
    Henrissat B (1991) A classification of glycosyl hydrolases based on amino acid sequence similarities. Biochem J 280:309–316Google Scholar
  32. 32.
    Claudel-Renard C, Chevalet C, Faraut T, Kahn D (2003) Enzyme-specific profiles for genome annotation: PRIAM. Nucleic Acids Res 31:6633–6639CrossRefGoogle Scholar
  33. 33.
    Bateman A, Coin L, Durbin R, Finn RD, Hollich V, Griffiths-Jones S, Khanna A, Marshall M, Moxon S, Sonnhammer EL, Studholme DJ, Yeats C, Eddy SR (2004) The Pfam protein families database. Nucleic Acids Res 32(Database issue):D138–D141CrossRefGoogle Scholar
  34. 34.
    Rost B, Yachdav G, Liu J (2004) The PredictProtein Server. Nucleic Acids Res 32(Web Server issue):W321–W326CrossRefGoogle Scholar
  35. 35.
    Selengut JD, Haft DH, Davidsen T, Ganapathy A, Gwinn-Giglio M, Nelson WC, Richter AR, White O (2007) TIGRFAMs and Genome Properties: tools for the assignment of molecular function and biological process in prokaryotic genomes. Nucleic Acids Res 35(Database issue):D260–D264CrossRefGoogle Scholar
  36. 36.
    Cantarel BL, Coutinho PM et al (2009) The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res 37(suppl 1):D233–238CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Jack Gilbert
    • 1
  • Luen-Luen Li
    • 2
  • Safiyh Taghavi
    • 3
  • Sean M. McCorkle
    • 2
  • Susannah Tringe
    • 4
  • Daniel van der Lelie
    • 3
  1. 1.Department of Ecology & EvolutionUniversity of ChicagoChicagoUSA
  2. 2.Biology DepartmentBrookhaven National LaboratoryUptonUSA
  3. 3.Center for Agriculture and Environmental BiotechnologyRTI InternationalResearch Triangle ParkUSA
  4. 4.DOE Joint Genome InstituteWalnut CreekUSA

Personalised recommendations