In this study, metagenomics was applied to characterize the microbial community and to discover carbohydrate-active genes of an enriched thermophilic cellulose-degrading sludge. The 16S analysis showed that the sludge microbiome was dominated by genus of cellulolytic Clostridium and methanogenesis Methanothermobacter. In order to retrieve genes from the metagenome, de novo assembly of the 11,930,760 Illumina 100 bp paired-end reads (totally 1.2 Gb) was carried out. 75% of all reads was utilized in the de novo assembly. 31,499 ORFs (Open Reading Frame) with an average length of 852 bp were predicted from the assembly; and 64% of these ORFs were predicted to present full-length genes. Based on the Hidden Markol Model, 253 of the predicted thermo-stable genes were identified as putatively carbohydrate-active. Among them the relative dominance of GH9 (Glycoside Hydrolase) and corresponding CBM3 (Carbohydrate Binding Module) revealed a cellulosome-based attached metabolism of polysaccharide in the thermophilic sludge. The putative carbohydrate-active genes ranged from 20% to 100% amino acid sequence identity to known proteins in NCBI nr database, with half of them showed less than 50% similarity. In addition, the coverage of the genes (in terms of ORFs) identified in the sludge were developed into three clear trends (112×, 29× and 8×) in which 85% of the high coverage trend (112×) mainly consisted of phylum of Firmicutes while 49.3% of the 29× trend was affiliated to the phylum of Chloroflexi.
References
[1]
Ragauskas AJ, Williams CK, Davison BH, Britovsek G, Cairney J, et al. (2006) The Path Forward for Biofuels and Biomaterials. Science 311: 484–489.
[2]
Lynd LR, Weimer PJ, Van Zyl WH, Pretorius IS (2002) Microbial Cellulose Utilization: Fundamentals and Biotechnology. Microbiol Mol Biol Rev 66: 506–577.
[3]
Geng A, Zou G, Yan X, Wang Q, Zhang J, et al. (2012) Expression and characterization of a novel metagenome-derived cellulase Exo2b and its application to improve cellulase activity in Trichoderma reesei. Appl Microbiol Biotechnol 96: 951–962.
[4]
Healy FG, Ray RM, Aldrich HC, Wilkie AC, Ingram LO, et al. (1995) Direct isolation of functional genes encoding cellulases from the microbial consortia in a thermophilic, anaerobic digester maintained on lignocellulose. Appl Microbiol Biotechnol 43: 667–674.
[5]
Ilmberger N, Meske D, Juergensen J, Schulte M, Barthen P, et al. (2012) Metagenomic cellulases highly tolerant towards the presence of ionic liquids-linking thermostability and halotolerance. Appl Microbiol Biotechnol 95: 135–146.
[6]
Voget S, Steele HL, Streit WR (2006) Characterization of a metagenome-derived halotolerant cellulase. J Biotechnol 126: 26–36.
[7]
Rees HC, Grant S, Jones B, Grant WD, Heaphy S (2003) Detecting cellulase and esterase enzyme activities encoded by novel genes present in environmental DNA libraries. Extremophiles 7: 415–421.
[8]
Nealson KH, Venter JC (2007) Metagenomics and the global ocean survey: what’s in it for us, and why should we care? ISME J 1: 185–187.
[9]
Delmont TO, Prestat E, Keegan KP, Faubladier M, Robe P, et al. (2012) Structure, fluctuation and magnitude of a natural grassland soil metagenome. ISME J 6: 1677–1687.
[10]
Mackelprang R, Waldrop MP, DeAngelis KM, David MM, Chavarria KL, et al. (2011) Metagenomic analysis of a permafrost microbial community reveals a rapid response to thaw. Nature 480: 368–371.
[11]
Hess M, Sczyrba A, Egan R, Kim T-W, Chokhawala H, et al. (2011) Metagenomic Discovery of Biomass-Degrading Genes and Genomes from Cow Rumen. Science 331: 463–467.
[12]
Warnecke F, Luginbuhl P, Ivanova N, Ghassemian M, Richardson TH, et al. (2007) Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature 450: 560–565.
[13]
Dai X, Zhu Y, Luo Y, Song L, Liu D, et al. (2012) Metagenomic Insights into the Fibrolytic Microbiome in Yak Rumen. PLoS ONE 7: e40430.
Albertsen M, Hansen LBS, Saunders AM, Nielsen PH, Nielsen KL (2012) A metagenome of a full-scale microbial community carrying out enhanced biological phosphorus removal. ISME J 6: 1094–1106.
[16]
Xia Y, Cai L, Zhang T, Fang HHP (2012) Effects of substrate loading and co-substrates on thermophilic anaerobic conversion of microcrystalline cellulose and microbial communities revealed using high-throughput sequencing. Int J Hydrogen Energy 37: 13652–13659.
[17]
Zerbino DR, Birney E (2008) Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18: 821–829.
[18]
Henrissat B, Bairoch A (1993) New families in the classification of glycosyl hydrolases based on amino acid sequence similarities. Biochem J 293: 781–788.
[19]
Pope PB, Denman SE, Jones M, Tringe SG, Barry K, et al. (2010) Adaptation to herbivory by the Tammar wallaby includes bacterial and glycoside hydrolase profiles different from other herbivores. Proc Natl Acad Sci USA 107: 14793–14798.
[20]
Brulc JM, Antonopoulos DA, Berg Miller ME, Wilson MK, Yannarell AC, et al. (2009) Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc Natl Acad Sci USA 106: 1948.
[21]
Sakon J, Irwin D, Wilson DB, Karplus PA (1997) Structure and mechanism of endo/exocellulase E4 from Thermomonospora fusca. Nat Struct Biol 4: 810–818.
[22]
Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10: R25.
[23]
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, et al. (2009) The Sequence Alignment/Map Format and SAMtools. Bioinformatics 25: 2078–2079.
[24]
Besemer J, Borodovsky M (2005) GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses. Nucleic Acids Res 33: W451–W454.
[25]
Finn RD, Mistry J, Tate J, Coggill P, Heger A, et al. (2009) The Pfam protein families database. Nucleic Acids Research 38: D211–D222 doi:10.1093/nar/gkp985.
[26]
Eddy SR (1998) Profile Hidden Markov Models. Bioinformatics 14: 755–763.
[27]
Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, et al. (2009) The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res 37: D233–D238.
[28]
Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, et al. (2008) The metagenomics RAST server–a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC bioinformatics 9: 386.
[29]
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215: 403–410.
[30]
Huson DH, Auch AF, Qi J, Schuster SC (2007) MEGAN Analysis of Metagenomic Data. Genome Res 17: 377–386.
