Acta Scientific Microbiology (ISSN: 2581-3226)

Research Article Volume 4 Issue 7

Diversity of Glycoside Hydrolase 10 Family Xylanases Found in Rumen Metagenome and Selection of Sequences with Biotechnological Potential

Gabriella Cavazzini Pavarina1, Natália Sarmanho Monteiro Lima1,2, Claudio Damasceno Pavani1, Eliana Gertrudes de Macedo Lemos1,2, João Martins Pizauro Junior1 and Adriano Marques Gonçalves3,4*

1Technology Department, Sao Paulo State University (Unesp), Faculty of Agricultural and Veterinary Sciences, Brazil
2Molecular Biology Laboratory, Bioenergy Research Institute (IPBEN), Jaboticabal, Sao Paulo, Unesp, Brazil
3Department of Biological and Health Sciences, University of Araraquara (UNIARA), Brazil
4Department of Biochemistry and Organic Chemistry, Chemistry Institute, Sao Paulo State University (UNESP), Brazil

*Corresponding Author: Adriano Marques Gonçalves, Professor, Department of Biological and Health Sciences, University of Araraquara (UNIARA), Araraquara, SP, Brazil.

Received: June 01, 2021 ; Published: June 17, 2021


Metagenomics is an important tool for mining and discovering new enzymes, making it possible to explore the diversity of environments, which could not be explored with conventional methods of cultivating microorganisms. Therefore, this strategy can be used to prospect for xylanases, which degrades lignocellulosic biomass, an important and strategic source of renewable energy of great economic interest. The aim of the work was to identify and investigate the diversity of xylanases of the GH10 family present in the rumen metagenome of Nelore cattle and to prospect molecules with good potential for biotechnological application through in silico analyzes. Pfam was used for the initial selection of GH10 sequences, then the physical and chemical parameters were computed using the ProtParam tool, SignalP-5.0 server was used to predict signal peptides and cleavage location, transmembrane helices prediction was made in TMHMM server, version 2.0 and domain annotation was performed with dbCAN meta server. In addition, identity comparison was performed with NCBI BLAST webtool, sequences were aligned with ClustalW and Neighbor-Joining Tree and pairwise analysis were performed. The metagenomics analysis from Nelore cattle rumen returned 38 sequences with the GH10 domain, CE1, GH43 and CBM6 were also identified in these sequences. Analysis of Neighbor-Joining Tree and proteins identity enabled to differentiate 6 groups with, at least, two proteins with identity higher than 70%. Based on the analysis, 13 sequences were considered unappropriated for biotechnological application for either for being unstable or having transmembrane helices. In this sense, based on the in silico analyzes, 25 of the 38 sequences presented good characteristics for in vitro studies. Thus, in addition to the identification of the 38 sequences with GH10 domain, a workflow of in silico methodologies was suggested to assist the selection of sequences that will guide future in vitro studies.

Keywords: Metagenomics; Xylanase; In silico Studies; Lignocellulose; Protein Biotechnology; Lignocellulose Degrading Enzymes


  1. Madhavan Aravind., et al. “Metagenome Analysis: A Powerful Tool for Enzyme Bioprospecting”. Applied Biochemistry and Biotechnology2 (2017): 636-651.
  2. Handelsman Jo. “Metagenomics: Application of Genomics to Uncultured Microorganisms”. Microbiology and Molecular Biology Reviews: MMBR4 (2004): 669-685.
  3. von Mering C., et al. “Quantitative Phylogenetic Assessment of Microbial Communities in Diverse Environments”. Science (New York, N.Y.)5815 (2007): 1126-1130.
  4. Chistoserdova Ludmila. “Recent Progress and New Challenges in Metagenomics for Biotechnology”. Biotechnology Letters10 (2010): 1351-1359.
  5. Sharma Hem Kanta., et al. “Biological Pretreatment of Lignocellulosic Biomass for Biofuels and Bioproducts: An Overview”. Waste and Biomass Valorization2 (2019): 235-251.
  6. Lazuka Adèle., et al. “Anaerobic Lignocellulolytic Microbial Consortium Derived from Termite Gut: Enrichment, Lignocellulose Degradation and Community Dynamics”. Biotechnology for Biofuels1 (2018): 284.
  7. Moraïs Sarah and Itzhak Mizrahi. “The Road Not Taken: The Rumen Microbiome, Functional Groups, and Community States”. Trends in Microbiology6 (2019): 538-549.
  8. Deusch Simon., et al. “A Structural and Functional Elucidation of the Rumen Microbiome Influenced by Various Diets and Microenvironments”. Frontiers in Microbiology 8 (2017).
  9. Gruninger Robert J., et al. “Contributions of a Unique β-Clamp to Substrate Recognition Illuminates the Molecular Basis of Exolysis in Ferulic Acid Esterases”. The Biochemical Journal7 (2016): 839-849.
  10. Ogunade Ibukun M., et al. “Effects of Live Yeast on Differential Genetic and Functional Attributes of Rumen Microbiota in Beef Cattle”. Journal of Animal Science and Biotechnology1 (2019): 68.
  11. Collins T., et al. “Xylanases, Xylanase Families and Extremophilic Xylanases”. Undefined (2005).
  12. Gao Dahai., et al. “Hemicellulases and Auxiliary Enzymes for Improved Conversion of Lignocellulosic Biomass to Monosaccharides”. Biotechnology for Biofuels 4 (2011): 5.
  13. Beg Q K., et al. “Microbial Xylanases and Their Industrial Applications: A Review”. Applied Microbiology and Biotechnology3-4 (2001): 326-338.
  14. de Freitas Caroline., et al. “Xylooligosaccharides Production Process from Lignocellulosic Biomass and Bioactive Effects”. Bioactive Carbohydrates and Dietary Fibre 18 (2019): 100184.
  15. Venkateswar Rao Linga., et al. “Bioconversion of Lignocellulosic Biomass to Xylitol: An Overview”. Bioresource Technology 213 (2016): 299-310.
  16. Jhamb Kamna and Debendra K Sahoo. “Production of Soluble Recombinant Proteins in Escherichia Coli: Effects of Process Conditions and Chaperone Co-Expression on Cell Growth and Production of Xylanase”. Bioresource Technology 123 (2012): 135-143.
  17. Hannig G and S C Makrides. “Strategies for Optimizing Heterologous Protein Expression in Escherichia Coli”. Trends in Biotechnology2 (1998): 54-60.
  18. Peti Wolfgang and Rebecca Page. “Strategies to Maximize Heterologous Protein Expression in Escherichia Coli with Minimal Cost”. Protein Expression and Purification1 (2007): 1-10.
  19. Leow Thean Chor., et al. “A Thermoalkaliphilic Lipase of Geobacillus Sp. T1”. Extremophiles3 (2007): 527-535.
  20. Chang Catherine Ching Han., et al. “Bioinformatics Approaches for Improved Recombinant Protein Production in Escherichia Coli: Protein Solubility Prediction”. Briefings in Bioinformatics6 (2014): 953-962.
  21. Gaspar Paulo., et al. “EuGene: Maximizing Synthetic Gene Design for Heterologous Expression”. Bioinformatics20 (2012): 2683-2684.
  22. Powell Sean., et al. “EggNOG v4.0: Nested Orthology Inference across 3686 Organisms”. Nucleic Acids Research 42 (2014): D231-239.
  23. Thompson J D., et al. “CLUSTAL W: Improving the Sensitivity of Progressive Multiple Sequence Alignment through Sequence Weighting, Position-Specific Gap Penalties and Weight Matrix Choice”. Nucleic Acids Research22 (1994): 4673-4680.
  24. Finn Robert D., et al. “Pfam: The Protein Families Database”. Nucleic Acids Research 42 (2014): D222-230.
  25. Gasteiger Elisabeth., et al. “Protein Identification and Analysis Tools on the ExPASy Server”. The Proteomics Protocols Handbook, edited by John M. Walker, Humana Press (2005): 571-607.
  26. Almagro Armenteros José Juan., et al. “SignalP 5.0 Improves Signal Peptide Predictions Using Deep Neural Networks”. Nature Biotechnology4 (2019): 420-423.
  27. Krogh A., et al. “Predicting Transmembrane Protein Topology with a Hidden Markov Model: Application to Complete Genomes”. Journal of Molecular Biology3 (2001): 567-580.
  28. Huang Le., et al. “DbCAN-Seq: A Database of Carbohydrate-Active Enzyme (CAZyme) Sequence and Annotation”. Nucleic Acids ResearchD1 (2018): D516-521.
  29. Altschul S F., et al. “Basic Local Alignment Search Tool”. Journal of Molecular Biology3 (1990): 403-410.
  30. Larkin M A., et al. “Clustal W and Clustal X Version 2.0”. Bioinformatics (Oxford, England)21 (2007): 2947-2948.
  31. Kumar Sudhir., et al. “MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms”. Molecular Biology and Evolution6 (2018): 1547-1549.
  32. Saitou N and M Nei. “The Neighbor-Joining Method: A New Method for Reconstructing Phylogenetic Trees”. Molecular Biology and Evolution4 (1987): 406-425.
  33. Felsenstein Joseph. “CONFIDENCE LIMITS ON PHYLOGENIES: AN APPROACH USING THE BOOTSTRAP”. International Journal of Organic Evolution4 (1985): 783-791.
  34. Masatoshi Nei and Sudhir Kumar. Molecular Evolution and Phylogenetics. Oxford University Press (2000).
  35. Letunic Ivica and Peer Bork. “Interactive Tree Of Life (ITOL) v5: An Online Tool for Phylogenetic Tree Display and Annotation”. Nucleic Acids Research (2021).
  36. Kmezik Cathleen., et al. “A Polysaccharide Utilization Locus from the Gut Bacterium Dysgonomonas Mossii Encodes Functionally Distinct Carbohydrate Esterases”. Journal of Biological Chemistry 296 (2021): 100500.
  37. Qiao Weibo., et al. “Biochemical Characterization of a Novel Thermostable GH11 Xylanase with CBM6 Domain from Caldicellulosiruptor Kronotskyensis”. Journal of Molecular Catalysis B: Enzymatic 107 (2014): 8-16.
  38. Mewis Keith., et al. “Dividing the Large Glycoside Hydrolase Family 43 into Subfamilies: A Motivation for Detailed Enzyme Characterization”. Applied and Environmental Microbiology6 (2016): 1686-1692.
  39. Glasgow Evan., et al. “Multifunctional Cellulases Are Potent, Versatile Tools for a Renewable Bioeconomy”. Current Opinion in Biotechnology 67 (2021): 141-148.
  40. Möller Steffen., et al. “Evaluation of Methods for the Prediction of Membrane Spanning Regions”. Bioinformatics7 (2001): 646-653.
  41. Pavarina Gabriella Cavazzini., et al. “Characterization of a New Bifunctional Endo-1,4-β-Xylanase/Esterase Found in the Rumen Metagenome”. Scientific Reports1 (2021): 10440.


Citation: Adriano Marques Gonçalves., et al. “Diversity of Glycoside Hydrolase 10 Family Xylanases Found in Rumen Metagenome and Selection of Sequences with Biotechnological Potential”. Acta Scientific Microbiology 4.7 (2021): 76-89.


Copyright: © 2021 Adriano Marques Gonçalves., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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