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References

  1. Lynd LR, Weimer PJ, Van Zyl WH, Pretorius IS. 2002. Microbial cellulose utilization: fundamentals and biotechnology. Microbiol. Mol. Biol. Rev. 66: 506-577.
    Pubmed PMC CrossRef
  2. Lee H, Hamid S, Zain S. 2014. Conversion of lignocellulosic biomass to nanocellulose: structure and chemical process. ScientificWorldJournal. 2014: 631013.
    Pubmed PMC CrossRef
  3. 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.
    Pubmed CrossRef
  4. Gallezot P. 2012. Conversion of biomass to selected chemical products. Chem. Soc. Rev. 41: 1538-1558.
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  5. Kato DM, Elia N, Flythe M, Lynn BC. 2014. Pretreatment of lignocellulosic biomass using Fenton chemistry. Bioresour. Technol. 162: 273-278.
    Pubmed CrossRef
  6. Iqbal HMN, Kyazze G, Keshavarz T. 2013. Advances in the valorization of lignocellulosic materials by biotechnology:an overview. BioResources 8: 3157-3176.
    CrossRef
  7. 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.
    Pubmed PMC CrossRef
  8. 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.
    Pubmed CrossRef
  9. 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.
    Pubmed CrossRef
  10. 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.
    Pubmed PMC CrossRef
  11. 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.
    Pubmed PMC CrossRef
  12. Metzker ML. 2010. Sequencing technologies — the next generation. Nat. Rev. Genetics 11: 31-46.
    Pubmed CrossRef
  13. Roberts RJ, Carneiro MO, Schatz MC. 2013. The advantages of SMRT sequencing. Genome Biol. 14: 405.
    Pubmed CrossRef
  14. 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.
    Pubmed PMC CrossRef
  15. Kim DS, Lee JH, Yang SH. 2010. Plant Community Dynamics, pp. 107-135.
  16. 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.
    Pubmed PMC CrossRef
  17. 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.
    Pubmed PMC CrossRef
  18. 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.
    Pubmed PMC CrossRef
  19. Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26: 2460-2461.
    Pubmed CrossRef
  20. 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.
    Pubmed PMC CrossRef
  21. 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.
    Pubmed PMC CrossRef
  22. 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.
    Pubmed PMC CrossRef
  23. Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30: 2068-2069.
    Pubmed CrossRef
  24. 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.
    Pubmed PMC CrossRef
  25. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. 2009. BLAST+: architecture and applications. BMC Bioinformatics 10: 421.
    Pubmed PMC CrossRef
  26. 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.
    Pubmed PMC CrossRef
  27. Li H, Durbin R. 2010. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26: 589-595.
    Pubmed PMC CrossRef
  28. 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.
    Pubmed PMC CrossRef
  29. 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.
    Pubmed PMC CrossRef
  30. 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.
    Pubmed PMC CrossRef
  31. 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.
    Pubmed CrossRef
  32. 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.
    Pubmed PMC CrossRef
  33. 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.
    Pubmed PMC CrossRef
  34. 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.
    Pubmed PMC CrossRef
  35. 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.
    Pubmed CrossRef
  36. 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.
    Pubmed PMC CrossRef
  37. 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.
    Pubmed PMC CrossRef
  38. 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.
    Pubmed PMC CrossRef
  39. Horn SJ, Vaaje-Kolstad G, Westereng B, Eijsink VG. 2012. Novel enzymes for the degradation of cellulose. Biotechnol. Biofuels 5: 45.
    Pubmed PMC CrossRef
  40. 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.
    Pubmed PMC CrossRef
  41. 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.
    CrossRef
  42. 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.
    Pubmed PMC CrossRef
  43. 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.
    Pubmed CrossRef
  44. 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.
  45. Kim Y, Liesack W. 2015. Differential assemblage of functional units in paddy soil microbiomes. PLoS One 10: e0122221.
    Pubmed PMC CrossRef
  46. 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.
    Pubmed CrossRef
  47. 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.
    Pubmed PMC CrossRef
  48. 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.
    Pubmed CrossRef
  49. 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.
    Pubmed PMC CrossRef

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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

Received: May 4, 2017; Accepted: June 19, 2017

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

  1. Lynd LR, Weimer PJ, Van Zyl WH, Pretorius IS. 2002. Microbial cellulose utilization: fundamentals and biotechnology. Microbiol. Mol. Biol. Rev. 66: 506-577.
    Pubmed KoreaMed CrossRef
  2. Lee H, Hamid S, Zain S. 2014. Conversion of lignocellulosic biomass to nanocellulose: structure and chemical process. ScientificWorldJournal. 2014: 631013.
    Pubmed KoreaMed CrossRef
  3. 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.
    Pubmed CrossRef
  4. Gallezot P. 2012. Conversion of biomass to selected chemical products. Chem. Soc. Rev. 41: 1538-1558.
    Pubmed CrossRef
  5. Kato DM, Elia N, Flythe M, Lynn BC. 2014. Pretreatment of lignocellulosic biomass using Fenton chemistry. Bioresour. Technol. 162: 273-278.
    Pubmed CrossRef
  6. Iqbal HMN, Kyazze G, Keshavarz T. 2013. Advances in the valorization of lignocellulosic materials by biotechnology:an overview. BioResources 8: 3157-3176.
    CrossRef
  7. 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.
    Pubmed KoreaMed CrossRef
  8. 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.
    Pubmed CrossRef
  9. 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.
    Pubmed CrossRef
  10. 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.
    Pubmed KoreaMed CrossRef
  11. 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.
    Pubmed KoreaMed CrossRef
  12. Metzker ML. 2010. Sequencing technologies — the next generation. Nat. Rev. Genetics 11: 31-46.
    Pubmed CrossRef
  13. Roberts RJ, Carneiro MO, Schatz MC. 2013. The advantages of SMRT sequencing. Genome Biol. 14: 405.
    Pubmed CrossRef
  14. 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.
    Pubmed KoreaMed CrossRef
  15. Kim DS, Lee JH, Yang SH. 2010. Plant Community Dynamics, pp. 107-135.
  16. 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.
    Pubmed KoreaMed CrossRef
  17. 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.
    Pubmed KoreaMed CrossRef
  18. 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.
    Pubmed KoreaMed CrossRef
  19. Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26: 2460-2461.
    Pubmed CrossRef
  20. 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.
    Pubmed KoreaMed CrossRef
  21. 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.
    Pubmed KoreaMed CrossRef
  22. 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.
    Pubmed KoreaMed CrossRef
  23. Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30: 2068-2069.
    Pubmed CrossRef
  24. 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.
    Pubmed KoreaMed CrossRef
  25. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. 2009. BLAST+: architecture and applications. BMC Bioinformatics 10: 421.
    Pubmed KoreaMed CrossRef
  26. 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.
    Pubmed KoreaMed CrossRef
  27. Li H, Durbin R. 2010. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26: 589-595.
    Pubmed KoreaMed CrossRef
  28. 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.
    Pubmed KoreaMed CrossRef
  29. 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.
    Pubmed KoreaMed CrossRef
  30. 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.
    Pubmed KoreaMed CrossRef
  31. 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.
    Pubmed CrossRef
  32. 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.
    Pubmed KoreaMed CrossRef
  33. 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.
    Pubmed KoreaMed CrossRef
  34. 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.
    Pubmed KoreaMed CrossRef
  35. 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.
    Pubmed CrossRef
  36. 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.
    Pubmed KoreaMed CrossRef
  37. 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.
    Pubmed KoreaMed CrossRef
  38. 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.
    Pubmed KoreaMed CrossRef
  39. Horn SJ, Vaaje-Kolstad G, Westereng B, Eijsink VG. 2012. Novel enzymes for the degradation of cellulose. Biotechnol. Biofuels 5: 45.
    Pubmed KoreaMed CrossRef
  40. 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.
    Pubmed KoreaMed CrossRef
  41. 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.
    CrossRef
  42. 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.
    Pubmed KoreaMed CrossRef
  43. 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.
    Pubmed CrossRef
  44. 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.
  45. Kim Y, Liesack W. 2015. Differential assemblage of functional units in paddy soil microbiomes. PLoS One 10: e0122221.
    Pubmed KoreaMed CrossRef
  46. 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.
    Pubmed CrossRef
  47. 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.
    Pubmed KoreaMed CrossRef
  48. 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.
    Pubmed CrossRef
  49. 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.
    Pubmed KoreaMed CrossRef