De novo prediction of the genomic components and capabilities for microbial plant biomass degradation from (meta-)genomes

Biotechnology for Biofuels

Volumn 6, Issue 1, 2013, Pages

  Weimann, Aaron ^a,c   Trukhina, Yulia ^a,c   Pope, Phillip B ^b   Konietzny, Sebastian Ga ^a,c   McHardy, Alice C ^a,c  

^a MAX PLANCK INSTITUTE FOR INFORMATICS   (Germany)

^b NORWEGIAN UNIVERSITY OF LIFE SCIENCES   (Norway)

^c HEINRICH HEINE UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BIOCHEMICAL TESTS; BIOLOGICAL MECHANISMS; CELLULOSE DEGRADATION; DATA SET; GENE FAMILIES; GENES ENCODING; GENOME SEQUENCES; GENOMIC COMPONENTS; GLYCOSIDE HYDROLASES; INCREASING PRODUCTION; METAGENOMES; MICROBIAL SPECIES; PLANT BIOMASS; PLANT POLYSACCHARIDES; PROTEIN DOMAINS;

BACTERIA; BIODEGRADATION; CELLULOSE; ENZYMES; GENES; STATISTICAL TESTS;

BIOMASS;

ACCURACY ASSESSMENT; BIODEGRADATION; BIOMASS; BIOTECHNOLOGY; ENZYME ACTIVITY; GENOME; MICROBIAL ACTIVITY; PHYSIOLOGY; PREDICTION; PROTEIN;

BACTEROIDETES;

EID: 84873702320     PISSN: 17546834     EISSN: None     Source Type: Journal    
DOI: 10.1186/1754-6834-6-24     Document Type: Article

References (45)

1. Genomics of cellulosic biofuels. Rubin EM, Nature 2008 454 841 845 10.1038/nature07190 18704079
2. Economic feasibility of producing ethanol from lignocellulosic feedstocks. Kaylen M, Van Dyne DL, Choi YS, Blasé M, Biores Technol 2000 72 19 32 10.1016/S0960-8524(99)00091-7 (Pubitemid 29492871)
3. Biological conversion of lignocellulosic biomass to ethanol. Lee J, J Biotechnol 1997 56 1 24 10.1016/S0168-1656(97)00073-4 9246788 (Pubitemid 27323661)
4. Fuel ethanol after 25 years. Wheals AE, Basso LC, Alves DMG, Amorim HV, TIBTECH 1999 17 482 487 10.1016/S0167-7799(99)01384-0 (Pubitemid 29525595)
5. Physiology of carbohydrate to solvent conversion by clostridia. Mitchell WJ, Adv Microb Physiol 1998 39 31 130 9328646 (Pubitemid 29077882)
6. Biomass recalcitrance: engineering plants and enzymes for biofuels production. Himmel ME, Ding SY, Johnson DK, Adney WS, Nimlos MR, Brady JW, Foust TD, Science 2007 315 804 807 10.1126/science.1137016 17289988
7. Genome sequence of the cellulolytic gliding bacterium cytophaga hutchinsonii. Xie G, Bruce DC, Challacombe JF, Chertkov O, Detter JC, Gilna P, Han CS, Lucas S, Misra M, Myers GL, et al. Appl Environ Microbiol 2007 73 3536 3546 10.1128/AEM.00225-07 17400776 (Pubitemid 46871442)
8. Functional annotation of fibrobacter succinogenes S85 carbohydrate active enzymes. Brumm P, Mead D, Boyum J, Drinkwater C, Gowda K, Stevenson D, Weimer P, Appl Biochem Biotechnol 2010 10.1007/s12010-010-9070-5
9. Plant biomass degradation by gut microbiomes: more of the same or something new? Morrison M, Pope PB, Denman SE, McSweeney CS, Curr Opin Biotech 2009 20 358 363 10.1016/j.copbio.2009.05.004 19515552
10. Mining Dictyoglomus turgidum for enzymatically active carbohydrases. Brumm P, Hermanson S, Hochstein B, Boyum J, Hermersmann N, Gowda K, Mead D, Appl Biochem Biotechnol 2010 10.1007/s12010-010-9029-6
11. Adaptation to herbivory by the Tammar wallaby includes bacterial and glycoside hydrolase profiles different to other herbivores. Pope PB, Denman SE, Jones M, Tringe SG, Barry K, Malfatti SA, McHardy AC, Cheng J-F, Hugenholtz P, McSweeney CS, Morrison M, Proc Natl Acad Sci USA 2010 107 14793 14798 10.1073/pnas.1005297107 20668243
12. Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Warnecke F, Luginbuhl P, Ivanova N, Ghassemian M, Richardson TH, Stege JT, Cayouette M, McHardy AC, Djordjevic G, Aboushadi N, et al. Nature 2007 450 560 565 10.1038/nature06269 18033299 (Pubitemid 350190243)
13. Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Brulc JM, Antonopoulos DA, Berg Miller ME, Wilson MK, Yannarell AC, Dinsdale EA, Edwards RE, Frank ED, Emerson JB, Wacklin P, et al. Proc Natl Acad Sci USA 1948 2009 106
14. Metagenomic discovery of biomass-degrading genes and genomes from cow rumen. Hess M, Sczyrba A, Egan R, Kim TW, Chokhawala H, Schroth G, Luo S, Clark DS, Chen F, Zhang T, et al. Science 2011 331 463 467 10.1126/science.1200387 21273488
15. Metagenomics of the svalbard reindeer rumen microbiome reveals abundance of polysaccharide utilization loci. Pope PB, Mackenzie AK, Gregor I, Smith W, Sundset MA, McHardy AC, Morrison M, Eijsink VGH, PLoS One 2012 10.1371/journal.pone.0038571
16. Database resources of the National Center for Biotechnology Information. Sayers EW, Barrett T, Benson DA, Bolton E, Bryant SH, Canese K, Chetvernin V, Church DM, Dicuccio M, Federhen S, et al. Nucleic Acids Res 2012 40 13 D25 10.1093/nar/gkr1184 22140104
17. Geno2Pheno: estimating phenotypic drug resistance from HIV-1 genotypes. Beerenwinkel N, Dumer M, Oette M, Korn K, Hoffmann D, Kaiser R, Lengauer T, Selbig J, Walter H, Nucleic Acids Res 2003 31 3850 3855 10.1093/nar/gkg575 12824435 (Pubitemid 37442263)
18. Prediction of phenotype information from genotype data. Yosef N, Gramm J, Wang Q-F, Noble WS, Karp RM, Sharan R, Commun Inf Syst 2010 10 99 114
19. Prediction of carbohydrate-binding proteins from sequences using support vector machines. Someya S, Kakuta M, Morita M, Sumikoshi K, Cao W, Ge Z, Hirose O, Nakamura S, Terada T, Shimizu K, Adv Bioinformatics 2010 10.1155/2010/289301
20. Support-vector networks. Cortes C, Vapnik V, Mach Learn 1995 20 273 297
21. A training algorithm for optimal margin classifiers. Boser B, Guyon I, Vapnik V, Fifth Proceedings of the Fifth Annual Workshop on Computational Learning Theory Pittsburgh: ACM 1992 144 152 (Pubitemid 23615470)
22. Complete genome sequence of Thermomonospora curvata type strain (B9). Chertkov O, Sikorski J, Nolan M, Lapidus A, Lucas S, Del Rio TG, Tice H, Cheng J-F, Goodwin L, Pitluck S, et al. Stand Genomic Sci 2011 4 13 22 10.4056/sigs.1453580 21475583
23. Genomics of aerobic cellulose utilization systems in actinobacteria. Anderson I, Abt B, Lykidis A, Klenk HP, Kyrpides N, Ivanova N, PLoS One 2012 7 39331 10.1371/journal.pone.0039331 22723998
24. Evolution, substrate specificity and subfamily classification of glycoside hydrolase family 5 (GH5). Aspeborg H, Coutinho PM, Wang Y, Brumer H 3rd, Henrissat B, BMC Evol Biol 2012 12 186 10.1186/1471-2148-12-186 22992189
25. Carbohydrate-binding modules: fine-tuning polysaccharide recognition. Boraston AB, Bolam DN, Gilbert HJ, Davies GJ, Biochem J 2004 15 769 781 (Pubitemid 39312891)
26. The complete genome sequence of fibrobacter succinogenes S85 reveals a cellulolytic and metabolic specialist. Suen G, Weimer PJ, Stevenson DM, Aylward FO, Boyum J, Deneke J, Drinkwater C, Ivanova NN, Mikhailova N, Chertkov O, et al. PLoS One 2011 6 18814 10.1371/journal.pone.0018814 21526192
27. SMART: a web-based tool for the study of genetically mobile domains. Schultz J, Copley RR, Doerks T, Ponting CP, Bork P, Nucleic Acids Res 2000 28 231 234 10.1093/nar/28.1.231 10592234 (Pubitemid 30047767)
28. The Pfam protein families database. Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, Pang N, Forslund K, Ceric G, Clements J, et al. Nucleic Acids Res 2012 40 290 D301 10.1093/nar/gkr1065 22127870
29. TIGRFAMs: a protein family resource for the functional identification of proteins. Haft DH, Loftus BJ, Richardson DL, Yang F, Eisen JA, Paulsen IT, White O, Nucleic Acids Res 2001 29 41 43 10.1093/nar/29.1.41 11125044 (Pubitemid 32054402)
30. Three microbial strategies for plant cell wall degradation. Wilson DB, Ann N Y Acad Sci 2008 1125 289 297 10.1196/annals.1419.026 18378599 (Pubitemid 351451168)
31. Deletion of the Cel48S cellulase from Clostridium thermocellum. Olson DG, Tripathi SA, Giannone RJ, Lo J, Caiazza NC, Hogsett DA, Hettich RL, Guss AM, Dubrovsky G, Lynd LR, Proc Natl Acad Sci USA 2010 10.1073/pnas.1003584107
32. Insights into plant cell wall degradation from the genome sequence of the soil bacterium Cellvibrio japonicus. DeBoy RT, Mongodin EF, Fouts DE, Tailford LE, Khouri H, Emerson JB, Mohamoud Y, Watkins K, Henrissat B, Gilbert HJ, Nelson KE, J Bacteriol 2008 190 5455 5463 10.1128/JB.01701-07 18556790
33. Complete cellulase system in the marine bacterium Saccharophagus degradans strain 2-40 T. Taylor LE, Henrissat B, Coutinho PM, Ekborg NA, Hutcheson SW, Weiner RM, J Bacteriol 2006 188 3849 3861 10.1128/JB.01348-05 16707677 (Pubitemid 43825390)
34. Carbohydrate-binding modules promote the enzymatic deconstruction of intact plant cell walls by targeting and proximity effects. Hervé C, Rogowski A, Blake AW, Marcus SE, Gilbert HJ, Knox JP, Proc Natl Acad Sci USA 2010 107 15293 15298 10.1073/pnas.1005732107 20696902
35. Mining metagenomes for novel cellulase genes. Duan CJ, Feng JX, Biotechnol Lett 2010 32 1765 1775 10.1007/s10529-010-0356-z 20640872
36. Evidence for a novel mechanism of microbial cellulose degradation. Wilson DB, Cellulose 2009 16 723 727 10.1007/s10570-009-9326-9
37. Microbial cellulose utilization: fundamentals and biotechnology. Lynd LR, Weimer PJ, van Zyl WH, Pretorius IS, Microbiol Mol Biol Rev 2002 66 506 577 10.1128/MMBR.66.3.506-577.2002 12209002 (Pubitemid 35005827)
38. The carbohydrate-active EnZymes database (CAZy): an expert resource for glycogenomics. Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B, Nucleic Acids Res 2009 37 233 D238 10.1093/nar/gkn663 18838391
39. IMG/M: the integrated metagenome data management and comparative analysis system. Markowitz VM, Chen IM, Chu K, Szeto E, Palaniappan K, Grechkin Y, Ratner A, Jacob B, Pati A, Huntemann M, et al. Nucleic Acids Res 2012 40 123 D129 10.1093/nar/gkr975 22086953
40. IMG: the integrated microbial genomes database and comparative analysis system. Markowitz VM, Chen IM, Palaniappan K, Chu K, Szeto E, Grechkin Y, Ratner A, Jacob B, Huang J, Williams P, et al. Nucleic Acids Res 2012 40 115 D122 10.1093/nar/gkr1044 22194640
41. HMMER web server: interactive sequence similarity searching. Finn RD, Clements J, Eddy SR, Nucleic Acids Res 2011 39 29 W37 10.1093/nar/gkr367 21593126
42. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Yin Y, Mao X, Yang J, Chen X, Mao F, Xu Y, Nucleic Acids Res 2012 10.1093/nar/gks479
43. A comparison of optimization methods for large-scale L1-regularized linear classification. Yaun G-X, Chang K-W, Hsieh C-J, Lin C-J, J Mach Learn Res 2010 11 3183 3234
44. LIBLINEAR: a library for large linear classification. Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ, J Mach Learn Res 2008 9 1871 1874
45. A compendium to ensure computational reproducibility in high-dimensional classification tasks. Ruschhaupt M, Huber W, Poustka A, Mansmann U, Stat Appl Genet Mol Biol 2004 3 rticle 37