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Metagenomic SMRT Sequencing-Based Exploration of Novel Lignocellulose-Degrading Capability in Wood Detritus from Torreya nucifera in Bija Forest on Jeju Island
1Department of Systems Biotechnology, Chung-Ang University, Anseong 17546, Republic of Korea, 2Department of Environmental Engineering, Yonsei University, Wonju 26493, Republic of Korea, 3Division of Life Sciences, Korea Polar Research Institute, Incheon 21990, Republic of Korea
J. Microbiol. Biotechnol. 2017; 27(9): 1670-1680
Published September 28, 2017 https://doi.org/10.4014/jmb.1705.05008
Copyright © The Korean Society for Microbiology and Biotechnology.
Abstract
Keywords
References
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- Lee H, Hamid S, Zain S. 2014. Conversion of lignocellulosic biomass to nanocellulose: structure and chemical process. ScientificWorldJournal. 2014: 631013.
- 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.
- Gallezot P. 2012. Conversion of biomass to selected chemical products. Chem. Soc. Rev. 41: 1538-1558.
- Kato DM, Elia N, Flythe M, Lynn BC. 2014. Pretreatment of lignocellulosic biomass using Fenton chemistry. Bioresour. Technol. 162: 273-278.
- Iqbal HMN, Kyazze G, Keshavarz T. 2013. Advances in the valorization of lignocellulosic materials by biotechnology:an overview. BioResources 8: 3157-3176.
- 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.
- 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.
- 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.
- 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.
- 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.
- Metzker ML. 2010. Sequencing technologies — the next generation. Nat. Rev. Genetics 11: 31-46.
- Roberts RJ, Carneiro MO, Schatz MC. 2013. The advantages of SMRT sequencing. Genome Biol. 14: 405.
- 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.
- Kim DS, Lee JH, Yang SH. 2010. Plant Community Dynamics, pp. 107-135.
- 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.
- 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.
- 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.
- Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26: 2460-2461.
- 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.
- 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.
- 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.
- Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30: 2068-2069.
- 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.
- Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. 2009. BLAST+: architecture and applications. BMC Bioinformatics 10: 421.
- 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.
- Li H, Durbin R. 2010. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26: 589-595.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Horn SJ, Vaaje-Kolstad G, Westereng B, Eijsink VG. 2012. Novel enzymes for the degradation of cellulose. Biotechnol. Biofuels 5: 45.
- 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.
- 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.
- 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.
- 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.
- 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.
- Kim Y, Liesack W. 2015. Differential assemblage of functional units in paddy soil microbiomes. PLoS One 10: e0122221.
- 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.
- 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.
- 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.
- 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.
Related articles in JMB
Article
Research article
J. Microbiol. Biotechnol. 2017; 27(9): 1670-1680
Published online September 28, 2017 https://doi.org/10.4014/jmb.1705.05008
Copyright © The Korean Society for Microbiology and Biotechnology.
Metagenomic SMRT Sequencing-Based Exploration of Novel Lignocellulose-Degrading Capability in Wood Detritus from Torreya nucifera in Bija Forest on Jeju Island
Han Na Oh 1, Tae Kwon Lee 2, Jae Wan Park 1, Jee Hyun No 2, Dockyu Kim 3 and Woo Jun Sul 1*
1Department of Systems Biotechnology, Chung-Ang University, Anseong 17546, Republic of Korea, 2Department of Environmental Engineering, Yonsei University, Wonju 26493, Republic of Korea, 3Division of Life Sciences, Korea Polar Research Institute, Incheon 21990, Republic of Korea
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
References
- Lynd LR, Weimer PJ, Van Zyl WH, Pretorius IS. 2002. Microbial cellulose utilization: fundamentals and biotechnology. Microbiol. Mol. Biol. Rev. 66: 506-577.
- Lee H, Hamid S, Zain S. 2014. Conversion of lignocellulosic biomass to nanocellulose: structure and chemical process. ScientificWorldJournal. 2014: 631013.
- 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.
- Gallezot P. 2012. Conversion of biomass to selected chemical products. Chem. Soc. Rev. 41: 1538-1558.
- Kato DM, Elia N, Flythe M, Lynn BC. 2014. Pretreatment of lignocellulosic biomass using Fenton chemistry. Bioresour. Technol. 162: 273-278.
- Iqbal HMN, Kyazze G, Keshavarz T. 2013. Advances in the valorization of lignocellulosic materials by biotechnology:an overview. BioResources 8: 3157-3176.
- 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.
- 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.
- 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.
- 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.
- 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.
- Metzker ML. 2010. Sequencing technologies — the next generation. Nat. Rev. Genetics 11: 31-46.
- Roberts RJ, Carneiro MO, Schatz MC. 2013. The advantages of SMRT sequencing. Genome Biol. 14: 405.
- 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.
- Kim DS, Lee JH, Yang SH. 2010. Plant Community Dynamics, pp. 107-135.
- 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.
- 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.
- 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.
- Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26: 2460-2461.
- 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.
- 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.
- 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.
- Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30: 2068-2069.
- 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.
- Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. 2009. BLAST+: architecture and applications. BMC Bioinformatics 10: 421.
- 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.
- Li H, Durbin R. 2010. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26: 589-595.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Horn SJ, Vaaje-Kolstad G, Westereng B, Eijsink VG. 2012. Novel enzymes for the degradation of cellulose. Biotechnol. Biofuels 5: 45.
- 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.
- 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.
- 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.
- 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.
- 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.
- Kim Y, Liesack W. 2015. Differential assemblage of functional units in paddy soil microbiomes. PLoS One 10: e0122221.
- 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.
- 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.
- 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.
- 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.