A PUTATIVE β-GLUCOSIDASE AND AN ENDO-1,4-β-GLUCANASE FROM POME METAGENOMIC DNA
DOI:
https://doi.org/10.71336/jabs.918Keywords:
metagenomics; palm oil mill effluent (POME); high-throughput screening; next-generation sequencing; glucosidase; glucanaseAbstract
Functional metagenomic approach with high-throughput screening can be used to identify tapped and untapped biocatalysts. Metagenomic DNA libraries of 4.49 Gbase were constructed from microbes in Malaysian palm oil mill effluent (POME). After culture experiment based on natural selection metagenomic DNA was extracted and cloned to pCC1FOS vector and transformed into EPI300T1R. Cellulose-degrading enzyme activity was screened with microtiter assay using methylumbelliferyl-β-D-glucopyranoside (MUGlc) and methylumbelliferyl-β-D-cellobioside (MUC) as fluorogenic substrates. Reads were normalized using robust z-score and 100 highly rated clones were selected. Fosmids of these clones were isolated and sequenced with Hiseq strategy. Using Solexa, Velvet, SSPACE, Prodigal and Blastp, genes IDs of 96 putative cellulose-degrading enzymes were identified. Two putative metagenomic cellulose-degrading enzymes, MCDE1 with β-glucosidase activity and MCDE3 with endo-1,4-β-glucanase activity were produced, purified, and partially biochemically characterized.
References
Kuhad, R. C., Gupta, R., & Singh, A. (2011): Microbial Cellulases and Their Industrial Applications. Enzyme Research, pp. 1–10. DOI: https://doi.org/10.4061/2011/280696
Ghimire, A., Kumar, G., Sivagurunathan, P., Shobana, S., Saratale, G. D., Kim, H. W., … Munoz, R. (2017): Bio-hythane production from microalgae biomass: Key challenges and potential opportunities for algal bio-refineries. Bioresource Technology. 241: 525–536. DOI: https://doi.org/10.1016/j.biortech.2017.05.156
Armstrong, Z., Liu, F., Kheirandish, S., Chen, H.-M., Mewis, K., Duo, T., Morgan-Lang, C., Hallam, S.J., & Withers, S. G. (2019): High-Throughput Recovery and Characterization of Metagenome-Derived Glycoside Hydrolase-Containing Clones as a Resource for Biocatalyst Development. MSystems. 4(4). https://doi.org/10.1128/mSystems.00082-19. DOI: https://doi.org/10.1128/mSystems.00082-19
Amann, R. I., Ludwig, W., & Schleifer, K. H. (1995): Phylogenetic identification and in situ detection of individual microbial cells without cultivation. Phylogenetic Identification and In Situ Detection of Individual Microbial Cells without Cultivation. Microbiological Reviews. 59(1): 143–169. DOI: https://doi.org/10.1128/mr.59.1.143-169.1995
Kumar, A. K. (2015): UV mutagenesis treatment for improved production of endoglucanase and β-glucosidase from newly isolated thermotolerant actinomycetes, Streptomyces griseoaurantiacus. Bioresources and Bioprocessing. 2(1): 22. https://doi.org/10.1186/s40643-015-0052-x. DOI: https://doi.org/10.1186/s40643-015-0052-x
Riesenfeld, C. S., Schloss, P. D., & Handelsman, J. (2004): Metagenomics: genomic analysis of microbial communities. Annual Review of Genetics. 38: 525–552. DOI: https://doi.org/10.1146/annurev.genet.38.072902.091216
Martin, M., Biver, S., Steels, S., Barbeyron, T., Jam, M., Portetelle, D., van Michel, G., & Vandenbol, M. (2014): Identification and Characterization of a Halotolerant, Cold-Active Marine Endo-β-1,4-Glucanase by Using Functional Metagenomics of Seaweed-Associated Microbiota. Applied and Environmental Microbiology. 80(16): 4958–4967. https://doi.org/10.1128/AEM.01194-14. DOI: https://doi.org/10.1128/AEM.01194-14
Vester, J., Glaring, M., & Stougaard, P. (2014): Discovery of novel enzymes with industrial potential from a cold and alkaline environment by a combination of functional metagenomics and culturing. Microbial Cell Factories. 13(1): 72. doi: 10.1186/1475-2859-13-72. DOI: https://doi.org/10.1186/1475-2859-13-72
Lewin, A., Zhou, J., Pham, V. T. T., Haugen, T., Zeiny, M. El, Aarstad, O., Leibl, W., Wenzel, A., & Liles, M. R. (2017): Novel archaeal thermostable cellulases from an oil reservoir metagenome. AMB Express. 7(1): 183. doi: 10.1186/s13568-017-0485-z. DOI: https://doi.org/10.1186/s13568-017-0485-z
Wierzbicka-Woś, A., Henneberger, R., Batista-García, R. A., Martínez-Ávila, L., Jackson, S. A., Kennedy, J., & Dobson, A. D. W. (2019): Biochemical Characterization of a Novel Monospecific Endo-β-1,4-Glucanase Belonging to GH Family 5 From a Rhizosphere Metagenomic Library. Frontiers in Microbiology. 10. https://doi.org/10.3389/fmicb.2019.01342. DOI: https://doi.org/10.3389/fmicb.2019.01342
Santana-Pereira, A. L. R., Sandoval-Powers, M., Monsma, S., Zhou, J., Santos, S. R., Mead, D. A., & Liles, M. R. (2020): Discovery of Novel Biosynthetic Gene Cluster Diversity from a Soil Metagenomic Library. Frontiers in Microbiology. 11. https://doi.org/10.3389/fmicb.2020.585398. DOI: https://doi.org/10.3389/fmicb.2020.585398
Birmingham, A., Selfors, L. M., Forster, T., Wrobel, D., Kennedy, C. J., Shanks, E., Santoyo-Lopez, J., Dunican, D.J., Long, A., Kelleher, D., Beijersbergen, R.L., Ghazal, P., & Shamu, C. E. (2009). Statistical methods for analysis of high-throughput RNA interference screens. Nat Methods, 6(8): 569–575. DOI: https://doi.org/10.1038/nmeth.1351
Rees, H. C., Grant, S., Jones, B., Grant, W. D., & Heaphy, S. (2003): Detecting cellulase and esterase enzyme activities encoded by novel genes present in environmental DNA libraries. Extremophiles. 7(5): 415–421. DOI: https://doi.org/10.1007/s00792-003-0339-2
Voget, S., Leggewie, C., Uesbeck, A., Raasch, C., Jaeger, K. E., & Streit, W. R. (2003): Prospecting for Novel Biocatalysts in a Soil Metagenome. Applied and Environmental Microbiology. 69(10): 6235–6242. DOI: https://doi.org/10.1128/AEM.69.10.6235-6242.2003
Lee, S., & Hallam, S. J. (2009): Extraction of high molecular weight genomic DNA from soils and sediments. Journal of Visualized Experiments: JoVE. (33): 2–5. DOI: https://doi.org/10.3791/1569-v
Asli, N., Sergio, C., & Chen, T. (2013): Data Analysis Approaches in High Throughput Screening. In Drug Discovery. InTech. doi: 10.5772/52508. DOI: https://doi.org/10.5772/52508
Benbelgacem, F. F., Mat Isa, Mohd Noor Abdelrahim, M. A. M., Tumian, Afidalina, Abdelkader Bellag, O., Parman, Adibah Noorbatcha, I. A., & Mohd Salleh, H. (2018): Next Generation Sequencing-Data Analysis for Cellulose- and Xylan-Degrading Enzymes from POME Metagenome. Sains Malaysiana, 47(12): 2951–2960. DOI: https://doi.org/10.17576/jsm-2018-4712-03
http://dx.doi.org/10.17576/jsm-2018-4712-03 Zerbino, D. R., & Birney, E. (2008): Velvet: Algorithms for de novo short read assembly using de Bruijn graphs. Genome Research. 18(5): 821–829. DOI: https://doi.org/10.1101/gr.074492.107
Boetzer, M., Henkel, C. V., Jansen, H. J., Butler, D., & Pirovano, W. (2011): Scaffolding pre-assembled contigs using SSPACE. Bioinformatics, 27(4): 578-579. doi: 10.1093/bioinformatics/btq683. DOI: https://doi.org/10.1093/bioinformatics/btq683
Hyatt, D., Chen, G. L., LoCascio, P. F., Land, M. L., Larimer, F. W., & Hauser, L. J. (2010): Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 11(1): 119. https://doi.org/10.1186/1471-2105-11-119. DOI: https://doi.org/10.1186/1471-2105-11-119
Laemmli, U. K. (1970): Cleavage of Structural Proteins during the Assembly of the Head of Bacteriophage T4. Nature. 227(5259): 680–685. DOI: https://doi.org/10.1038/227680a0
Walker, J. M. (2002): Protein Protocols Handbook, The. New Jersey: Humana Press. DOI: https://doi.org/10.1385/1592591698
Yang, J., & Zhang, Y. (2015): Protein Structure and Function Prediction Using I-TASSER. Current Protocols in Bioinformatics. 52(1): 5.8.1-5.8.15. doi: 10.1002/0471250953.bi0508s52. DOI: https://doi.org/10.1002/0471250953.bi0508s52
Roy, A., Kucukural, A., & Zhang, Y. (2010): I-TASSER: A unified platform for automated protein structure and function prediction. Nature Protocols. 5(4): 725–738. DOI: https://doi.org/10.1038/nprot.2010.5
Y Zhang. (2008): I-TASSER server for protein 3D structure prediction. BMC Bioinformatics, 9: 40. https://doi.org/10.1186/1471-2105-9-40. DOI: https://doi.org/10.1186/1471-2105-9-40
de Castro, E., Sigrist, C. J. A., Gattiker, A., Bulliard, V., Langendijk-Genevaux, P. S., Gasteiger, E., Bairoch, A., & Hulo, N. (2006): ScanProsite: Detection of PROSITE signature matches and ProRule-associated functional and structural residues in proteins. Nucleic Acids Research. 34(Web Server), W362–W365. doi: 10.1093/nar/gkl124. DOI: https://doi.org/10.1093/nar/gkl124
Benbelgacem, F.F., Bellag, O.A., Ahmad, A. A., Noorbatcha, I.A., & Mohd. Salleh, H. (2017): Palm Oil Mill Effluent Metagenome for Cellulose-Degrading Enzymes. Journal of Basic and Applied Research. 3(3): 101–106.
Uchiyama, T., & Kentaro Miyazaki. (2009): Functional metagenomics for enzyme discovery: challenges to efficient screening. Current Opinion in Biotechnology. 20: 616–622. DOI: https://doi.org/10.1016/j.copbio.2009.09.010
Madhavan, A., Sindhu, R., Parameswaran, B., Sukumaran, R. K., & Pandey, A. (2017): Metagenome Analysis: a Powerful Tool for Enzyme Bioprospecting. Applied Biochemistry and Biotechnology. 183(2): 636–651. DOI: https://doi.org/10.1007/s12010-017-2568-3
Parks, R., & Graham, F. (1997): A helper-dependent system for adenovirus vector production helps define a lower limit for efficient DNA packaging. Journal of Virology. 71: 3293–3298. DOI: https://doi.org/10.1128/jvi.71.4.3293-3298.1997
Zhou, J., Bruns, M. A., & Tiedje, J. M. (1996): DNA recovery from soils of diverse composition. Applied and Environmental Microbiology. 62(2): 316–322. DOI: https://doi.org/10.1128/aem.62.2.316-322.1996
Wexler, M., Bond, P. L., Richardson, D. J., & Johnston, A. W. B. (2005): A wide host-range metagenomic library from a waste water treatment plant yields a novel alcohol/aldehyde dehydrogenase. Environmental Microbiology. 7(12): 1917–1926. DOI: https://doi.org/10.1111/j.1462-2920.2005.00854.x
Kakirde, K. S., Parsley, L. C., & Liles, M. R. (2010): Size does matter: Application-driven approaches for soil metagenomics. Soil Biology and Biochemistry, 42(11): 1911-1923. doi: 10.1016/j.soilbio.2010.07.021 DOI: https://doi.org/10.1016/j.soilbio.2010.07.021
Craig, J. W., Chang, F.-Y., Kim, J. H., Obiajulu, S. C., & Brady, S. F. (2010): Expanding Small-Molecule Functional Metagenomics through Parallel Screening of Broad-Host-Range Cosmid Environmental DNA Libraries in Diverse Proteobacteria. Applied and Environmental Microbiology. 76(5): 1633–1641. DOI: https://doi.org/10.1128/AEM.02169-09
Troeschel, S. C., Drepper, T., Leggewie, C., Streit, W. R., & Jaeger, K.-E. (2010): Novel Tools for the Functional Expression of Metagenomic DANN, pp. 117–139. DOI: https://doi.org/10.1007/978-1-60761-823-2_8
Cheng, J., Pinnell, L., Engel, K., Neufeld, J. D., & Charles, T. C. (2014): Versatile broad-host-range cosmids for construction of high quality metagenomic libraries. Journal of Microbiological Methods. 99(1): 27–34. DOI: https://doi.org/10.1016/j.mimet.2014.01.015
Lam, K. N., Cheng, J., Engel, K., Neufeld, J. D., & Charles, T. C. (2015): Current and future resources for functional metagenomics. Frontiers in Microbiology, 6. https://doi.org/10.3389/fmicb.2015.01196. DOI: https://doi.org/10.3389/fmicb.2015.01196
Taupp, M., Mewis, K., & Hallam, S. J. (2011): The art and design of functional metagenomic screens. Current Opinion in Biotechnology. 22(3) 465–472. DOI: https://doi.org/10.1016/j.copbio.2011.02.010
Cano-Ramírez, C., Santiago-Hernández, A., Rivera-Orduña, F. N., García-Huante, Y., Zúñiga, G., & Hidalgo-Lara, M. E. (2016): Expression, purification and characterization of an endoglucanase from Serratia proteamaculans CDBB-1961, isolated from the gut of Dendroctonus adjunctus (Coleoptera: Scolytinae). AMB Express, 6(1): 63. doi: 10.1186/s13568-016-0233-9 DOI: https://doi.org/10.1186/s13568-016-0233-9
Cregg, J. M., Vedvick, T. S., & Raschke, W. C. (1993): Recent advances in the expression of foreign genes in Pichia pastoris. Nature Biotechnology. 11(8): 905–910. DOI: https://doi.org/10.1038/nbt0893-905
Woon, J. S., Jie, P., King, H., Mohamed, M., Mahadi, N. M., Mohd, W., & Wan, K. (2017): Cloning, Production and Characterization of a Glycoside Hydrolase Family 7 Enzyme from the Gut Microbiota of the Termite Coptotermes curvignathus. Molecular Biotechnology. 59(7): 271–283. DOI: https://doi.org/10.1007/s12033-017-0015-x
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Journal of Applied Biological Sciences
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
