Exportar registro bibliográfico


Revealing the insoluble metasecretome of lignocellulose-degrading microbial communities (2017)

  • Authors:
  • Unidade: IFSC
  • DOI: 10.1038/s41598-017-02506-5
  • Language: Inglês
  • Imprenta:
  • Source:
  • Acesso à fonteDOI
    Informações sobre o DOI: 10.1038/s41598-017-02506-5 (Fonte: oaDOI API)
    • Este periódico é de acesso aberto
    • Este artigo é de acesso aberto
    • URL de acesso aberto
    • Cor do Acesso Aberto: gold
    • Licença: cc-by

    How to cite
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      ALESSI, Anna M.; BIRD, Susannah M.; BENNETT, Joseph P.; et al. Revealing the insoluble metasecretome of lignocellulose-degrading microbial communities. Scientific Reports, London, Nature, v. 7, p. 2356-1-2356-10, 2017. Disponível em: < http://dx.doi.org/10.1038/s41598-017-02506-5 > DOI: 10.1038/s41598-017-02506-5.
    • APA

      Alessi, A. M., Bird, S. M., Bennett, J. P., Oates, N. C., Li, Y., Dowle, A. A., et al. (2017). Revealing the insoluble metasecretome of lignocellulose-degrading microbial communities. Scientific Reports, 7, 2356-1-2356-10. doi:10.1038/s41598-017-02506-5
    • NLM

      Alessi AM, Bird SM, Bennett JP, Oates NC, Li Y, Dowle AA, Polikarpov I, Young JPW, McQueen-Mason SJ, Bruce NC. Revealing the insoluble metasecretome of lignocellulose-degrading microbial communities [Internet]. Scientific Reports. 2017 ; 7 2356-1-2356-10.Available from: http://dx.doi.org/10.1038/s41598-017-02506-5
    • Vancouver

      Alessi AM, Bird SM, Bennett JP, Oates NC, Li Y, Dowle AA, Polikarpov I, Young JPW, McQueen-Mason SJ, Bruce NC. Revealing the insoluble metasecretome of lignocellulose-degrading microbial communities [Internet]. Scientific Reports. 2017 ; 7 2356-1-2356-10.Available from: http://dx.doi.org/10.1038/s41598-017-02506-5

    Referências citadas na obra
    Schneider, T. et al. Who is who in litter decomposition? Metaproteomics reveals major microbial players and their biogeochemical functions. ISME J. 6, 1749–1762, doi: 10.1038/ismej.2012.11 (2012).
    Burns, R. G. et al. Soil enzymes in a changing environment: current knowledge and future directions. Soil Biol. Biochem. 58, 216–234, doi: 10.1016/j.soilbio.2012.11.009 (2013).
    Stroobants, A., Portetelle, D. & Vandenbol, M. New carbohydrate-active enzymes identified by screening two metagenomic libraries derived from the soil of a winter wheat field. J. Appl. Microbiol. 117, 1045–1055, doi: 10.1111/jam.12597 (2014).
    Verastegui, Y. et al. Multisubstrate isotope labeling and metagenomic analysis of active soil bacterial communities. mBio 5, e01157–14, doi: 10.1128/mBio.01157-14 (2014).
    Jiménez, D. J., Chaves-Moreno, D. & van Elsas, J. D. Unveiling the metabolic potential of two soil-derived microbial consortia selected on wheat straw. Sci. Rep. 5, 13845, doi: 10.1038/srep13845 (2015).
    Wang, C. et al. Metagenomic analysis of microbial consortia enriched from compost: new insights into the role of Actinobacteria in lignocellulose decomposition. Biotechnol. Biofuels 9, 22, doi: 10.1186/s13068-016-0440-2 (2016).
    Gagic, D., Ciric, M., Wen, W. X., Ng, F. & Rakonjac, J. Exploring the secretomes of microbes and microbial communities using filamentous phage display. Front. Microbiol. 7, doi: 10.3389/fmicb.2016.00429 (2016).
    Desvaux, M., Hébraud, M., Talon, R. & Henderson, I. R. Secretion and subcellular localizations of bacterial proteins: a semantic awareness issue. Trends Microbiol. 17, 139–145, doi: 10.1016/j.tim.2009.01.004 (2009).
    Zhou, M., Theunissen, D., Wels, M. & Siezen, R. J. LAB-Secretome: a genome-scale comparative analysis of the predicted extracellular and surface-associated proteins of lactic acid bacteria. BMC Genomics 11, 651, doi: 10.1186/1471-2164-11-651 (2010).
    Lynd, L. R., Weimer, P. J., Zyl, W. Hvan & Pretorius, I. S. Microbial cellulose utilization: fundamentals and biotechnology. Microbiol. Mol. Biol. Rev. 66, 506–577, doi: 10.1128/MMBR.66.3.506-577.2002 (2002).
    Adav, S. S., Ravindran, A. & Sze, S. K. Quantitative proteomic study of Aspergillus fumigatus secretome revealed deamidation of secretory enzymes. J. Proteomics 119, 154–168, doi: 10.1016/j.jprot.2015.02.007 (2015).
    Enany, S. et al. Two dimensional electrophoresis of the exo-proteome produced from community acquired methicillin resistant Staphylococcus aureus belonging to clonal complex 80. Microbiol. Res. 168, 504–511, doi: 10.1016/j.micres.2013.03.004 (2013).
    Brinkworth, A. J. et al. Identification of outer membrane and exoproteins of carbapenem-resistant multilocus sequence type 258 Klebsiella pneumoniae. PloS One 10, e0123219, doi: 10.1371/journal.pone.0123219 (2015).
    Johnson-Rollings, A. S. et al. Exploring the functional soil-microbe interface and exoenzymes through soil metaexoproteomics. ISME J. 8, 2148–2150, doi: 10.1038/ismej.2014.130 (2014).
    Feiz, L., Irshad, M., F Pont-Lezica, R., Canut, H. & Jamet, E. Evaluation of cell wall preparations for proteomics: a new procedure for purifying cell walls from Arabidopsis hypocotyls. Plant Methods 2, 10, doi: 10.1186/1746-4811-2-10 (2006).
    Yoshimura, S. H., Iwasaka, S., Schwarz, W. & Takeyasu, K. Fast degradation of the auxiliary subunit of Na+/K+ -ATPase in the plasma membrane of HeLa cells. J. Cell Sci. 121, 2159–2168, doi: 10.1242/jcs.022905 (2008).
    Niehage, C. et al. The cell surface proteome of human mesenchymal stromal cells. PloS One 6, e20399, doi: 10.1371/journal.pone.0020399 (2011).
    Ventorino, V. et al. Exploring the microbiota dynamics related to vegetable biomasses degradation and study of lignocellulose-degrading bacteria for industrial biotechnological application. Sci. Rep. 5, 8161, doi: 10.1038/srep08161 (2015).
    Christopherson, M. R. et al. The genome sequences of Cellulomonas fimi and ‘ Cellvibrio gilvus’ reveal the cellulolytic strategies of two facultative anaerobes, transfer of ‘ Cellvibrio gilvus’ to the genus Cellulomonas, and proposal of Cellulomonas gilvus sp. nov. PLOS ONE 8, e53954, doi: 10.1371/journal.pone.0053954 (2013).
    Lombard, V., Ramulu, H. G., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495, doi: 10.1093/nar/gkt1178 (2014).
    Evans, V. C. et al. De novo derivation of proteomes from transcriptomes for transcript and protein identification. Nat. Methods 9, 1207–1211, doi: 10.1038/nmeth.2227 (2012).
    Vargas-García, M. C., Suárez-Estrella, F., López, M. J. & Moreno, J. In vitro studies on lignocellulose degradation by microbial strains isolated from composting processes. Int. Biodeterior. Biodegrad. 59, 322–328, doi: 10.1016/j.ibiod.2006.09.008 (2007).
    López-González, J. A. et al. Tracking organic matter and microbiota dynamics during the stages of lignocellulosic waste composting. Bioresour. Technol. 146, 574–584, doi: 10.1016/j.biortech.2013.07.122 (2013).
    Jiménez, D. J. et al. Characterization of three plant biomass-degrading microbial consortia by metagenomics- and metasecretomics-based approaches. Appl. Microbiol. Biotechnol. 24, 10463–10477, doi: 10.1007/s00253-016-7713-3 (2016).
    Dougherty, M. J. et al. Glycoside hydrolases from a targeted compost metagenome, activity-screening and functional characterization. BMC Biotechnol. 12, 38, doi: 10.1186/1472-6750-12-38 (2012).
    Jiménez, D. J., Maruthamuthu, M. & van Elsas, J. D. Metasecretome analysis of a lignocellulolytic microbial consortium grown on wheat straw, xylan and xylose. Biotechnol. Biofuels 8, 199, doi: 10.1186/s13068-015-0387-8 (2015).
    D’haeseleer, P. et al. Proteogenomic analysis of a thermophilic bacterial consortium adapted to deconstruct switchgrass. PloS One 8, e68465, doi: 10.1371/journal.pone.0068465 (2013).
    Levasseur, A., Drula, E., Lombard, V., Coutinho, P. M. & Henrissat, B. Expansion of the enzymatic repertoire of the CAZy database to integrate auxiliary redox enzymes. Biotechnol. Biofuels 6, 41, doi: 10.1186/1754-6834-6-41 (2013).
    Floudas, D. et al. The paleozoic origin of enzymatic lignin decomposition reconstructed from 31 fungal genomes. Science 336, 1715–1719, doi: 10.1126/science.1221748 (2012).
    Geisen, S. et al. Metatranscriptomic census of active protists in soils. ISME J. 9, 2178–2190, doi: 10.1038/ismej.2015.30 (2015).
    Campos, B. M. et al. A novel carbohydrate-binding module from sugar cane soil metagenome featuring unique structural and carbohydrate affinity properties. J. Biol. Chem. 291, 23734–23743, doi: 10.1074/jbc.M116.744383 (2016).
    Hutner, S., Provasoli, L., Schatz, A. & Haskins, C. P. Some approaches to the study of the role of metals in the metabolism of microorganisms. Proc. Am. Phil. Soc. 94, 152–170 (1950).
    Griffiths, R. I., Whiteley, A. S., O’Donnell, A. G. & Bailey, M. J. Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Appl. Environ. Microbiol. 66, 5488–5491, doi: 10.1128/AEM.66.12.5488-5491.2000 (2000).
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–596, doi: 10.1093/nar/gks1219 (2013).
    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359, doi: 10.1038/nmeth.1923 (2012).
    Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652, doi: 10.1038/nbt.1883 (2011).
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522, doi: 10.1073/pnas.1000080107 (2011).
    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336, doi: 10.1038/nmeth.f.303 (2010).
    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinforma. Oxf. Engl. 26, 2460–2461, doi: 10.1093/bioinformatics/btq461 (2010).
    McDonald, D. et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618, doi: 10.1038/ismej.2011.139 (2012).
    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410, doi: 10.1016/S0022-2836(05)80360-2 (1990).
    Rice, P., Longden, I. & Bleasby, A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. TIG 16, 276–277, doi: 10.1016/S0168-9525(00)02024-2 (2000).
    Ishihama, Y. et al. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol. Cell. Proteomics MCP 4, 1265–1272, doi: 10.1074/mcp.M500061-MCP200 (2005).
    Yin, Y. et al. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 40, W445–451, doi: 10.1093/nar/gks479 (2012).
    Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. L. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes1. J. Mol. Biol. 305, 567–580, doi: 10.1006/jmbi.2000.4315 (2001).
    Nielsen, H., Engelbrecht, J., Brunak, S. & Heijne, G. von. Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Eng. 10, 1–6, doi: 10.1093/protein/10.1.1 (1997).

Digital Library of Intellectual Production of Universidade de São Paulo     2012 - 2020