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http://dx.doi.org/10.4014/jmb.1705.05008

Metagenomic SMRT Sequencing-Based Exploration of Novel Lignocellulose-Degrading Capability in Wood Detritus from Torreya nucifera in Bija Forest on Jeju Island  

Oh, Han Na (Department of Systems Biotechnology, Chung-Ang University)
Lee, Tae Kwon (Department of Environmental Engineering, Yonsei University)
Park, Jae Wan (Department of Systems Biotechnology, Chung-Ang University)
No, Jee Hyun (Department of Environmental Engineering, Yonsei University)
Kim, Dockyu (Division of Life Sciences, Korea Polar Research Institute)
Sul, Woo Jun (Department of Systems Biotechnology, Chung-Ang University)
Publication Information
Journal of Microbiology and Biotechnology / v.27, no.9, 2017 , pp. 1670-1680 More about this Journal
Abstract
Lignocellulose, composed mostly of cellulose, hemicellulose, and lignin generated through secondary growth of woody plant, is considered as promising resources for biofuel. In order to use lignocellulose as a biofuel, biodegradation besides high-cost chemical treatments were applied, but knowledge on the decomposition of lignocellulose occurring in a natural environment is insufficient. We analyzed the 16S rRNA gene and metagenome to understand how the lignocellulose is decomposed naturally in decayed Torreya nucifera (L) of Bija forest (Bijarim) in Gotjawal, an ecologically distinct environment. A total of 464,360 reads were obtained from 16S rRNA gene sequencing, representing diverse phyla; Proteobacteria (51%), Bacteroidetes (11%) and Actinobacteria (10%). The metagenome analysis using single molecules real-time sequencing revealed that the assembled contigs determined originated from Proteobacteria (58%) and Actinobacteria (10.3%). Carbohydrate Active enZYmes (CAZy)- and Protein families (Pfam)-based analysis showed that Proteobacteria was involved in degrading whole lignocellulose, and Actinobacteria played a role only in a part of hemicellulose degradation. Combining these results, it suggested that Proteobacteria and Actinobacteria had selective biodegradation potential for different lignocellulose substrates. Thus, it is considered that understanding of the systemic microbial degradation pathways may be a useful strategy for recycle of lignocellulosic biomass, and the microbial enzymes in Bija forest can be useful natural resources in industrial processes.
Keywords
Lignocellulose degradation; Bija forest; metagenome; 16S rRNA; CAZy; Pfam;
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1 Lopez-Mondejar R, Zuhlke D, Becher D, Riedel K, Baldrian P. 2016. Cellulose and hemicellulose decomposition by forest soil bacteria proceeds by the action of structurally variable enzymatic systems. Sci. Rep. 6: 25279.   DOI
2 Lynd LR, Weimer PJ, Van Zyl WH, Pretorius IS. 2002. Microbial cellulose utilization: fundamentals and biotechnology. Microbiol. Mol. Biol. Rev. 66: 506-577.   DOI
3 Lee H, Hamid S, Zain S. 2014. Conversion of lignocellulosic biomass to nanocellulose: structure and chemical process. ScientificWorldJournal. 2014: 631013.
4 Himmel ME, Ding S-Y, Johnson DK, Adney WS, Nimlos MR, Brady JW, et al. 2007. Biomass recalcitrance: engineering plants and enzymes for biofuels production. Science 315: 804-807.   DOI
5 Gallezot P. 2012. Conversion of biomass to selected chemical products. Chem. Soc. Rev. 41: 1538-1558.   DOI
6 Kato DM, Elia N, Flythe M, Lynn BC. 2014. Pretreatment of lignocellulosic biomass using Fenton chemistry. Bioresour. Technol. 162: 273-278.   DOI
7 Iqbal HMN, Kyazze G, Keshavarz T. 2013. Advances in the valorization of lignocellulosic materials by biotechnology: an overview. BioResources 8: 3157-3176.
8 Mhuantong W, Charoensawan V, Kanokratana P, Tangphatsornruang S, Champreda V. 2015. Comparative analysis of sugarcane bagasse metagenome reveals unique and conserved biomass-degrading enzymes among lignocellulolytic microbial communities. Biotechnol. Biofuels 8: 16.   DOI
9 Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. 2009. The carbohydrate-active enzymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res. 37: D233-D238.   DOI
10 O'Leary N A, W right MW, Brister JR, C iufo S , Haddad D , McVeigh R, et al. 2016. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44: D733-D745.   DOI
11 Lombard V, Ramulu HG, Drula E, Coutinho PM, Henrissat B. 2014. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42: D490-D495.   DOI
12 Park BH, Karpinets TV, Syed MH, Leuze MR, Uberbacher EC. 2010. CAZymes Analysis Toolkit (CAT): Web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database. Glycobiology 20: 1574-1584.   DOI
13 Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, et al. 2016. The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44: D279-D285.   DOI
14 Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, et al. 2008. The RAST server: rapid annotations using subsystems technology. BMC Genomics 9: 75.   DOI
15 Aylward FO, Burnum KE, Scott JJ, Suen G, Tringe SG, Adams SM, et al. 2012. Metagenomic and metaproteomic insights into bacterial communities in leaf-cutter ant fungus gardens. ISME J. 6: 1688-1701.   DOI
16 Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. 2016. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 44: D286-D293.   DOI
17 Kanehisa M, Sato Y, Morishima K. 2016. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428: 726-731.   DOI
18 Konietzny SG, Pope PB, Weimann A, McHardy AC. 2014. Inference of phenotype-defining functional modules of protein families for microbial plant biomass degraders. Biotechnol. Biofuels 7: 124.   DOI
19 Kanokratana P, Uengwetwanit T, Rattanachomsri U, Bunterngsook B, Nimchua T, Tangphatsornruang S, et al. 2011. Insights into the phylogeny and metabolic potential of a primary tropical peat swamp forest microbial community by metagenomic analysis. Microb. Ecol. 61: 518-528.   DOI
20 Woo HL, Hazen TC, Simmons BA, DeAngelis KM. 2014. Enzyme activities of aerobic lignocellulolytic bacteria isolated from wet tropical forest soils. Syst. Appl. Microbiol. 37: 60-67.   DOI
21 Scully ED, Geib SM, Hoover K, Tien M, Tringe SG, Barry KW, et al. 2013. Metagenomic profiling reveals lignocellulose degrading system in a microbial community associated with a wood-feeding beetle. PLoS One 8: e73827.   DOI
22 Metzker ML. 2010. Sequencing technologies - the next generation. Nat. Rev. Genetics 11: 31-46.   DOI
23 Roberts RJ, Carneiro MO, Schatz MC. 2013. The advantages of SMRT sequencing. Genome Biol. 14: 405.   DOI
24 Qin W. 2016. Recent developments in using advanced sequencing technologies for the genomic studies of lignin and cellulose degrading microorganisms. Int. J. Biol. Sci. 12: 156.   DOI
25 Kim DS, Lee JH, Yang SH. 2010. Plant Community Dynamics, pp. 107-135.
26 Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, et al. 2012. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6: 1621-1624.   DOI
27 Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. 2010. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7: 335-336.   DOI
28 Horn SJ, Vaaje-Kolstad G, Westereng B, Eijsink VG. 2012. Novel enzymes for the degradation of cellulose. Biotechnol. Biofuels 5: 45.   DOI
29 Zhu D, Zhang P, Xie C, Zhang W, Sun J, Qian WJ, Yang B. 2017. Biodegradation of alkaline lignin by Bacillus ligniniphilus L1. Biotechnol. Biofuels 10: 44.   DOI
30 Zhang J, Presley GN, Hammel KE, Ryu JS, Menke JR, Figueroa M, et al. 2016. Localizing gene regulation reveals a staggered wood decay mechanism for the brown rot fungus Postia placenta. Proc. Natl. Acad. Sci. USA 113: 10968-10973.   DOI
31 Kameshwar AKS, Qin WS. 2016. Recent developments in using advanced sequencing technologies for the genomic studies of lignin and cellulose degrading microorganisms. Int. J. Biol. Sci. 12: 156-171.   DOI
32 Han S-I. 2016. Phylogenetic characteristics of bacterial populations and isolation of aromatic compounds utilizing bacteria from humus layer of oak forest. Korean J. Microbiol. 52: 175-182.   DOI
33 Kim Y, Liesack W. 2015. Differential assemblage of functional units in paddy soil microbiomes. PLoS One 10: e0122221.   DOI
34 Jimenez DJ, de Lima Brossi MJ, Schuckel J, Kracun SK, Willats WG, van Elsas JD. 2016. Characterization of three plant biomass-degrading microbial consortia by metagenomicsand metasecretomics-based approaches. Appl. Microbiol. Biotechnol. 100: 10463-10477.   DOI
35 Folman LB, Klein Gunnewiek PJ, Boddy L, de Boer W. 2008. Impact of white-rot fungi on numbers and community composition of bacteria colonizing beech wood from forest soil. FEMS Microbiol. Ecol. 63: 181-191.   DOI
36 Lacerda J unior G V, N oronha M F, d e Sousa ST, Cabral L , Domingos DF, Saber ML, et al. 2017. Potential of semiarid soil from Caatinga biome as a novel source for mining lignocellulose-degrading enzymes. FEMS Microbiol. Ecol. 93: fiw248.   DOI
37 Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. 2010. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26: 266-267.   DOI
38 Eren AM, Vineis JH, Morrison HG, Sogin ML. 2013. A filtering method to generate high quality short reads using Illumina paired-end technology. PLoS One 8: e66643.   DOI
39 Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26: 2460-2461.   DOI
40 Cole JR, Wang Q, Cardenas E, Fish J, Chai B, Farris RJ, et al. 2009. The ribosomal database project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37: D141-D145.   DOI
41 Sakai H, Naito K, Ogiso-Tanaka E, Takahashi Y, Iseki K, Muto C, et al. 2015. The power of single molecule real-time sequencing technology in the de novo assembly of a eukaryotic genome. Sci. Rep. 5: 16780.   DOI
42 Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30: 2068-2069.   DOI
43 Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. 2010. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11: 119.   DOI
44 Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. 2009. BLAST+: architecture and applications. BMC Bioinformatics 10: 421.   DOI
45 Li P-E, Lo C-C, Anderson JJ, Davenport KW, Bishop-Lilly KA, Xu Y, et al. 2017. Enabling the democratization of the genomics revolution with a fully integrated Web-based bioinformatics platform. Nucleic Acids Res. 45: 67-80.   DOI
46 Li H, Durbin R. 2010. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26: 589-595.   DOI
47 Warnecke F, Luginbühl P, Ivanova N, Ghassemian M, Richardson TH, Stege JT, et al. 2007. Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature 450: 560-565.   DOI
48 Cragg SM, Beckham GT, Bruce NC, Bugg TD, Distel DL, Dupree P, et al. 2015. Lignocellulose degradation mechanisms across the Tree of Life. Curr. Opin. Chem. Biol. 29: 108-119.   DOI
49 Wang C, Dong D, Wang H, Muller K, Qin Y, Wang H, et al. 2016. Metagenomic analysis of microbial consortia enriched from compost: new insights into the role of Actinobacteria in lignocellulose decomposition. Biotechnol. Biofuels 9: 22.   DOI