Abstract
To reveal the dynamic process of cellulose biodegradation and explore more potential cellulases, a microbiota (FPDM) with cellulose-degrading ability was cultivated, and different stages of filter paper degradation were compared. Ion chromatography and comparative metagenomic sequencing revealed that the diversity of FPDM enhanced as the hydrolysate length diversity increased. Sporocytophaga and Cohnella dynamically dominated the synergistic degradation of cellulose in early-intermediate and intermediate-final periods, respectively. Moreover, 363 declining shifting and 231 progressive shifting unannotated genes were speculated to participate in the catabolism of cellulose to cellodextrin/cello-oligosaccharide and to cellobiose, respectively. Based on the dynamic changes in hydrolysates, community structure and gene abundance, a dynamic cellulose-degrading pathway of FPDM was predicted. Our work should provide a new perspective for subsequent identification of key cellulolytic strains and enzymes and clarification of the mechanism of cellulose biodegradation.
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Data availability
The metagenomic reads generated in this study have been submitted to NCBI Sequence Read Archive (SRA) under the accession number SRP255666. A previous version of this paper was published as preprint in Research Square (https://doi.org/10.21203/rs.3.rs-22654/v1) (Yang et al. 2020).
References
Alneberg J, Bjarnason BS, de Bruijn I et al (2014) Binning metagenomic contigs by coverage and composition. Nat Methods 11:1144–1146. https://doi.org/10.1038/nmeth.3103
Berg B, Hofsten B, Pettersson G (1972) Electronmicroscopie observations on the degradation of cellulose fibres by Cellvibrio fulvus and Sporocytophaga myxococcoides. J Appl Bacteriol 35:215–219. https://doi.org/10.1111/j.1365-2672.1972.tb03692.x
Berlemont R, Allison SD, Weihe C et al (2014) Cellulolytic potential under environmental changes in microbial communities from grassland litter. Front Microbiol 5:639. https://doi.org/10.3389/fmicb.2014.00639
Brulc JM, Antonopoulos DA, Miller MEB et al (2009) Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc Natl Acad Sci USA 106:1948–1953. https://doi.org/10.1073/pnas.0806191105
Buchfink B, Xie C, Huson DH (2015) Fast and sensitive protein alignment using DIAMOND. Nat Methods 12:59–60. https://doi.org/10.1038/nmeth.3176
Cao R, Jin Y, Xu D (2012) Recognition of cello-oligosaccharides by CBM17 from Clostridium cellulovorans: molecular dynamics simulation. J Phys Chem B 116:6087–6096. https://doi.org/10.1021/jp3010647
Caporaso JG, Kuczynski J, Stombaugh J et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336. https://doi.org/10.1038/nmeth.f.303
Chen Y, Chen Y, Shi C et al (2018) SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data. GigaScience 7:1–6. https://doi.org/10.1093/gigascience/gix120
Couto-Rodriguez RL, Montalvo-Rodriguez R (2019) Temporal analysis of the microbial community from the crystallizer ponds in Cabo Rojo, Puerto Rico, using metagenomics. Genes 10:422. https://doi.org/10.3390/genes10060422
Cui J, Mai G, Wang Z et al (2019) Metagenomic insights into a cellulose-rich niche reveal microbial cooperation in cellulose degradation. Front Microbiol 10:618. https://doi.org/10.3389/fmicb.2019.00618
Deng YJ, Wang SY (2016) Synergistic growth in bacteria depends on substrate complexity. J Microbiol 54:23–30. https://doi.org/10.1007/s12275-016-5461-9
Dhaeseleer P, Gladden JM, Allgaier M et al (2013) Proteogenomic analysis of a thermophilic bacterial consortium adapted to deconstruct switchgrass. PLoS ONE 8:e68465. https://doi.org/10.1371/journal.pone.0068465
Dos Santos FC, de Oliveira MAS, Seixas FAV et al (2020) A novel cellobiohydrolase I (CBHI) from Penicillium digitatum: production, purification, and characterization. Appl Biochem Biotechnol 192:257–282. https://doi.org/10.1007/s12010-020-03307-9
Dumova VA, Kruglov YV (2009) A cellulose-decomposing bacterial association. Microbiology 78:234–239. https://doi.org/10.1134/S0026261709020155
Eida MF, Nagaoka T, Wasaki J et al (2012) Isolation and characterization of cellulose-decomposing bacteria inhabiting sawdust and coffee residue composts. Microbes Environ 27:226–233. https://doi.org/10.1264/jsme2.ME11299
Ernst J, Bar-Joseph Z (2006) STEM: a tool for the analysis of short time series gene expression data. BMC Bioinform 7:191. https://doi.org/10.1186/1471-2105-7-191
Fosses A, Maté M, Franche N et al (2017) A seven-gene cluster in Ruminiclostridium cellulolyticum is essential for signalization, uptake and catabolism of the degradation products of cellulose hydrolysis. Biotechnol Biofuels 10:250. https://doi.org/10.1186/s13068-017-0933-7
Hallberg BM, Henriksson G, Pettersson G et al (2002) Crystal structure of the flavoprotein domain of the extracellular flavocytochrome cellobiose dehydrogenase. J Mol Biol 315:421–434. https://doi.org/10.1006/jmbi.2001.5246
Hameed A, Hung M, Lin S et al (2013) Cohnella formosensis sp. nov., a xylanolytic bacterium isolated from the rhizosphere of Medicago sativa L. Int J Syst Evol Microbiol 63:2806–2812. https://doi.org/10.1099/ijs.0.045831-0
Hayat R, Sheirdil RA, Iftikhar-ul-Hassan M et al (2012) Characterization and identification of compost bacteria based on 16S rRNA gene sequencing. Ann Microbiol 63:905–912. https://doi.org/10.1007/s13213-012-0542-4
He B, Jin S, Cao J et al (2019) Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases. Biotechnol Biofuels 12:153. https://doi.org/10.1186/s13068-019-1498-4
Herrera LM, Brana V, Franco Fraguas L et al (2019) Characterization of the cellulase-secretome produced by the Antarctic bacterium Flavobacterium sp. AUG42. Microbiol Res 223–225:13–21. https://doi.org/10.1016/j.micres.2019.03.009
Huson DH, Mitra S, Ruscheweyh HJ et al (2011) Integrative analysis of environmental sequences using MEGAN4. Genome Res 21:1552–1560. https://doi.org/10.1101/gr.120618.111
Jayasekara S, Ratnayake R (2019) Microbial cellulases: an overview and applications. In: Pascual AR, Martin MEE (eds) Cellulose. IntechOpen, London, pp 1–18. https://doi.org/10.5772/intechopen.84531
Jiménez DJ, Korenblum E, van Elsas JD (2014) Novel multispecies microbial consortia involved in lignocellulose and 5-hydroxymethylfurfural bioconversion. Appl Microbiol Biotechnol 98:2789–2803. https://doi.org/10.1007/s00253-013-5253-7
Juturu V, Wu JC (2014) Microbial cellulases: engineering, production and applications. Renew Sust Energ Rev 33:188–203. https://doi.org/10.1016/j.rser.2014.01.077
Kämpfer P, Rossello-Mora R, Falsen E et al (2006) Cohnella thermotolerans gen. nov., sp. nov., and classification of ‘Paenibacillus hongkongensis’ as Cohnella hongkongensis sp. nov. Int J Syst Evol Microbiol 56:781–786. https://doi.org/10.1099/ijs.0.63985-0
Kanehisa M, Araki M, Goto S et al (2008) KEGG for linking genomes to life and the environment. Nucl Acids Res 36:D480–D484. https://doi.org/10.1093/nar/gkm882
Kang DD, Li F, Kirton E et al (2019) MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7:e7359. https://doi.org/10.7717/peerj.7359
Khianngam S, Tanasupawat S, Akaracharanya A et al (2012) Cohnella cellulosilytica sp. nov., isolated from buffalo faeces. Int J Syst Evol Microbiol 62:1921–1925. https://doi.org/10.1099/ijs.0.032607-0
Knight R, Vrbanac A, Taylor BC et al (2018) Best practices for analysing microbiomes. Nat Rev Microbiol 16:410–422. https://doi.org/10.1038/s41579-018-0029-9
Kołaczkowski BM, Schaller KS, Sørensen TH et al (2020) Removal of N-linked glycans in cellobiohydrolase Cel7A from Trichoderma reesei reveals higher activity and binding affinity on crystalline cellulose. Biotechnol Biofuels 13:1–13. https://doi.org/10.1186/s13068-020-01779-9
Lee CK, Jang MY, Park HR et al (2016) Cloning and characterization of xylanase in cellulolytic Bacillus sp. strain JMY1 isolated from forest soil. Appl Biol Chem 59:415–423. https://doi.org/10.1007/s13765-016-0179-2
Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658–1659. https://doi.org/10.1093/bioinformatics/btl158
Li R, Yu C, Li Y et al (2009) SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics 25:1966–1967. https://doi.org/10.1093/bioinformatics/btp336
Li J, Jia H, Cai X et al (2014) An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol 32:834–841. https://doi.org/10.1038/nbt.2942
Liu L, Gao P, Chen G et al (2014) Draft genome sequence of cellulose-digesting bacterium Sporocytophaga myxococcoides PG-01. Genome Announc 2:e01154-e1214. https://doi.org/10.1128/genomea.01154-14
Lladó S, López-Mondéjar R, Baldrian P (2017) Forest soil bacteria: diversity, involvement in ecosystem processes, and response to global change. Microbiol Mol Biol Rev 81:e00063-e116. https://doi.org/10.1128/mmbr.00063-16
Lombard V, Ramulu HG, Drula E et al (2014) The carbohydrate-active enzymes database (CAZy) in 2013. Nucl Acids Res 42:D490–D495. https://doi.org/10.1093/nar/gkt1178
López-Mondéjar R, Zühlke D, Becher D et al (2016a) Cellulose and hemicellulose decomposition by forest soil bacteria proceeds by the action of structurally variable enzymatic systems. Sci Rep 6:25279. https://doi.org/10.1038/srep25279
López-Mondéjar R, Zühlke D, Větrovský T et al (2016b) Decoding the complete arsenal for cellulose and hemicellulose deconstruction in the highly efficient cellulose decomposer Paenibacillus O199. Biotechnol Biofuels 9:104. https://doi.org/10.1186/s13068-016-0518-x
Luo R, Liu B, Xie Y et al (2012) SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience 1:18. https://doi.org/10.1186/2047-217X-1-18
Mande SS, Mohammed MH, Ghosh TS (2012) Classification of metagenomic sequences: methods and challenges. Brief Bioinform 13:669–681. https://doi.org/10.1093/bib/bbs054
Manfredi AP, Perotti NI, Martinez MA (2015) Cellulose degrading bacteria isolated from industrial samples and the gut of native insects from Northwest of Argentina. J Basic Microbiol 55:1384–1393. https://doi.org/10.1002/jobm.201500269
Medie FM, Davies GJ, Drancourt M et al (2012) Genome analyses highlight the different biological roles of cellulases. Nat Rev Microbiol 10:227–234. https://doi.org/10.1038/nrmicro2729
Miller GL (1959) Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal Chem 31:426–428. https://doi.org/10.1021/ac60147a030
Moraes EC, Alvarez TM, Persinoti GF et al (2018) Lignolytic-consortium omics analyses reveal novel genomes and pathways involved in lignin modification and valorization. Biotechnol Biofuels 11:75. https://doi.org/10.1186/s13068-018-1073-4
Nedashkovskaya OI, Kim SB (2015) Pontibacter Bergey’s manual of systematics of archaea and bacteria. Wiley, Hoboken, pp 1–4. https://doi.org/10.1002/9781118960608.gbm00272
Parks DH, Imelfort M, Skennerton CT et al (2015) CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res 25:1043–1055. https://doi.org/10.1101/gr.186072.114
Qiao C, Ryan Penton C, Liu C et al (2019) Key extracellular enzymes triggered high-efficiency composting associated with bacterial community succession. Bioresour Technol 288:121576. https://doi.org/10.1016/j.biortech.2019.121576
Qin J, Li R, Raes J et al (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464:59–65. https://doi.org/10.1038/nature08821
Qin N, Yang F, Li A et al (2014) Alterations of the human gut microbiome in liver cirrhosis. Nature 513:59–64. https://doi.org/10.1038/nature13568
Raman B, McKeown CK, Rodriguez M et al (2011) Transcriptomic analysis of Clostridium thermocellum ATCC 27405 cellulose fermentation. BMC Microbiol 11:134. https://doi.org/10.1186/1471-2180-11-134
Rodionova MV, Poudyal RS, Tiwari I et al (2017) Biofuel production: challenges and opportunities. Int J Hydrogen Energ 42:8450–8461. https://doi.org/10.1016/j.ijhydene.2016.11.125
Rodriguez-R LM, Gunturu S, Harvey WT et al (2018) The microbial genomes atlas (MiGA) webserver: taxonomic and gene diversity analysis of Archaea and Bacteria at the whole genome level. Nucl Acids Res 46:W282–W288. https://doi.org/10.1093/nar/gky467
Rosnow JJ, Anderson LN, Nair RN et al (2016) Profiling microbial lignocellulose degradation and utilization by emergent omics technologies. Crit Rev Biotechnol 37:626–640. https://doi.org/10.1080/07388551.2016.1209158
Schloss PD, Westcott SL, Ryabin T et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75:7537–7541. https://doi.org/10.1128/AEM.01541-09
Seemann T (2014) Prokka: rapid prokaryotic genome annotation. Bioinformatics 30:2068–2069. https://doi.org/10.1093/bioinformatics/btu153
Song W, Han X, Qian Y et al (2016) Proteomic analysis of the biomass hydrolytic potentials of Penicillium oxalicum lignocellulolytic enzyme system. Biotechnol Biofuels 9:68. https://doi.org/10.1186/s13068-016-0477-2
Sulej J, Janusz G, Osińska-Jaroszuk M et al (2013) Characterization of cellobiose dehydrogenase and its FAD-domain from the ligninolytic basidiomycete Pycnoporus sanguineus. Enzyme Microb Technol 53:427–437. https://doi.org/10.1016/j.enzmictec.2013.09.007
Sunagawa S, Coelho LP, Chaffron S et al (2015) Structure and function of the global ocean microbiome. Science 348:1261359. https://doi.org/10.1126/science.1261359
Taillefer M, Arntzen MO, Henrissat B et al (2018) Proteomic dissection of the cellulolytic machineries used by soil-dwelling Bacteroidetes. mSystems 3:e00240-00218. https://doi.org/10.1128/mSystems.00240-18
Talamantes D, Biabini N, Dang H et al (2016) Natural diversity of cellulases, xylanases, and chitinases in bacteria. Biotechnol Biofuels 9:133. https://doi.org/10.1186/s13068-016-0538-6
Tomazetto G, Pimentel AC, Wibberg D et al (2020) Multi-omic directed discovery of cellulosomes, polysaccharide utilization loci, and lignocellulases from an enriched rumen anaerobic consortium. Appl Environ Microbiol. https://doi.org/10.1128/aem.00199-20
Uritskiy GV, DiRuggiero J, Taylor J (2018) MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6:1–13. https://doi.org/10.1186/s40168-018-0541-1
Villar E, Farrant GK, Follows M et al (2015) Environmental characteristics of Agulhas rings affect interocean plankton transport. Science 348:1261447. https://doi.org/10.1126/science.1261447
Wang C, Dong D, 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. https://doi.org/10.1186/s13068-016-0440-2
Wang J, Lu J, Zhang Y et al (2018) Metagenomic analysis of antibiotic resistance genes in coastal industrial mariculture systems. Bioresour Technol 253:235–243. https://doi.org/10.1016/j.biortech.2018.01.035
White JR, Nagarajan N, Pop M (2009) Statistical methods for detecting differentially abundant features in clinical metagenomic samples. PLoS Comput Biol 5:e1000352. https://doi.org/10.1371/journal.pcbi.1000352
Wilhelm RC, Singh R, Eltis LD et al (2018) Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J 13:413–429. https://doi.org/10.1038/s41396-018-0279-6
Wilson DB (2011) Microbial diversity of cellulose hydrolysis. Curr Opin Microbiol 14:259–263. https://doi.org/10.1016/j.mib.2011.04.004
Wongwilaiwalin S, Rattanachomsri U, Laothanachareon T et al (2010) Analysis of a thermophilic lignocellulose degrading microbial consortium and multi-species lignocellulolytic enzyme system. Enzyme Microb Technol 47:283–290. https://doi.org/10.1016/j.enzmictec.2010.07.013
Wu YW, Simmons BA, Singer SW (2016) MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32:605–607. https://doi.org/10.1093/bioinformatics/btv638
Yang M, Zhao J, Yuan Y et al (2020) Comparative metagenomic discovery of the dynamic cellulose-degrading process from a synergistic cellulolytic microbiota. Res Sq. https://doi.org/10.21203/rs.3.rs-22654/v1
Yin Y, Mao X, Yang J et al (2012) dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucl Acids Res 40:W445–W451. https://doi.org/10.1093/nar/gks479
Younesi FS, Pazhang M, Najavand S et al (2016) Deleting the Ig-like domain of Alicyclobacillus acidocaldarius endoglucanase Cel9A causes a simultaneous increase in the activity and stability. Mol Biotechnol 58:12–21. https://doi.org/10.1007/s12033-015-9900-3
Zhang KD, Li W, Wang YF et al (2018) Processive degradation of crystalline cellulose by a multimodular endoglucanase via a wirewalking mode. Biomacromol 19:1686–1696. https://doi.org/10.1021/acs.biomac.8b00340
Zhou Y, Pope PB, Li S et al (2014) Omics-based interpretation of synergism in a soil-derived cellulose-degrading microbial community. Sci Rep 4:5288. https://doi.org/10.1038/srep05288
Zhu W, Lomsadze A, Borodovsky M (2010) Ab initio gene identification in metagenomic sequences. Nucl Acids Res 38:e132. https://doi.org/10.1093/nar/gkq275
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FY, XL, and XC were supported by grants from the National Natural Sciences Foundation of China (31671796, 31771907, and 31801469), and the Liaoning BaiQianWan Talents Program is also gratefully acknowledged.
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This work was supported by the National Natural Sciences Foundation of China (31671796, 31771907, and 31801469), and the Liaoning BaiQianWan Talents Program is also gratefully acknowledged.
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MY: designed the strategy for metagenomic analysis, developed the scripts and wrote the manuscript. JZ: contributed with reagents, materials, and analyses, performed the experiments, and wrote the manuscript. YY, XC: contributed with reagents, materials, and performed the experiments. FY, XL: drafted, wrote and revised the manuscript. All authors agreed with the submitted version of the paper. All authors read and approved the final manuscript.
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Yang, M., Zhao, J., Yuan, Y. et al. Comparative metagenomic discovery of the dynamic cellulose-degrading process from a synergistic cellulolytic microbiota. Cellulose 28, 2105–2123 (2021). https://doi.org/10.1007/s10570-020-03671-z
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DOI: https://doi.org/10.1007/s10570-020-03671-z